Journal articles on the topic 'Autonomous Transformable Marine Robot'
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Woolley, Robert, Jon Timmis, and Andy M. Tyrrell. "Cylindabot: Transformable Wheg Robot Traversing Stepped and Sloped Environments." Robotics 10, no. 3 (August 30, 2021): 104. http://dx.doi.org/10.3390/robotics10030104.
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The ability of an autonomous robot to adapt to different terrain affords the flexibility to move successfully in a range of environments. This paper proposes the Cylindabot, a transformable Wheg robot that can move with two large wheels, each of which can rotate out, producing three legs. This ability to change its mode of locomotion allows for specialised performance. The Cylindabot has been tested in simulation and on a physical robot on steps and slopes as an indication of its efficacy in different environments. These experiments show that such robots are capable of climbing up to a 32 degree slope and a step 1.43 times their initial height. Theoretical limits are devised that match the results, and a comparison with existing Wheg platforms is made.
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Kim, Hyun-Sik, Hyung-Joo Kang, Youn-Jae Ham, and Seung-Soo Park. "Development of Underwater-type Autonomous Marine Robot-kit." Journal of Korean Institute of Intelligent Systems 22, no. 3 (June 25, 2012): 312–18. http://dx.doi.org/10.5391/jkiis.2012.22.3.312.
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Jeong, Jinseok, Youngmin Sa, and Hyun-Sik Kim. "Development of Autonomous Surface Robot for Marine Fire Safety." Journal of Ocean Engineering and Technology 32, no. 2 (April 30, 2018): 138–42. http://dx.doi.org/10.26748/ksoe.2018.4.32.2.138.
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Gurenko, Boris, Roman Fedorenko, Maksim Beresnev, and Roman Saprykin. "Development of Simulator for Intelligent Autonomous Underwater Vehicle." Applied Mechanics and Materials 799-800 (October 2015): 1001–5. http://dx.doi.org/10.4028/www.scientific.net/amm.799-800.1001.
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Testing and debugging of real equipment is a time consuming task. In particular, in the case of marine robots, it is necessary each time to carry out the transportation and deployment of a robot on the water. Experiments with not yet fully functional prototype of marine robot equipped with expensive hardware is in the meantime very risky. Therefore, the use of simulators is affordable way to accelerate the development of robotic systems from the viewpoint of labor effort and cost of experiments. This paper presents a simulator specifically designed for autonomous unmanned underwater vehicles.
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Anto, Adhy Febry, and Totok Sukardiyono. "Prototype Autonomous Rover Pembersih Sampah Pantai menggunakan ArduPilot." Elinvo (Electronics, Informatics, and Vocational Education) 4, no. 2 (December 13, 2019): 202–9. http://dx.doi.org/10.21831/elinvo.v4i2.28793.
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Indonesia has the second longest coastline in the world. On the other hand, Indonesia is the second largest contributor to marine waste in the world. Coastal cleanliness needs to be maintained so that it becomes an attraction for tourism and to protect the marine ecosystem. This article describes the results of testing devices that can be used to clean beaches. Research carried out by the development method. An autonomous beach garbage cleaning rover is a beach trash sweeper robot equipped with GPS, compass, telemetry, ArduPilot as a navigation and communication system when the robot operates. This robot moves using 2 DC motors with torque of ± 4 kg. The robot is also equipped with a conveyor that functions to sweep plastic debris on the beach. The test results show that the robot can go according to the specified path, according to the coordinates entered (100% accuracy) and be able to transport plastic waste. This is because the motor used has a small torque, so it is not able to lift large objects. The next robot development can be done in terms of: identifying the capacity of robots to accommodate various types of waste, testing the ability of robots to clean waste, the use of solar cells, and trajectory management systems.
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Bonin-Font, Francisco, and Antoni Burguera. "Towards Multi-Robot Visual Graph-SLAM for Autonomous Marine Vehicles." Journal of Marine Science and Engineering 8, no. 6 (June 14, 2020): 437. http://dx.doi.org/10.3390/jmse8060437.
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State of the art approaches to Multi-robot localization and mapping still present multiple issues to be improved, offering a wide range of possibilities for researchers and technology. This paper presents a new algorithm for visual Multi-robot simultaneous localization and mapping, used to join, in a common reference system, several trajectories of different robots that participate simultaneously in a common mission. One of the main problems in centralized configurations, where the leader can receive multiple data from the rest of robots, is the limited communications bandwidth that delays the data transmission and can be overloaded quickly, restricting the reactive actions. This paper presents a new approach to Multi-robot visual graph Simultaneous Localization and Mapping (SLAM) that aims to perform a joined topological map, which evolves in different directions according to the different trajectories of the different robots. The main contributions of this new strategy are centered on: (a) reducing to hashes of small dimensions the visual data to be exchanged among all agents, diminishing, in consequence, the data delivery time, (b) running two different phases of SLAM, intra- and inter-session, with their respective loop-closing tasks, with a trajectory joining action in between, with high flexibility in their combination, (c) simplifying the complete SLAM process, in concept and implementation, and addressing it to correct the trajectory of several robots, initially and continuously estimated by means of a visual odometer, and (d) executing the process online, in order to assure a successful accomplishment of the mission, with the planned trajectories and at the planned points. Primary results included in this paper show a promising performance of the algorithm in visual datasets obtained in different points on the coast of the Balearic Islands, either by divers or by an Autonomous Underwater Vehicle (AUV) equipped with cameras.
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Molina-Molina, J. Carlos, Marouane Salhaoui, Antonio Guerrero-González, and Mounir Arioua. "Autonomous Marine Robot Based on AI Recognition for Permanent Surveillance in Marine Protected Areas." Sensors 21, no. 8 (April 10, 2021): 2664. http://dx.doi.org/10.3390/s21082664.
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The world’s oceans are one of the most valuable sources of biodiversity and resources on the planet, although there are areas where the marine ecosystem is threatened by human activities. Marine protected areas (MPAs) are distinctive spaces protected by law due to their unique characteristics, such as being the habitat of endangered marine species. Even with this protection, there are still illegal activities such as poaching or anchoring that threaten the survival of different marine species. In this context, we propose an autonomous surface vehicle (ASV) model system for the surveillance of marine areas by detecting and recognizing vessels through artificial intelligence (AI)-based image recognition services, in search of those carrying out illegal activities. Cloud and edge AI computing technologies were used for computer vision. These technologies have proven to be accurate and reliable in detecting shapes and objects for which they have been trained. Azure edge and cloud vision services offer the best option in terms of accuracy for this task. Due to the lack of 4G and 5G coverage in offshore marine environments, it is necessary to use radio links with a coastal base station to ensure communications, which may result in a high response time due to the high latency involved. The analysis of on-board images may not be sufficiently accurate; therefore, we proposed a smart algorithm for autonomy optimization by selecting the proper AI technology according to the current scenario (SAAO) capable of selecting the best AI source for the current scenario in real time, according to the required recognition accuracy or low latency. The SAAO optimizes the execution, efficiency, risk reduction, and results of each stage of the surveillance mission, taking appropriate decisions by selecting either cloud or edge vision models without human intervention.
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Mellinger, David K., Holger Klinck, Neil M. Bogue, Jim Luby, Haru Matsumoto, and Roland Stelzer. "Gliders, floats, and robot sailboats: autonomous platforms for marine mammal research." Journal of the Acoustical Society of America 131, no. 4 (April 2012): 3493. http://dx.doi.org/10.1121/1.4709197.
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Pan, Lisheng. "Exploration and Mining Learning Robot of Autonomous Marine Resources Based on Adaptive Neural Network Controller." Polish Maritime Research 25, s3 (December 1, 2018): 78–83. http://dx.doi.org/10.2478/pomr-2018-0115.
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Abstract To study the autonomous learning model of the learning robot for marine resource exploration, an adaptive neural network controller was applied. The motion characteristics of autonomous learning robots were identified. The mathematical model of the multilayer forward neural network and its improved learning algorithm were studied. The improved Elman regression neural network and the composite input dynamic regression neural network were further discussed. At the same time, the diagonal neural network was analysed from the structure and learning algorithms. The results showed that for the complex environment of the ocean, the structure of the composite input dynamic regression network was simple, and the convergence was fast. In summary, the identification method of underwater robot system based on neural network is effective.
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Ahmed, Mohammed, Markus Eich, and Felix Bernhard. "Design and Control of MIRA: A Lightweight Climbing Robot for Ship Inspection." International Letters of Chemistry, Physics and Astronomy 55 (July 2015): 128–35. http://dx.doi.org/10.18052/www.scipress.com/ilcpa.55.128.
