Academic literature on the topic 'Autonomous Underwater Vehicles, Underwater robotics, Mobile robots navigation'

Purpose – The purpose of this article is to illustrate how sensors impart perceptive capabilities to robots. This is the second part of a two-part article. This second part considers positional awareness and sensing in the external environment, notably but not exclusively by autonomous, mobile robots. Design/methodology/approach – Following a short introduction, this article first discusses positional sensing and navigation by mobile robots, including self-driving cars, automated guided vehicles, unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs). It then considers sensing with UAVs and AUVs, and finally discusses robots for hazard detection. Brief concluding comments are drawn. Findings – This shows that sensors based on a multitude of techniques confer navigational capabilities to mobile robots, including LIDARs, radar, sonar, imaging and inertial sensing devices. UAVs, AUVs and mobile terrestrial robots can be equipped with all manner of sensors to create detailed terrestrial and underwater maps, monitor air and water quality, locate pollution and detect hazards. While existing sensors are used widely, many new devices are now being developed to meet specific requirements and to comply with size, weight and cost restraints. Originality/value – The use of mobile robots is growing rapidly, and this article provides a timely account of how sensors confer them with positional awareness and allow them to act as mobile sensing platforms.

An interesting current research field related to autonomous robots is mobile manipulation performed by cooperating robots (in terrestrial, aerial and underwater environments). Focusing on the underwater scenario, cooperative manipulation of Intervention-Autonomous Underwater Vehicles (I-AUVs) is a complex and difficult application compared with the terrestrial or aerial ones because of many technical issues, such as underwater localization and limited communication. A decentralized approach for cooperative mobile manipulation of I-AUVs based on Artificial Neural Networks (ANNs) is proposed in this article. This strategy exploits the potential field method; a multi-layer control structure is developed to manage the coordination of the swarm, the guidance and navigation of I-AUVs and the manipulation task. In the article, this new strategy has been implemented in the simulation environment, simulating the transportation of an object. This object is moved along a desired trajectory in an unknown environment and it is transported by four underwater mobile robots, each one provided with a seven-degrees-of-freedom robotic arm. The simulation results are optimized thanks to the ANNs used for the potentials tuning.

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.

"This article is devoted to a state of the art on mobile multi-robot systems in the underwater domain. It start with the history of underwater robots as well as their applications in various fields, then focus on underwater vehicles different categories charactheristics and properties, their appearance and their applications. Subsequently, the design of an autonomous underwater inspection vehicle is presented together with its functions, movements and control system."

Purpose This paper aims to provide details of recent developments in robots aimed at applications in the offshore oil and gas industries. Design/methodology/approach Following a short introduction, this first discusses developments to remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs). It then describes the Total-sponsored Autonomous Robot for Gas and Oil Sites (ARGOS) robot challenge. This is followed by a discussion of the Offshore Robotics for Certification of Assets (ORCA) programme. Finally, brief concluding comments are drawn. Findings Subsea residency and other techniques are being developed that will enhance the availability and capabilities of AUVs and ROVs and reduce their operating costs. Mobile robots that can operate in harsh topside rig environments to monitor and detect hazards arose from ARGOS and are being developed further prior to commercialisation. Bringing together academics and users, the collaborative ORCA programme is making significant progress in the development of aerial, topside and underwater robotic and sensing technologies for rig asset inspection and maintenance. Originality/value This paper identifies and describes key development activities that will stimulate the use of robots by the offshore industries.