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The inspection of marine vessels is currently per-formed manually. Inspectors use tools (e.g. cameras and devices for non-destructive testing) to detect damaged areas, cracks, and corrosion in large cargo holds, tanks, and other parts of a ship. Due to the size and complex geometry of most ships, ship inspection is time-consuming and expensive. The EU-funded project INCASS develops concepts for a marine inspection robotic assistant system to improve and automate ship inspections. In this paper, we introduce our magnetic wall–climbing robot: Marine Inspection Robotic Assistant (MIRA). This semi-autonomous lightweight system is able to climb a vessels steel frame to deliver on-line visual inspection data. In addition, we describe the design of the robot and its building subsystems as well as its hardware and software components.
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Ahmed, Mohammed, Markus Eich, and Felix Bernhard. "Design and Control of MIRA: A Lightweight Climbing Robot for Ship Inspection." International Letters of Chemistry, Physics and Astronomy 55 (July 3, 2015): 128–35. http://dx.doi.org/10.56431/p-326xa8.
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The inspection of marine vessels is currently per-formed manually. Inspectors use tools (e.g. cameras and devices for non-destructive testing) to detect damaged areas, cracks, and corrosion in large cargo holds, tanks, and other parts of a ship. Due to the size and complex geometry of most ships, ship inspection is time-consuming and expensive. The EU-funded project INCASS develops concepts for a marine inspection robotic assistant system to improve and automate ship inspections. In this paper, we introduce our magnetic wall–climbing robot: Marine Inspection Robotic Assistant (MIRA). This semi-autonomous lightweight system is able to climb a vessels steel frame to deliver on-line visual inspection data. In addition, we describe the design of the robot and its building subsystems as well as its hardware and software components.
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Cho, Sungjin, Fumin Zhang, and Catherine R. Edwards. "Learning and detecting abnormal speed of marine robots." International Journal of Advanced Robotic Systems 18, no. 2 (March 1, 2021): 172988142199926. http://dx.doi.org/10.1177/1729881421999268.
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This article presents anomaly detection algorithms for marine robots based on their trajectories under the influence of unknown ocean flow. A learning algorithm identifies the flow field and estimates the through-water speed of a marine robot. By comparing the through-water speed with a nominal speed range, the algorithm is able to detect anomalies causing unusual speed changes. The identified ocean flow field is used to eliminate false alarms, where an abnormal trajectory may be caused by unexpected flow. The convergence of the algorithms is justified through the theory of adaptive control. The proposed strategy is robust to speed constraints and inaccurate flow modeling. Experimental results are collected on an indoor testbed formed by the Georgia Tech Miniature Autonomous Blimp and Georgia Tech Wind Measuring Robot, while simulation study is performed for ocean flow field. Data collected in both studies confirm the effectiveness of the algorithms in identifying the through-water speed and the detection of speed anomalies while avoiding false alarms.
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Wu, Que, and M. Yu Rachkov. "Calculation and Optimization of the Wheel-Track Mobile Robot Reconfi guration Mechanism." Mekhatronika, Avtomatizatsiya, Upravlenie 23, no. 4 (April 8, 2022): 209–15. http://dx.doi.org/10.17587/mau.23.209-215.
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The actual problem of an autonomous transport robot design for work in an unstructured environment and in emergency situations is considered. The design of the robot contains a combined system, consisting of transformable track and wheel groups, which allows moving over different types of surfaces, including the conditions of the urban environment, in particular flights of stairs. Movement on a flat surface is carried out only by wheel groups with raised track groups, which provides an increased speed of the robot. Movement on uneven surface is carried out only by track groups, while the wheel groups are raised. It provides increased cross-country ability. Staircase and complex obstacles can be overcome with the simultaneous use of wheel and track groups at angle of the wheel groups relative to the track groups during reconfiguration of geometric shapes and steps of the staircase and obstacles. The robot reconfiguration unit is made in the form of a lever mechanism with electric cylinders, which can be self-locking. It implements the switching of the robot movement modes, as well as lifting the wheel group to the required angle for overcoming obstacles. The analysis of the design and calculation of the lever mechanism of the wheel-track robot is carried out. A kinematic model of the reconfiguration unit has been developed. Relationships between the angles and length of the levers, as well as between the angular velocity of movement of the levers and the speed of movement of the electric cylinder pusher of the lever mechanism are obtained. Optimization of the reconfiguration block operation by creating its mathematical model for programming in the Matlab package is done. The target function and restrictions on the system operation have been determined. As a result, improved mechanical characteristics of the reconstruction unit are obtained.
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González-Reolid, I., J. Molina-Molina, A. Guerrero-González, F. Ortiz, and D. Alonso. "An Autonomous Solar-Powered Marine Robotic Observatory for Permanent Monitoring of Large Areas of Shallow Water." Sensors 18, no. 10 (October 17, 2018): 3497. http://dx.doi.org/10.3390/s18103497.
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Apart from their ecological value, the world’s oceans are among the planet’s most valuable resources, a rich source of food and wealth and in urgent need of protection. This article describes BUSCAMOS-RobObs, a robot-based observatory, consisting of an autonomous solar-powered marine robot with specialized sensing systems designed to carry out long-term observation missions in the inland sea of the Mar Menor in southeastern Spain. This highly specialised device is unique because it has the capacity to anchor itself to the seabed and become a “buoy”, either to take measurements at specific points or to recharge its batteries. It thus avoids drifting and possible accidents in the buoy mode, especially near the coast, and resumes monitoring tasks when the required energy levels are reached. The robot is equipped with a broad range of sensors, including side scan sonar, sub-bottom sonar, laser systems, ultrasound sonar, depth meters, a multi-parametric probe and a GPS, which can collect georeferenced oceanic data. Although various types of autonomous vehicles have been described in the literature, they all have limited autonomy (even in the long term) as regards operational time and covering the seabed. The article describes a permanent monitoring mission in the Mar Menor, with a combination of solar energy and a decision-making strategy as regards the optimum route to be followed. The energy and mission simulation results, as well as an account of actual monitoring missions are also included.
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Martorell-Torres, Antoni, Eric Guerrero-Font, José Guerrero-Sastre, and Gabriel Oliver-Codina. "Xiroi II, an Evolved ASV Platform for Marine Multirobot Operations." Sensors 23, no. 1 (December 22, 2022): 109. http://dx.doi.org/10.3390/s23010109.
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In this paper, we present the design, development and a practical use of an Autonomous Surface Vehicle (ASV) as a modular and flexible platform for a large variety of marine tasks including the coordination strategies with other marine robots. This work tackles the integration of an open-source Robot-Operating-System (ROS)-based control architecture that provides the ASV with a wide variety of navigation behaviors. These new ASV capabilities can be used to acquire useful data from the environment to survey, map, and characterize marine habitats. In addition, the ASV is used as a radio frequency relay point between an Autonomous Underwater Vehicle (AUV) and the ground station as well as to enhance the Acoustic Communication Link (ACL) with the AUV. In order to improve the quality of the ACL, a new Marine Multirobot System (MMRS) coordination strategy has been developed that aims to keep both vehicles close to each other. The entire system has been successfully designed, implemented, and tested in real marine environment robotic tasks. The experimental tests show satisfactory results both in ROS-based navigation architecture and the MMRS coordination strategy resulting in a significant improvement of the quality of the ACL.
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Hong, Le, Weicheng Cui, and Hao Chen. "A Novel Multi-Robot Task Allocation Model in Marine Plastics Cleaning Based on Replicator Dynamics." Journal of Marine Science and Engineering 9, no. 8 (August 14, 2021): 879. http://dx.doi.org/10.3390/jmse9080879.
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As marine plastic pollution threatens the marine ecosystem seriously, the government needs to find an effective way to clean marine plastics. Due to the advantages of easy operation and high efficiency, autonomous underwater vehicles (AUVs) have been applied to clean marine plastics. As for the large-scale marine environment, the marine plastic cleaning task needs to be accomplished through the collaborative work of multiple AUVs. Assigning the cleaning task to each AUV reasonably and effectively has an essential impact on improving cleaning efficiency. The coordination of AUVs is subjected to harsh communication conditions. Therefore, to release the dependence on the underwater communications among AUVs, proposing a reliable multi-robot task allocation (MRTA) model is necessary. Inspired by the evolutionary game theory, this paper proposes a novel multi-robot task allocation (MRTA) model based on replicator dynamics for marine plastic cleaning. This novel model not only satisfies the minimization of the cost function, but also reaches a relatively stable state of the task allocation. A novel optimization algorithm, equilibrium optimizer (EO), is adopted as the optimizer. The simulation results validate the correctness of the results achieved by EO and the applicability of the proposed model. At last, several valuable conclusions are obtained from the simulations on the three different assumed AUVs.
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Yoerger, Dana R., Annette F. Govindarajan, Jonathan C. Howland, Joel K. Llopiz, Peter H. Wiebe, Molly Curran, Justin Fujii, et al. "A hybrid underwater robot for multidisciplinary investigation of the ocean twilight zone." Science Robotics 6, no. 55 (June 16, 2021): eabe1901. http://dx.doi.org/10.1126/scirobotics.abe1901.