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>

Autonomous navigation requires both a precise and robust mapping and localization solution. In this context, Simultaneous Localization and Mapping (SLAM) is a very well-suited solution. SLAM is used for many applications including mobile robotics, self-driving cars, unmanned aerial vehicles, or autonomous underwater vehicles. In these domains, both visual and visual-IMU SLAM are well studied, and improvements are regularly proposed in the literature. However, LiDAR-SLAM techniques seem to be relatively the same as ten or twenty years ago. Moreover, few research works focus on vision-LiDAR approaches, whereas such a fusion would have many advantages. Indeed, hybridized solutions offer improvements in the performance of SLAM, especially with respect to aggressive motion, lack of light, or lack of visual features. This study provides a comprehensive survey on visual-LiDAR SLAM. After a summary of the basic idea of SLAM and its implementation, we give a complete review of the state-of-the-art of SLAM research, focusing on solutions using vision, LiDAR, and a sensor fusion of both modalities.

This paper proposes novel distributed control methods to address coverage and flocking problems in three-dimensional (3D) environments using multiple unmanned aerial vehicles (UAVs). Two classes of coverage problems are considered in this work, namely barrier and sweep problems. Additionally, the approach is also applied to general 3D flocking problems for advanced swarm behavior. The proposed control strategies adopt a region-based control approach based on Voronoi partitions to ensure collision-free self-deployment and coordinated movement of all vehicles within a 3D region. It provides robustness for the multi-vehicle system against vehicles’ failure. It is also computationally-efficient to ensure scalability, and it handles obstacle avoidance on a higher level to avoid conflicts in control with the inter-vehicle collision avoidance objective. The problem formulation is rather general considering mobile robots navigating in 3D spaces, which makes the proposed approach applicable to different UAV types and autonomous underwater vehicles (AUVs). However, implementation details have also been shown considering quadrotor-type UAVs for an example application in precision agriculture. Validation of the proposed methods have been performed using several simulations considering different simulation platforms such as MATLAB and Gazebo. Software-in-the-loop simulations were carried out to asses the real-time computational performance of the methods showing the actual implementation with quadrotors using C++ and the Robot Operating System (ROS) framework. Good results were obtained validating the performance of the suggested methods for coverage and flocking scenarios in 3D using systems with different sizes up to 100 vehicles. Some scenarios considering obstacle avoidance and robustness against vehicles’ failure were also used.

Purpose The following paper details a “Q&A interview” conducted by Joanne Pransky, Associate Editor of Industrial Robot Journal, to impart the combined technological, business and personal experience of a prominent, robotic industry engineer-turned successful business leader, regarding the commercialization and challenges of bringing technological inventions to the market while overseeing a company. The paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr William “Red” Whittaker, Fredkin Research Professor of Robotics, Robotics Institute, Carnegie Mellon University (CMU); CEO of Astrobotic Technology; and President of Workhorse Technologies. Dr Whittaker provides answers to questions regarding the pioneering experiences of some of his technological wonders in land, sea, air, underwater, underground and space. Findings As a child, Dr Whittaker built things and made them work and dreamed about space and robots. He has since then turned his dreams, and those of the world, into realities. Dr Whittaker’s formal education includes a BS degree in civil engineering from Princeton and MS and PhD degrees in civil engineering from CMU. In response to designing a robot to cleanup radioactive material at the Three Mile Island nuclear plant, Dr Whittaker established the Field Robotics Center (FRC) in 1983. He is also the founder of the National Robotics Engineering Center, an operating unit within CMU’s Robotics Institute (RI), the world’s largest robotics research and development organization. Dr Whittaker has developed more than 60 robots, breaking new ground in autonomous vehicles, field robotics, space exploration, mining and agriculture. Dr Whittaker’s research addresses computer architectures for robots, modeling and planning for non-repetitive tasks, complex problems of objective sensing in random and dynamic environments and integration of complete robot systems. His current focus is Astrobotic Technology, a CMU spin-off firm that is developing space robotics technology to support planetary missions. Dr Whittaker is competing for the US$20m Google Lunar XPRIZE for privately landing a robot on the Moon. Originality/value Dr Whittaker coined the term “field robotics” to describe his research that centers on robots in unconstrained, uncontrived settings, typically outdoors and in the full range of operational and environmental conditions: robotics in the “natural” world. The Field Robotics Center has been one of the most successful initiatives within the entire robotics industry. As the Father of Field Robotics, Dr Whittaker has pioneered locomotion technologies, navigation and route-planning methods and advanced sensing systems. He has directed over US$100m worth of research programs and spearheaded several world-class robotic explorations and operations with significant outreach, education and technology commercializations. His ground vehicles have driven thousands of autonomous miles. Dr Whittaker won DARPA’s US$2m Urban Challenge. His Humvees finished second and third in the 2005 DARPA’s Grand race Challenge desert race. Other robot projects have included: Dante II, a walking robot that explored an active volcano; Nomad, which searched for meteorites in Antarctica; and Tugbot, which surveyed a 1,800-acre area of Nevada for buried hazards. Dr Whittaker is a member of the National Academy of Engineering. He is a fellow of the American Association for Artificial Intelligence and served on the National Academy of Sciences Space Studies Board. Dr Whittaker received the Alan Newell Medal for Research Excellence. He received Carnegie Mellon’s Teare Award for Teaching Excellence. He received the Joseph Engelberger Award for Outstanding Achievement in Robotics, the Advancement of Artificial Intelligence’s inaugural Feigenbaum Prize for his contributions to machine intelligence, the Institute of Electrical and Electronics Engineers Simon Ramo Medal, the American Society of Civil Engineers Columbia Medal, the Antarctic Service Medal and the American Spirit Honor Medal. Science Digest named Dr Whittaker one of the top 100 US innovators for his work in robotics. He has been recognized by Aviation Week & Space Technology and Design News magazines for outstanding achievement. Fortune named him a “Hero of US Manufacturing”. Dr Whittaker has advised 26 PhD students, has 16 patents and has authored over 200 publications. Dr Whittaker’s vision is to drive nanobiologics technology to fulfillment and create nanorobotic agents for enterprise on Earth and beyond (Figure 1).