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Mesobot, an autonomous underwater vehicle, addresses specific unmet needs for observing and sampling a variety of phenomena in the ocean’s midwaters. The midwater hosts a vast biomass, has a role in regulating climate, and may soon be exploited commercially, yet our scientific understanding of it is incomplete. Mesobot has the ability to survey and track slow-moving animals and to correlate the animals’ movements with critical environmental measurements. Mesobot will complement existing oceanographic assets such as towed, remotely operated, and autonomous vehicles; shipboard acoustic sensors; and net tows. Its potential to perform behavioral studies unobtrusively over long periods with substantial autonomy provides a capability that is not presently available to midwater researchers. The 250-kilogram marine robot can be teleoperated through a lightweight fiber optic tether and can also operate untethered with full autonomy while minimizing environmental disturbance. We present recent results illustrating the vehicle’s ability to automatically track free-swimming hydromedusae (Solmissus sp.) and larvaceans (Bathochordaeus stygius) at depths of 200 meters in Monterey Bay, USA. In addition to these tracking missions, the vehicle can execute preprogrammed missions collecting image and sensor data while also carrying substantial auxiliary payloads such as cameras, sonars, and samplers.
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Zhang, Yongji, Yu Jiang, Hong Qi, Minghao Zhao, Yuehang Wang, Kai Wang, and Fenglin Wei. "An Underwater Human–Robot Interaction Using a Visual–Textual Model for Autonomous Underwater Vehicles." Sensors 23, no. 1 (December 24, 2022): 197. http://dx.doi.org/10.3390/s23010197.
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The marine environment presents a unique set of challenges for human–robot interaction. Communicating with gestures is a common way for interacting between the diver and autonomous underwater vehicles (AUVs). However, underwater gesture recognition is a challenging visual task for AUVs due to light refraction and wavelength color attenuation issues. Current gesture recognition methods classify the whole image directly or locate the hand position first and then classify the hand features. Among these purely visual approaches, textual information is largely ignored. This paper proposes a visual–textual model for underwater hand gesture recognition (VT-UHGR). The VT-UHGR model encodes the underwater diver’s image as visual features, the category text as textual features, and generates visual–textual features through multimodal interactions. We guide AUVs to use image–text matching for learning and inference. The proposed method achieves better performance than most existing purely visual methods on the dataset CADDY, demonstrating the effectiveness of using textual patterns for underwater gesture recognition.
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Zhao, Yusen, Chen Xuan, Xiaoshi Qian, Yousif Alsaid, Mutian Hua, Lihua Jin, and Ximin He. "Soft phototactic swimmer based on self-sustained hydrogel oscillator." Science Robotics 4, no. 33 (August 21, 2019): eaax7112. http://dx.doi.org/10.1126/scirobotics.aax7112.
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Oscillations are widely found in living organisms to generate propulsion-based locomotion often driven by constant ambient conditions, such as phototactic movements. Such environment-powered and environment-directed locomotions may advance fully autonomous remotely steered robots. However, most man-made oscillations require nonconstant energy input and cannot perform environment-dictated movement. Here, we report a self-sustained soft oscillator that exhibits perpetual and untethered locomotion as a phototactic soft swimming robot, remotely fueled and steered by constant visible light. This particular out-of-equilibrium actuation arises from a self-shadowing–enabled negative feedback loop inherent in the dynamic light–material interactions, promoted by the fast and substantial volume change of the photoresponsive hydrogel. Our analytical model and governing equation unveil the oscillation mechanism and design principle with key parameters identified to tune the dynamics. On this autonomous oscillator platform, we establish a broadly applicable principle for converting a continuous input into a discontinuous output. The modular design can be customized to accommodate various forms of input energy and to generate diverse oscillatory behaviors. The hydrogel oscillator showcases agile life-like omnidirectional motion in the entire three-dimensional space with near-infinite degrees of freedom. The large force generated by the powerful and long-lasting oscillation can sufficiently overcome water damping and effectively self-propel away from a light source. Such a hydrogel oscillator–based all-soft swimming robot, named OsciBot, demonstrated high-speed and controllable phototactic locomotion. This autonomous robot is battery free, deployable, scalable, and integratable. Artificial phototaxis opens broad opportunities in maneuverable marine automated systems, miniaturized transportation, and solar sails.
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D’Angelo, Vincenzo, Paolo Folino, Marco Lupia, Gianfranco Gagliardi, Gianni Cario, Francesco Cicchello Gaccio, and Alessandro Casavola. "A ROS-Based GNC Architecture for Autonomous Surface Vehicle Based on a New Multimission Management Paradigm." Drones 6, no. 12 (November 27, 2022): 382. http://dx.doi.org/10.3390/drones6120382.
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This paper presents the design and implementation of BAICal (Intelligent Autonomous Buoy by the University of Calabria), an autonomous surface vehicle (ASV) developed at the Autonomous Systems Lab (LASA) of the Department of Computer Science, Modeling, Electronics, and Systems Engineering (DIMES), University of Calabria. The basic project was born as a research program in marine robotics with multiple applications, either in the sea or in lake/river environments, for data monitoring, search and rescue operations and diver support tasks. Mechanical and hardware designs are discussed by considering a three-degree-of-freedom (3DoF) dynamical model of the vehicle. An extension to the typical guidance, navigation, and control (GNC) software architecture is presented. The software design and the implementation of a manager module (M-GNC architecture) that allows the vehicle to autonomously coordinate missions are described. Indeed, autonomous guidance and movement are only one of several more complex tasks that mobile robots have to perform in a real scenario and that allow a long-term life cycle. Module-based software architecture is developed by using the Robot Operating System (ROS) framework that is suitable for different kinds of autonomous vehicles, such as aerial, ground, surface or underwater drones.
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Fitzpatrick, Laura M., A. Zachary Trimble, and Brian S. Bingham. "VERIFICATION OF A MARINE POLLUTANT SURFACE PLUME MODEL FOR USE IN THE DEVELOPMENT OF AUTONOMOUS VEHICLE TRACKING SYSTEMS." International Oil Spill Conference Proceedings 2017, no. 1 (May 1, 2017): 1612–28. http://dx.doi.org/10.7901/2169-3358-2017.1.1612.
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ABSTRACT A marine pollutant spill environmental model that can accurately predict fine scale pollutant concentration variations on a free surface is needed in early stages of testing robotic control systems for tracking pollutant spills. The model must reproduce, for use in a robotic control system simulation environment, the fine-scale surface concentration variations observed by a robot. Furthermore, to facilitate development of robotic control systems, the model must reproduce sample spill distributions in minimal computational time. A combination Eulerian-Lagrangian type model, with two tuning parameters, was developed to produce, with minimal computational effort, the fine scale concentrations that would be observed by a robot. Multiple model scenarios were run with different tuning parameters to determine the effects of those parameters on the model’s ability to reproduce an experimental measured pollutant plume’s structure. A qualitative method for analyzing the concentration variations was established using amplitude and temporal statistical parameters. The differences in the statistical parameters between the model and experiment vary from 69%–316%. After tuning, the model produces a sample spill, which includes a high frequency concentration component not observed in the experimental data, but that generally represents the real-time, fine scale pollutant plume structure and can be used for testing control algorithms.
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Yuan, Jian, Feng Li Zhang, and Zhong Hai Zhou. "Finite-Time Formation Control for Autonomous Underwater Vehicles with Limited Speed and Communication Range." Applied Mechanics and Materials 511-512 (February 2014): 909–12. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.909.
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Cooperative control of multiple autonomous underwater vehicles (AUVs) plays an important role on marine scientific investigation and marine development. The formation of multi-AUV can significantly enhance applications on the marine sampling, imaging, surveillance and communications. Compared to the formation control of multi-robot, the formation control of multi-AUV is particularly difficult, especially on controlling attitude and direction of AUV; what is more, the communication method among AUVs is acoustic. When communication distance increases, the communication qualities deteriorate quickly; this mainly makes time-delay, signal attenuation and distortion. Although formation control of multiple AUVs obtains a wide range of attention in recent years, the fruits on formation control problem are less than ones on land multi-robot problems. For example, Fiorelli conducted a collaborative and adaptive sampling research of multi-AUV at the Monterey Bay [; Yu and Ura carried out the cable-based modular fast-moving and obstacle-avoidance experiments, and presented an interconnected multi-AUV system with three-dimension sensors. On the aspect of formation control framework [2-, [ proposed a four-layer cooperative control strategy based on hierarchical structure; [ proposed a hierarchical control framework based on hybrid model. In addition, Yang converted a nonholonomic system to a chain one and designed a controller to implement a leader-follower formation for multiple AUVs in [. The formation control for multiple autonomous underwater vehicles is rather different than the control methods for other vehicles, because the formation control for AUVs is of its characteristics, such as the large-scale distribution in space. The finite-time consensus controller designing based on finite-time control and consensus problem has important theoretical and practical significance. The decentralized controller methods for the autonomous underwater vehicle are applied more and more, but they ignore the coupling relationship between them. Another method is that an AUV is modeling as an agent, but this method ignores attitude characteristics of AUVs (pitch, roll and yaw). In this paper, we consider the cooperative control problem in three dimensional spaces. Finite-time formation for Autonomous Underwater Vehicles (AUVs) with constraints on communication range is investigated. We proposed a two-layer finite-time consensus control law, to avoid leading to collapse on formation because of failure leader, all AUVs are arrayed in the same level and each AUV can obtain global formation information. Finally, the simulation results show the effectiveness of the control strategy.
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Babić, Anja, Filip Mandić, and Nikola Mišković. "Development of Visual Servoing-Based Autonomous Docking Capabilities in a Heterogeneous Swarm of Marine Robots." Applied Sciences 10, no. 20 (October 13, 2020): 7124. http://dx.doi.org/10.3390/app10207124.
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This paper describes the design, development, and testing of both hardware and software for a visual servoing-based system that enables agents within a heterogeneous marine robotic swarm to share energy. The goal of this system is prolonging the active operational time of the swarm as a whole, allowing it to perform long-term environmental monitoring and data collection missions. The implementation presented in the paper features an over-actuated autonomous surface platform docking up to four floating sensor nodes at a time and replenishing their batteries using wireless inductive charging. Mechanical solutions for each robot segment related to the docking procedure are presented, along with pertinent high-level and low-level control structures. A node featuring an extended Kalman filter and additional heuristics is used to fuse measurements from a neural network trained for object detection and a hue thresholding image processing algorithm, in order to track the docking target and achieve visual servoing. Finally, experimental results in both a controlled environment and challenging conditions on-site are presented. Once deployed, the developed system successfully enables the approach and docking of the designated target, showing its feasibility in different real-life conditions.
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Cheng, Qiyun, Wenyuan Mo, Long Chen, Wei Ke, Jun Hu, and Yuwei Wu. "Numerical Study of Different Engineering Conditions on the Propulsive Performance of the Bionic Jellyfish Robot." Sustainability 15, no. 5 (February 25, 2023): 4186. http://dx.doi.org/10.3390/su15054186.
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Underwater robotics is rapidly evolving due to the increasing demand for marine resource exploitation. Compared with rigid robots propelled by propellers, bionic robots are stealthier and more maneuverable, such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs), making them widely used underwater. In order to study the motion state of the umbrella jellyfish bionic robot, the displacement of the jellyfish robot along the same direction and the surrounding fluid pressure distribution caused by the jellyfish motion under different experimental conditions are discussed in this paper. The effect of different environmental factors on driving the jellyfish robot is determined by comparing the displacements at different observation points. The results of the study show that the lower the frequency and the longer the motion period, the greater the displacement produced by the robot within the same motion period. Frequency has a significant effect on the motion state of the jellyfish robot. While the change of amplitude also affects the motion state of the jellyfish robot, the displacement of the relaxation phase of the jellyfish robot is much smaller than that of the contraction phase with a small amplitude. It can be concluded that the effect of frequency on robot displacement is greater than the effect of amplitude on robot displacement. This study qualitatively discusses the changes of the motion state of the bionic jellyfish robot in still water under the excitation of different frequencies and amplitudes, and the results can provide corresponding reference for the future application of the bionic jellyfish robot, such as resource exploration, underwater exploration, and complex environment exploration.
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Ferreira, Fausto, Igor Kvasić, Đula Nađ, Luka Mandić, Nikola Mišković, Christopher Walker, Derek Orbaugh Antillon, and Iain Anderson. "Diver‐Robot Communication Using Wearable Sensing: Remote Pool Experiments." Marine Technology Society Journal 56, no. 5 (October 14, 2022): 26–35. http://dx.doi.org/10.4031/mtsj.56.5.5.
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Abstract Diver‐robot interaction is an exciting and recent field of study. There are different ways a diver and robot can interact, such as using tablets or detecting divers with cameras or sonars. A novel approach presented in this paper uses direct diver‐robot communication. To facilitate communication for humans, we use typical diver gestures, which are transmitted to a robot using a wearable glove and acoustic communications. Following previous work by the University of Zagreb and the University of Auckland, a collaboration to control an autonomous underwater vehicle based on a wearable diver glove has been made possible through the EU Marine Robots project. Under this project, Trans-National Access trials allow Laboratory for Underwater Systems and Technologies, University of Zagreb, to offer its robots and infrastructure to external partners. Initial trials with the University of Auckland, which were planned to take place on site, were transformed into remote access trials. This paper reports on these challenging trials and collaboration given the distance and time zone difference. The key point is to demonstrate the possibility of having a diver remotely controlling a robot using typical gestures recognized by a wearable glove and transmitted via acoustic modems (and the Internet for the remote connection).
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Breier, John A., Michael V. Jakuba, Mak A. Saito, Gregory J. Dick, Sharon L. Grim, Eric W. Chan, Matthew R. McIlvin, et al. "Revealing ocean-scale biochemical structure with a deep-diving vertical profiling autonomous vehicle." Science Robotics 5, no. 48 (November 25, 2020): eabc7104. http://dx.doi.org/10.1126/scirobotics.abc7104.
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Vast and diverse microbial communities exist within the ocean. To better understand the global influence of these microorganisms on Earth’s climate, we developed a robot capable of sampling dissolved and particulate seawater biochemistry across ocean basins while still capturing the fine-scale biogeochemical processes therein. Carbon and other nutrients are acquired and released by marine microorganisms as they build and break down organic matter. The scale of the ocean makes these processes globally relevant and, at the same time, challenging to fully characterize. Microbial community composition and ocean biochemistry vary across multiple physical scales up to that of the ocean basins. Other autonomous underwater vehicles are optimized for moving continuously and, primarily, horizontally through the ocean. In contrast, Clio, the robot that we describe, is designed to efficiently and precisely move vertically through the ocean, drift laterally in a Lagrangian manner to better observe water masses, and integrate with research vessel operations to map large horizontal scales to a depth of 6000 meters. We present results that show how Clio conducts high-resolution sensor surveys and sample return missions, including a mapping of 1144 kilometers of the Sargasso Sea to a depth of 1000 meters. We further show how the samples obtain filtered biomass from seawater that enable genomic and proteomic measurements not possible through in situ sensing. These results demonstrate a robotic oceanography approach for global-scale surveys of ocean biochemistry.
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White, Connor F., Yukun Lin, Christopher M. Clark, and Christopher G. Lowe. "Human vs robot: Comparing the viability and utility of autonomous underwater vehicles for the acoustic telemetry tracking of marine organisms." Journal of Experimental Marine Biology and Ecology 485 (December 2016): 112–18. http://dx.doi.org/10.1016/j.jembe.2016.08.010.
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Li, Bin, Jianlin Mao, Shuyi Yin, Lixia Fu, and Yan Wang. "Path Planning of Multi-Objective Underwater Robot Based on Improved Sparrow Search Algorithm in Complex Marine Environment." Journal of Marine Science and Engineering 10, no. 11 (November 8, 2022): 1695. http://dx.doi.org/10.3390/jmse10111695.
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Autonomous underwater vehicle (AUV) path planning in complex marine environments meets many chanllenges, such as many influencing factors, complex models and the performance of the optimization algorithm to be improved. To find a path with minimum cost of the actual environmental threats, navigation height changes and the minimum energy consumption of the AUV, an improved sparrow search algorithm is designed under the impact of the time-varying characteristics of the current in the complex marine environment on the AUV and the physical constraints of the AUV movement. In the proposed algorithm, an adaptive weight factor balance strategy is introduced into the position update of the discoverer to improve the convergence speed and search ability. In the follower position, the variable spiral search strategy is improved based on the position update formula to enhance the local jumping ability of the algorithm. After the position update, Levy flying strategy is added, and Cauchy–Gaussian mutation is performed on the optimal individual to increase the population diversity, which improves the anti-stagnation ability of the algorithm. Finally, the AUV global path with minimum cost in the sense of multi-objective weighting is obtained. The simulation results show that the proposed algorithm can plan a path with less cost and quickly converge to an optimized point, and finally it can meet the global path planning requirements of AUV when navigating in complex sea areas.
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Griffin, Robert, Stephen McCrory, Sylvain Bertrand, Duncan Calvert, Inho Lee, Peter Neuhaus, Doug Stephen, et al. "Quadrupedal Walking over Complex Terrain with a Quasi-Direct Drive Actuated Robot." Field Robotics 2, no. 1 (March 10, 2022): 356–84. http://dx.doi.org/10.55417/fr.2022013.
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In this paper, we present our approach to achieve autonomous walking over complex terrain on the quadrupedal robot, LLAMA. LLAMA is a prototype robot designed by NASA Jet Propulsion Lab as part of the Army Research Laboratory’s Robotics Collaborative Technology Alliance. One of the major objectives of this robot is to be capable of traversing complex terrain autonomously to enable operating alongside a human squadron. This goal requires the robot to be capable of identifying practical footholds according to the environment, which may be sparse, and using these for walking. To accomplish this end, we first introduce two new contact planners. One is based on either desired body path plans; the other an A* graph-search based planner that find contacts over rough terrain given the environmental constraints. We then plan a dynamic trajectory using a custom Divergent Component of Motion planner, which is tracked using a whole-body inverse-dynamics control framework. We also introduce new methods for maintaining balance by adjusting step position and timing. We additionally discuss our approach for contact detection without the use of force sensors. We highlight the results in several experiments, both in our laboratory and at the RCTA Capstone Event at the Camp Lejeune Marine Corps base. We conclude with a discussion of these results, specific implementation problems, and lessons learned when developing such a control architecture on a quadruped.
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Bingham, Brian S., Jeffrey M. Walls, and Ryan M. Eustice. "Development of a Flexible Command and Control Software Architecture for Marine Robotic Applications." Marine Technology Society Journal 45, no. 3 (May 1, 2011): 25–36. http://dx.doi.org/10.4031/mtsj.45.3.4.
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AbstractThis paper reports the implementation of a supervisory control framework and modular software architecture built around the lightweight communication and marshalling (LCM) publish/subscribe message passing system. In particular, we examine two diverse marine robotics applications using this modular system: (i) the development of an unmanned port security vehicle, a robotic surface platform to support first responders reacting to transportation security incidents in harbor environments, and (ii) the adaptation of a commercial off-the-shelf autonomous underwater vehicle (the Ocean-Server Iver2) for visual feature-based navigation. In both cases, the modular vehicle software infrastructures are based around the open-source LCM software library for low-latency, real-time message passing. To elucidate the real-world application of LCM in marine robotic systems, we present the software architecture of these two successful marine robotic applications and illustrate the capabilities and flexibilities of this approach to real-time marine robotics. We present benchmarking test results comparing the throughput of LCM with the Mission-Oriented Operating Suite, another robot software system popular in marine robotics. Experimental results demonstrate the capacity of the LCM framework to make large amounts of actionable information available to the operator and to allow for distributed supervisory control. We also provide a discussion of the qualitative tradeoffs involved in selecting software infrastructure for supervisory control.
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Liu, Tao, Yuli Hu, and Hui Xu. "Deep Reinforcement Learning for Vectored Thruster Autonomous Underwater Vehicle Control." Complexity 2021 (April 23, 2021): 1–25. http://dx.doi.org/10.1155/2021/6649625.
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Autonomous underwater vehicles (AUVs) are widely used to accomplish various missions in the complex marine environment; the design of a control system for AUVs is particularly difficult due to the high nonlinearity, variations in hydrodynamic coefficients, and external force from ocean currents. In this paper, we propose a controller based on deep reinforcement learning (DRL) in a simulation environment for studying the control performance of the vectored thruster AUV. RL is an important method of artificial intelligence that can learn behavior through trial-and-error interactions with the environment, so it does not need to provide an accurate AUV control model that is very hard to establish. The proposed RL algorithm only uses the information that can be measured by sensors inside the AUVs as the input parameters, and the outputs of the designed controller are the continuous control actions, which are the commands that are set to the vectored thruster. Moreover, a reward function is developed for deep RL controller considering different factors which actually affect the control accuracy of AUV navigation control. To confirm the algorithm’s effectiveness, a series of simulations are carried out in the designed simulation environment, which is a method to save time and improve efficiency. Simulation results prove the feasibility of the deep RL algorithm applied to the control system for AUV. Furthermore, our work also provides an optional method for robot control problems to deal with improving technology requirements and complicated application environments.
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Costa, Daniele, Giacomo Palmieri, Matteo-Claudio Palpacelli, David Scaradozzi, and Massimo Callegari. "Design of a Carangiform Swimming Robot through a Multiphysics Simulation Environment." Biomimetics 5, no. 4 (September 30, 2020): 46. http://dx.doi.org/10.3390/biomimetics5040046.
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Bio-inspired solutions devised for autonomous underwater robots are currently being investigated by researchers worldwide as a way to improve propulsion. Despite efforts to harness the substantial potential payoffs of marine animal locomotion, biological system performance still has far to go. In order to address this very ambitious objective, the authors of this study designed and manufactured a series of ostraciiform swimming robots over the past three years. However, the pursuit of the maximum propulsive efficiency by which to maximize robot autonomy while maintaining acceptable maneuverability ultimately drove us to improve our design and move from ostraciiform to carangiform locomotion. In order to comply with the tail motion required by the aforementioned swimmers, the authors designed a transmission system capable of converting the continuous rotation of a single motor in the travelling wave-shaped undulations of a multijoint serial mechanism. The propulsive performance of the resulting thruster (i.e., the caudal fin), which constitutes the mechanism end effector, was investigated by means of computational fluid dynamics techniques. Finally, in order to compute the resulting motion of the robot, numerical predictions were integrated into a multibody model that also accounted for the mass distribution inside the robotic swimmer and the hydrodynamic forces resulting from the relative motion between its body and the surrounding fluid. Dynamic analysis allowed the performance of the robotic propulsion to be computed while in the cruising condition.
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Chen, Yanhu, Siyue Liu, Jinchang Fan, and Canjun Yang. "Novel Online Optimized Control for Underwater Pipe-Cleaning Robots." Applied Sciences 10, no. 12 (June 22, 2020): 4279. http://dx.doi.org/10.3390/app10124279.
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Due to the particularity of the jacket structure of offshore platforms and the complexity of the marine environment, there have been few effective localization and autonomous control methods for underwater robots that are designed for cleaning tasks. To improve this situation, a fusion bat algorithm (BA) online optimized fuzzy control method using vision localization was developed based on the constraints of the underwater operational environment. Vision localization was achieved based on images from a catadioptric panoramic imaging system. The features of the pipe edge and the boundary of the area covered by biofouling were obtained by image processing and feature extraction. The feature point chosen as the “highest” point of the boundary was calculated by projection transformation to generate the reference path. The specialized fuzzy controller was designed to drive the robot to track the reference path, and an improved bat algorithm with dynamic inertia weight and differential evolution method was developed to optimize the scale factors of the fuzzy controller online. The control method was simulated and further implemented on an underwater pipe-cleaning robot (UPCR), and the results indicate its rationality and validity.
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Hu, Kai, Feiyu Lu, Meixia Lu, Zhiliang Deng, and Yunping Liu. "A Marine Object Detection Algorithm Based on SSD and Feature Enhancement." Complexity 2020 (September 30, 2020): 1–14. http://dx.doi.org/10.1155/2020/5476142.
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Autonomous detection and fishing by underwater robots will be the main way to obtain aquatic products in the future; sea urchins are the main research object of aquatic product detection. When the classical Single-Shot MultiBox Detector (SSD) algorithm is applied to the detection of sea urchins, it also has disadvantages of being inaccurate to small targets and insensitive to the direction of the sea urchin. Based on the classic SSD algorithm, this paper proposes a feature-enhanced sea urchin detection algorithm. Firstly, according to the spiny-edge characteristics of a sea urchin, a multidirectional edge detection algorithm is proposed to enhance the feature, which is taken as the 4th channel of image and the original 3 channels of underwater image together as the input for the further deep learning. Then, in order to improve the shortcomings of SSD algorithm’s poor ability to detect small targets, resnet 50 is used as the basic framework of the network, and the idea of feature cross-level fusion is adopted to improve the feature expression ability and strengthen semantic information. The open data set provided by the National Natural Science Foundation of China underwater Robot Competition will be used as the test set and training set. Under the same training and test conditions, the AP value of the algorithm in this paper reaches 81.0%, 7.6% higher than the classic SSD algorithm, and the confidence of small target analysis is also improved. Experimental results show that the algorithm in this paper can effectively improve the accuracy of sea urchin detection.
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Fagundes Gasparoto, Henrique, Olivier Chocron, Mohamed Benbouzid, Pablo Siqueira Meirelles, and Luiz Saraiva Ferreira. "Torque Analysis of a Flat Reconfigurable Magnetic Coupling Thruster for Marine Renewable Energy Systems Maintenance AUVs." Energies 12, no. 1 (December 25, 2018): 56. http://dx.doi.org/10.3390/en12010056.
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The concept of reconfigurable magnetic coupling thrusters (RMCT) applied to the vectorial thrust of autonomous underwater vehicles (AUV) has been recently developed and presented. This technology ensures greater robot watertightness with enhanced maneuvering capabilities, which are desired features in agile AUVs for marine renewable energy (MRE) system maintenance. It is possible since in RMCTs the driving torque is magnetically transmitted to the propeller, which has its orientation changed. This work is focused on the coupling and control torque calculation and further analysis of the latest prototype version (Flat-RMCT), in the static condition for the full thrust vector range. For this purpose, a numerical model is implemented and validated with experimental results. The numerical model is based on the finite volume integral method. The results indicate that the minimum magnetic reluctance propensity creates not only the expected magnetic spring effect but also an auto-driving torque due to the non-axial symmetry of coupling rotors, which exists only for reconfigurable couplings. Mathematical functions are proposed to model these effects and they are used to extend the understanding of the coupling. These models can be used to compose a full and accurate dynamic model for a better RMCT simulation, identification, and control.
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Ishii, Kazuo, Eiji Hayashi, Norhisam Bin Misron, and Blair Thornton. "Special Issue on Advanced Robotics in Agriculture, Forestry and Fisheries." Journal of Robotics and Mechatronics 30, no. 2 (April 20, 2018): 163–64. http://dx.doi.org/10.20965/jrm.2018.p0163.
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The importance of primary industries, agriculture, forestry and fisheries, is obvious and needless to mention, however, the reduction of the working population and the aging problem make the situation of primary industry more sever. To compensate for the issues, the advanced technology in robotics has attracted attentions and expected the contributions in terms of productivity, cost effectiveness, pesticide-less, monitoring of the growth and harvesting, etc. Recently, robotic technologies are gradually being used in primary industry and their application area will expand more in the near future. This special issue’s objectives include collecting recent advances, automation, mechanization, research trends and their applications in agriculture, forestry and fisheries to promote a deeper understanding of major conceptual and technical challenges and facilitate spreading of recent breakthroughs in primary industries, and contribute to the enhancement of the quality of agricultural, forestry and fisheries robots by introducing the state-of-the-art in sensing, mobility, manipulation and related technologies. In this special issue, twelve papers are included. The first paper by Noguchi is the survey paper of the state-of-the-art in the agricultural vehicle type robots and discusses the future scope of agriculture with robotics. The next three papers are on tomato-monitoring system, and Fukui et al. propose a tomato fruit volume estimation method using saliency-based image processing and point cloud and clustering technology, Yoshida et al. do the cutting point identification for tomato-harvesting using a RGBD sensor and evaluate in the real farm experiments, and Fujinaga et al. present an image mosaicking method of tomato yard based on the infrared images and color images of tomato-clusters in the large green house. The fifth paper by Sori et al. reports a paddy weeding robot in wet-rice field to realize the pesticide-free produce of rice, and the sixth paper by Shigeta et al. is about an image processing system to measure cow’s BCS (Body Condition Score) automatically before milking cows and analyzes the two months data by CNN (Convolutional Neural Network). The seventh paper by Inoue et al. reports on an upper-limb power assist robot with a single actuator to reduce the weight and cost. The assist machine supports the shoulder and elbow movements for viticulture operations and upper-limb holding for load transport tasks. In the next paper, Tominaga et al. show an autonomous robotic system to move between the trees without damaging them and to cut the weeds in the forest for the forest industry. The last four papers are for the fishery industry, and Komeyama et al. propose a methods for monitoring the size of fish, red sea bream (RSB) aquaculture by developing a stereo vision system to avoid the risks of physical injury and mental stress to the fish. Nishida et al. report on a hovering type underwater robot to measure seafloor for monitoring marine resources whose sensor can be replaced depending on missions as the open hardware system. Yasukawa et al. propose a vision system for an autonomous underwater robot with a benthos sampling function, especially, sampling-autonomous underwater vehicles (SAUVs) to achieve a new sampling mission. The last paper by Han et al. is for gait planning and simulation analysis of an amphibious quadruped robot in the field of fisheries and aquaculture. We hope that this special issue can contributes to find solutions in primary industries, agriculture, forestry and fisheries.
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Jorge, Vitor, Roger Granada, Renan Maidana, Darlan Jurak, Guilherme Heck, Alvaro Negreiros, Davi dos Santos, Luiz Gonçalves, and Alexandre Amory. "A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions." Sensors 19, no. 3 (February 8, 2019): 702. http://dx.doi.org/10.3390/s19030702.
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Disaster robotics has become a research area in its own right, with several reported cases of successful robot deployment in actual disaster scenarios. Most of these disaster deployments use aerial, ground, or underwater robotic platforms. However, the research involving autonomous boats or Unmanned Surface Vehicles (USVs) for Disaster Management (DM) is currently spread across several publications, with varying degrees of depth, and focusing on more than one unmanned vehicle—usually under the umbrella of Unmanned Marine Vessels (UMV). Therefore, the current importance of USVs for the DM process in its different phases is not clear. This paper presents the first comprehensive survey about the applications and roles of USVs for DM, as far as we know. This work demonstrates that there are few current deployments in disaster scenarios, with most of the research in the area focusing on the technological aspects of USV hardware and software, such as Guidance Navigation and Control, and not focusing on their actual importance for DM. Finally, to guide future research, this paper also summarizes our own contributions, the lessons learned, guidelines, and research gaps.
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Ochoa, Eduardo, Nuno Gracias, Klemen Istenič, Josep Bosch, Patryk Cieślak, and Rafael García. "Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision." Sensors 22, no. 14 (July 18, 2022): 5354. http://dx.doi.org/10.3390/s22145354.
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Exploration of marine habitats is one of the key pillars of underwater science, which often involves collecting images at close range. As acquiring imagery close to the seabed involves multiple hazards, the safety of underwater vehicles, such as remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs), is often compromised. Common applications for obstacle avoidance in underwater environments are often conducted with acoustic sensors, which cannot be used reliably at very short distances, thus requiring a high level of attention from the operator to avoid damaging the robot. Therefore, developing capabilities such as advanced assisted mapping, spatial awareness and safety, and user immersion in confined environments is an important research area for human-operated underwater robotics. In this paper, we present a novel approach that provides an ROV with capabilities for navigation in complex environments. By leveraging the ability of omnidirectional multi-camera systems to provide a comprehensive view of the environment, we create a 360° real-time point cloud of nearby objects or structures within a visual SLAM framework. We also develop a strategy to assess the risk of obstacles in the vicinity. We show that the system can use the risk information to generate warnings that the robot can use to perform evasive maneuvers when approaching dangerous obstacles in real-world scenarios. This system is a first step towards a comprehensive pilot assistance system that will enable inexperienced pilots to operate vehicles in complex and cluttered environments.
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Burguera, Antoni, Francisco Bonin-Font, Eric Guerrero Font, and Antoni Martorell Torres. "Combining Deep Learning and Robust Estimation for Outlier-Resilient Underwater Visual Graph SLAM." Journal of Marine Science and Engineering 10, no. 4 (April 6, 2022): 511. http://dx.doi.org/10.3390/jmse10040511.
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Visual Loop Detection (VLD) is a core component of any Visual Simultaneous Localization and Mapping (SLAM) system, and its goal is to determine if the robot has returned to a previously visited region by comparing images obtained at different time steps. This paper presents a new approach to visual Graph-SLAM for underwater robots that goes one step forward the current techniques. The proposal, which centers its attention on designing a robust VLD algorithm aimed at reducing the amount of false loops that enter into the pose graph optimizer, operates in three steps. In the first step, an easily trainable Neural Network performs a fast selection of image pairs that are likely to close loops. The second step carefully confirms or rejects these candidate loops by means of a robust image matcher. During the third step, all the loops accepted in the second step are subject to a geometric consistency verification process, being rejected those that do not fit with it. The accepted loops are then used to feed a Graph-SLAM algorithm. The advantages of this approach are twofold. First, the robustness in front of wrong loop detection. Second, the computational efficiency since each step operates only on the loops accepted in the previous one. This makes online usage of this VLD algorithm possible. Results of experiments with semi-synthetic data and real data obtained with an autonomous robot in several marine resorts of the Balearic Islands, support the validity and suitability of the approach to be applied in further field campaigns.
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Mitra, Santanu, Vaibhav Sehgal, Shubham Rathore, Raghav Puri, Shivani Chouhan, and Aditya Sharma. "Design and Control Strategy of Bio-inspired Underwater Vehicle with Flexible Propulsor." Journal of Modern Mechanical Engineering and Technology 8 (December 7, 2021): 57–65. http://dx.doi.org/10.31875/2409-9848.2021.08.7.
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Biomimetics aims to take inspiration from nature and develop new models and efficient systems for a sustainable future. Bioinspired underwater robotics help develop future submarines that will navigate through the water using flexible propulsor. This research has focused on the Manta Ray species as batoid has a unique advantage over other species. This study also aims to improve AUV (Autonomous Underwater Vehicle) efficiency through biomimetic design, the purpose of which is to observe and study the marine environment, be it for sea exploration or navigation. The design and prototyping process of bioinspired AUVs have been mentioned in this study, along with testing a propulsive mechanism for efficient swimming and turning capabilities. The Robot was designed taking structural considerations from the actual Manta-Ray locomotion and body design. The propulsion mechanism and control circuit were then implemented on the developed systems. The prototype of the Manta Ray was able to generate a realistic swimming pattern and was tested in an acrylic tank. The experimental results obtained in the tank basin are very close to the results we observe in the real-world scenario in terms of the vehicle's forward and turning motion.
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Sun, Yushan, Xiaokun Luo, Xiangrui Ran, and Guocheng Zhang. "A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons." Journal of Marine Science and Engineering 9, no. 3 (February 27, 2021): 252. http://dx.doi.org/10.3390/jmse9030252.
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This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.
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Belіkov, V., O. Hryhoriev, S. Kovalishyn, and I. Symonenkova. "MODERN SYSTEM OF THE POWER SUPPLY OF THE MODULE TRANSPORT PLATFORMS OF GROUND ROBOTIC COMPLEXES FOR COVERT COMBAT ACTIONS." Collection of scientific works of Odesa Military Academy 2, no. 12 (December 27, 2019): 33–38. http://dx.doi.org/10.37129/2313-7509.2019.12.2.33-38.
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The choice of energy sources to ensure the smooth functioning of the entire set of actuators of an autonomous transport platform of ground military robot, including traction propulsion, has a decisive impact on the overall performance and viability of the entire GRC (ground robotic complex) and determines, ultimately, its quality and reliability in the process. For covert warfare, recommending the use of energy sources such as internal combustion engines is impractical because of the high levels of inherent acoustic noise. With the advent of electrochemical energy sources, significant progress has been made in the development of a number of high technologies, namely, in the newest fields of technology - portable electronics and power electromechanics. Power sources such as lithium-ion batteries have proven to be much lighter and more compact than other types of batteries. Therefore, electrochemical lithium-ion batteries have been widely used in military robotics, ranging from small UAVs and multicopters to portable ground combat robots and marine robotic vehicles. Recent achievements in enhancing the specific performance of batteries of this type make it possible to use them in the electromechanical motors of modular transport platforms of modern ground robots for covert combat operations with a carrying capacity of up to 500 kg and above. The analytical purpose of the materials in this article is to compare the potential sources of electricity in terms of their practical use in military robotics, namely, in the transport platforms of modern GRC for covert warfare. Frame-modular platforms with a capacity of 50, 150 and 500 kg were adopted for the real objects with installed sources of electricity. The principal feature of these platforms is the presence of rigid tubular modular horizontal frame made of precision steel or composite tubes. In the internal cavities of these pipes it is proposed to install power sources made in the form of cylindrical functional blocks-modules.
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Zou, Tao, Weilun Situ, Wenlin Yang, Weixiang Zeng, and Yunting Wang. "A Method for Long-Term Target Anti-Interference Tracking Combining Deep Learning and CKF for LARS Tracking and Capturing." Remote Sensing 15, no. 3 (January 28, 2023): 748. http://dx.doi.org/10.3390/rs15030748.
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Autonomous underwater vehicles (AUV) recycling in an underwater environment is particularly challenging due to the continuous exploitation of marine resources. AUV recycling via visual technology is the primary method. However, the current visual technology is limited by harsh sea conditions and has problems, such as poor tracking and detection. To solve these problems, we propose a long-term target anti-interference tracking (LTAT) method, which integrates Siamese networks, You Only Look Once (YOLO) networks and online learning ideas. Meanwhile, we propose using the cubature Kalman filter (CKF) for optimization and prediction of the position. We constructed a launch and recovery system (LARS) tracking and capturing the AUV. The system consists of the following parts: First, images are acquired via binocular cameras. Next, the relative position between the AUV and the end of the LARS was estimated based on the pixel positions of the tracking AUV feature points and binocular camera data. Finally, using a discrete proportion integration differentiation (PID) method, the LARS is controlled to capture the moving AUV via a CKF-optimized position. To verify the feasibility of our proposed system, we used the robot operating system (ROS) platform and Gazebo software to simulate the system for experiments and visualization. The experiment demonstrates that in the tracking process when the AUV makes a sinusoidal motion with an amplitude of 0.2 m in the three-dimensional space and the relative distance between the AUV and LARS is no more than 1 m, the estimated position error of the AUV does not exceed 0.03 m. In the capturing process, the final capturing error is about 28 mm. Our results verify that our proposed system has high robustness and accuracy, providing the foundation for future AUV recycling research.
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Sayed, Mohammed, Markus Nemitz, Simona Aracri, Alistair McConnell, Ross McKenzie, and Adam Stokes. "The Limpet: A ROS-Enabled Multi-Sensing Platform for the ORCA Hub." Sensors 18, no. 10 (October 16, 2018): 3487. http://dx.doi.org/10.3390/s18103487.
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The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. Robots present a cost-effective and safe method for inspection, repair, and maintenance of topside and marine offshore infrastructure. In this work, we introduce a new multi-sensing platform, the Limpet, which is designed to be low-cost and highly manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet can be considered an instrument, where in abstract terms, an instrument is a device that transforms a physical variable of interest (measurand) into a form that is suitable for recording (measurement). The Limpet is designed to be part of the ORCA (Offshore Robotics for Certification of Assets) Hub System, which consists of the offshore assets and all the robots (Underwater Autonomous Vehicles, drones, mobile legged robots etc.) interacting with them. The Limpet comprises the sensing aspect of the ORCA Hub System. We integrated the Limpet with Robot Operating System (ROS), which allows it to interact with other robots in the ORCA Hub System. In this work, we demonstrate how the Limpet can be used to achieve real-time condition monitoring for offshore structures, by combining remote sensing with signal-processing techniques. We show an example of this approach for monitoring offshore wind turbines, by designing an experimental setup to mimic a wind turbine using a stepper motor and custom-designed acrylic fan blades. We use the distance sensor, which is a Time-of-Flight sensor, to achieve the monitoring process. We use two different approaches for the condition monitoring process: offline and online classification. We tested the offline classification approach using two different communication techniques: serial and Wi-Fi. We performed the online classification approach using two different communication techniques: LoRa and optical. We train our classifier offline and transfer its parameters to the Limpet for online classification. We simulated and classified four different faults in the operation of wind turbines. We tailored a data processing procedure for the gathered data and trained the Limpet to distinguish among each of the functioning states. The results show successful classification using the online approach, where the processing and analysis of the data is done on-board by the microcontroller. By using online classification, we reduce the information density of our transmissions, which allows us to substitute short-range high-bandwidth communication systems with low-bandwidth long-range communication systems. This work shines light on how robots can perform on-board signal processing and analysis to gain multi-functional sensing capabilities, improve their communication requirements, and monitor the structural health of equipment.
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Kalwa, Joerg, Daniel Tietjen, Marina Carreiro-Silva, Jorge Fontes, Lorenzo Brignone, Nuno Gracias, Pere Ridao, et al. "The European Project MORPH: Distributed UUV Systems for Multimodal, 3D Underwater Surveys." Marine Technology Society Journal 50, no. 4 (July 1, 2016): 26–41. http://dx.doi.org/10.4031/mtsj.50.4.10.
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AbstractThe MORPH project (FP 7, 2012‐2016) is aimed at developing efficient methods and tools to map the underwater environment in situations that are not easily addressed by current technology. Namely, the missions that are of interest are those that involve underwater surveying and marine habitat mapping of rugged terrain and structures with full 3D complexity, including vertical cliffs. Potential applications include the study of cold water coral reef communities, ecosystems from underwater canyons, pipeline and harbor monitoring, or the inspection of wind turbine foundations. The project introduced and advanced a novel concept of an underwater robotic system composed of a number of mobile robot modules (nodes), carrying complementary sensors for perception of the environment. Instead of being physically coupled, the modules are connected via communication links that allow a flow of essential information among them. Without rigid links, the so-called MORPH Supra-Vehicle can reconfigure itself and adapt according to the environment and mission goals, responding, for example, to the shape of the terrain, including vertical walls. The flexibility allows for more optimal positioning of each sensor, increased number of simultaneous viewpoints, and generally high-resolution data collection.MORPH is aimed at providing a proof-of-concept demonstration of such capabilities, an effort that includes technological developments in many of the subfields of underwater technology. The main results are summarized and presented in this paper.<def-list>Abbreviation List<def-item><term>AUV</term><def>autonomous underwater vehicles</def></def-item><def-item><term>CV</term><def>camera vehicle</def></def-item><def-item><term>CWC</term><def>cold water corals</def></def-item><def-item><term>GCV</term><def>global navigation and communications vehicle</def></def-item><def-item><term>ICP</term><def>iterative closest point method</def></def-item><def-item><term>LSV</term><def>local sonar vehicle</def></def-item><def-item><term>MBES</term><def>multibeam echosounder</def></def-item><def-item><term>MCL</term><def>mission control language</def></def-item><def-item><term>PF</term><def>path following</def> </def-item><def-item><term>PI</term><def>principal investigator</def></def-item><def-item><term>ROF</term><def> range-only formation</def></def-item><def-item><term>ROS</term><def>Robot Operation System</def></def-item><def-item><term>SSV</term><def>surface support vessel</def></def-item> <def-item> <term>TDMA</term> <def> time division multiple access </def> </def-item><def-item><term>USBL</term><def>ultra-short baseline (navigation)</def></def-item> <def-item> <term>UUV</term> <def> unmanned underwater vehicle </def> </def-item><def-item><term>VCS</term><def>version control system</def></def-item></def-list>
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Macaulay, Michael O., and Mahmood Shafiee. "Machine learning techniques for robotic and autonomous inspection of mechanical systems and civil infrastructure." Autonomous Intelligent Systems 2, no. 1 (April 29, 2022). http://dx.doi.org/10.1007/s43684-022-00025-3.
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AbstractMachine learning and in particular deep learning techniques have demonstrated the most efficacy in training, learning, analyzing, and modelling large complex structured and unstructured datasets. These techniques have recently been commonly deployed in different industries to support robotic and autonomous system (RAS) requirements and applications ranging from planning and navigation to machine vision and robot manipulation in complex environments. This paper reviews the state-of-the-art with regard to RAS technologies (including unmanned marine robot systems, unmanned ground robot systems, climbing and crawler robots, unmanned aerial vehicles, and space robot systems) and their application for the inspection and monitoring of mechanical systems and civil infrastructure. We explore various types of data provided by such systems and the analytical techniques being adopted to process and analyze these data. This paper provides a brief overview of machine learning and deep learning techniques, and more importantly, a classification of the literature which have reported the deployment of such techniques for RAS-based inspection and monitoring of utility pipelines, wind turbines, aircrafts, power lines, pressure vessels, bridges, etc. Our research provides documented information on the use of advanced data-driven technologies in the analysis of critical assets and examines the main challenges to the applications of such technologies in the industry.
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Li, Lei, Siqi Wang, Yiyuan Zhang, Shanyuan Song, Chuqian Wang, Shaochang Tan, Wei Zhao, et al. "Aerial-aquatic robots capable of crossing the air-water boundary and hitchhiking on surfaces." Science Robotics 7, no. 66 (May 4, 2022). http://dx.doi.org/10.1126/scirobotics.abm6695.
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Many real-world applications for robots—such as long-term aerial and underwater observation, cross-medium operations, and marine life surveys—require robots with the ability to move between the air-water boundary. Here, we describe an aerial-aquatic hitchhiking robot that is self-contained for flying, swimming, and attaching to surfaces in both air and water and that can seamlessly move between the two. We describe this robot’s redundant, hydrostatically enhanced hitchhiking device, inspired by the morphology of a remora ( Echeneis naucrates ) disc, which works in both air and water. As with the biological remora disc, this device has separate lamellar compartments for redundant sealing, which enables the robot to achieve adhesion and hitchhike with only partial disc attachment. The self-contained, rotor-based aerial-aquatic robot, which has passively morphing propellers that unfold in the air and fold underwater, can cross the air-water boundary in 0.35 second. The robot can perform rapid attachment and detachment on challenging surfaces both in air and under water, including curved, rough, incomplete, and biofouling surfaces, and achieve long-duration adhesion with minimal oscillation. We also show that the robot can attach to and hitchhike on moving surfaces. In field tests, we show that the robot can record video in both media and move objects across the air/water boundary in a mountain stream and the ocean. We envision that this study can pave the way for future robots with autonomous biological detection, monitoring, and tracking capabilities in a wide variety of aerial-aquatic environments.
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Ford, David A., Shenan Grossberg, Gianmario Rinaldi, Prathyush P. Menon, Matthew R. Palmer, Jozef Skákala, Tim Smyth, Charlotte A. J. Williams, Alvaro Lorenzo Lopez, and Stefano Ciavatta. "A solution for autonomous, adaptive monitoring of coastal ocean ecosystems: Integrating ocean robots and operational forecasts." Frontiers in Marine Science 9 (December 19, 2022). http://dx.doi.org/10.3389/fmars.2022.1067174.
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This study presents a proof-of-concept for a fully automated and adaptive observing system for coastal ocean ecosystems. Such systems present a viable future observational framework for oceanography, reducing the cost and carbon footprint of marine research. An autonomous ocean robot (an ocean glider) was deployed for 11 weeks in the western English Channel and navigated by exchanging information with operational forecasting models. It aimed to track the onset and development of the spring phytoplankton bloom in 2021. A stochastic prediction model combined the real-time glider data with forecasts from an operational numerical model, which in turn assimilated the glider observations and other environmental data, to create high-resolution probabilistic predictions of phytoplankton and its chlorophyll signature. A series of waypoints were calculated at regular time intervals, to navigate the glider to where the phytoplankton bloom was most likely to be found. The glider successfully tracked the spring bloom at unprecedented temporal resolution, and the adaptive sampling strategy was shown to be feasible in an operational context. Assimilating the real-time glider data clearly improved operational biogeochemical forecasts when validated against independent observations at a nearby time series station, with a smaller impact at a more distant neighboring station. Remaining issues to be addressed were identified, for instance relating to quality control of near-real time data, accounting for differences between remote sensing and in situ observations, and extension to larger geographic domains. Based on these, recommendations are made for the development of future smart observing systems.
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Costa, Daniele, Giacomo Palmieri, David Scaradozzi, and Massimo Callegari. "Experimental Validation of a Bio-Inspired Thruster." Journal of Dynamic Systems, Measurement, and Control 143, no. 8 (March 19, 2021). http://dx.doi.org/10.1115/1.4050258.
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Abstract Bio-inspired solutions have been deeply investigated in the last two decades as a source of propulsive improvement for autonomous underwater vehicles. Despite the efforts made to pursue the substantial potential payoffs of marine animals' locomotion, the performance of biological swimmers is still far to reach. The possibility to design a machine capable of propelling itself like a marine animal strongly depends on the understanding of the mechanics principles underlying biological swimming. Therefore, the adoption of advanced simulation and measurement techniques is fundamental to investigate the fluid–structure interaction phenomena of aquatic animals' locomotion. Among those, computational fluid dynamics represents an invaluable tool to assess the propulsive loads due to swimming. However, the numerical predictions must be validated before they can be applied to the design of a bio-inspired robot. To this end, this paper presents the experimental setup devised to validate the fluid dynamics analysis performed on an oscillating foil. The numerical predictions led to the design of a strain gages-based sensor, which exploits the deflection and twisting of the foil shaft to indirectly measure the propulsive loads and obtain a complete dynamic characterization of the oscillating foil. The results obtained from the experiments showed a good agreement between the numerical predictions and the measured loads; the test equipment also allowed to investigate the potential benefits of a slender fish-like body placed before the spinning fin. Therefore, in future work, the system will be employed to validate the analysis performed on more sophisticated modes of locomotion.
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Castaño, Maria L., and Xiaobo Tan. "Model Predictive Control-Based Path-Following for Tail-Actuated Robotic Fish." Journal of Dynamic Systems, Measurement, and Control 141, no. 7 (April 9, 2019). http://dx.doi.org/10.1115/1.4043152.
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There has been an increasing interest in the use of autonomous underwater robots to monitor freshwater and marine environments. In particular, robots that propel and maneuver themselves like fish, often known as robotic fish, have emerged as mobile sensing platforms for aquatic environments. Highly nonlinear and often under-actuated dynamics of robotic fish present significant challenges in control of these robots. In this work, we propose a nonlinear model predictive control (NMPC) approach to path-following of a tail-actuated robotic fish that accommodates the nonlinear dynamics and actuation constraints while minimizing the control effort. Considering the cyclic nature of tail actuation, the control design is based on an averaged dynamic model, where the hydrodynamic force generated by tail beating is captured using Lighthill's large-amplitude elongated-body theory. A computationally efficient approach is developed to identify the model parameters based on the measured swimming and turning data for the robot. With the tail beat frequency fixed, the bias and amplitude of the tail oscillation are treated as physical variables to be manipulated, which are related to the control inputs via a nonlinear map. A control projection method is introduced to accommodate the sector-shaped constraints of the control inputs while minimizing the optimization complexity in solving the NMPC problem. Both simulation and experimental results support the efficacy of the proposed approach. In particular, the advantages of the control projection method are shown via comparison with alternative approaches.