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.

Aquesta tesi proposa l'ús d'un seguit de tècniques pel control a alt nivell d'un robot autònom i també per l'aprenentatge automàtic de comportaments. L'objectiu principal de la tesis fou el de dotar d'intel·ligència als robots autònoms que han d'acomplir unes missions determinades en entorns desconeguts i no estructurats. Una de les premisses tingudes en compte en tots els passos d'aquesta tesis va ser la selecció d'aquelles tècniques que poguessin ésser aplicades en temps real, i demostrar-ne el seu funcionament amb experiments reals. El camp d'aplicació de tots els experiments es la robòtica submarina.
En una primera part, la tesis es centra en el disseny d'una arquitectura de control que ha de permetre l'assoliment d'una missió prèviament definida. En particular, la tesis proposa l'ús de les arquitectures de control basades en comportaments per a l'assoliment de cada una de les tasques que composen la totalitat de la missió. Una arquitectura d'aquest tipus està formada per un conjunt independent de comportaments, els quals representen diferents intencions del robot (ex.: "anar a una posició", "evitar obstacles",...). Es presenta una recerca bibliogràfica sobre aquest camp i alhora es mostren els resultats d'aplicar quatre de les arquitectures basades en comportaments més representatives a una tasca concreta. De l'anàlisi dels resultats se'n deriva que un dels factors que més influeixen en el rendiment d'aquestes arquitectures, és la metodologia emprada per coordinar les respostes dels comportaments. Per una banda, la coordinació competitiva és aquella en que només un dels comportaments controla el robot. Per altra banda, en la coordinació cooperativa el control del robot és realitza a partir d'una fusió de totes les respostes dels comportaments actius. La tesis, proposa un esquema híbrid d'arquitectura capaç de beneficiar-se dels principals avantatges d'ambdues metodologies.
En una segona part, la tesis proposa la utilització de l'aprenentatge per reforç per aprendre l'estructura interna dels comportaments. Aquest tipus d'aprenentatge és adequat per entorns desconeguts i el procés d'aprenentatge es realitza al mateix temps que el robot està explorant l'entorn. La tesis presenta també un estat de l'art d'aquest camp, en el que es detallen els principals problemes que apareixen en utilitzar els algoritmes d'aprenentatge per reforç en aplicacions reals, com la robòtica. El problema de la generalització és un dels que més influeix i consisteix en permetre l'ús de variables continues sense augmentar substancialment el temps de convergència. Després de descriure breument les principals metodologies per generalitzar, la tesis proposa l'ús d'una xarxa neural combinada amb l'algoritme d'aprenentatge per reforç Q_learning. Aquesta combinació proporciona una gran capacitat de generalització i una molt bona disposició per aprendre en tasques de robòtica amb exigències de temps real. No obstant, les xarxes neurals són aproximadors de funcions no-locals, el que significa que en treballar amb un conjunt de dades no homogeni es produeix una interferència: aprendre en un subconjunt de l'espai significa desaprendre en la resta de l'espai. El problema de la interferència afecta de manera directa en robòtica, ja que l'exploració de l'espai es realitza sempre localment. L'algoritme proposat en la tesi té en compte aquest problema i manté una base de dades representativa de totes les zones explorades. Així doncs, totes les mostres de la base de dades s'utilitzen per actualitzar la xarxa neural, i per tant, l'aprenentatge és homogeni.
Finalment, la tesi presenta els resultats obtinguts amb la arquitectura de control basada en comportaments i l'algoritme d'aprenentatge per reforç. Els experiments es realitzen amb el robot URIS, desenvolupat a la Universitat de Girona, i el comportament après és el seguiment d'un objecte mitjançant visió per computador. La tesi detalla tots els dispositius desenvolupats pels experiments així com les característiques del propi robot submarí. Els resultats obtinguts demostren la idoneïtat de les propostes en permetre l'aprenentatge del comportament en temps real. En un segon apartat de resultats es demostra la capacitat de generalització de l'algoritme d'aprenentatge mitjançant el "benchmark" del "cotxe i la muntanya". Els resultats obtinguts en aquest problema milloren els resultats d'altres metodologies, demostrant la millor capacitat de generalització de les xarxes neurals.

As of today, autonomous underwater navigation can still be considered a challenging task; the determination of self-localization techniques for autonomous underwater vehicles is an open research topic, and many efforts are undertaken by researchers and companies to improve existing algorithms or to develop new solutions to increase the level of accuracy achievable with current vehicle technologies. In this framework, the research activity carried out during the Ph.D. period concentrated on the study of pose estimation algorithms for mobile robots, with special focus given to the underwater field. Starting from current solutions identified within the state of the art, the work was conducted in parallel on the topics of attitude and position estimation. A nonlinear attitude observer employing inertial and magnetic field data and suitable for use in the underwater field was derived; the estimated attitude constitutes an input for an UKF-based position estimator exploiting position, depth, and velocity measurements. Furthermore, the possibility of including the real-time estimation of sea currents within the developed estimators, relying only on already available measurements, was investigated. The performance of the resulting solutions was evaluated by means of simulations exploiting real navigation data or during suitable experimental test campaigns which allowed to assess their effectiveness in a real-world scenario; the obtained results were satisfying, indicating that the derived algorithms may constitute a valid alternative to existing pose estimation strategies commonly adopted in the underwater field.

Driven by the rising demand for underwater operations concerning dam structure monitoring, Hydropower Plant (HPP), reservoir, and lake ecosystem inspection, and mining and oil exploration, underwater robotics applications are increasing rapidly. The increase in exploration, prospecting, monitoring, and security in lakes, rivers, and the sea in commercial applications has led large companies and research centers to invest underwater vehicle development. The purpose of this work is to present the design of an Autonomous Underwater Vehicle (AUV), focusing efforts on dimensioning structural elements and machinery and elaborating the sensory part, which includes navigation sensors and environmental conditions sensors. The integration of these sensors in an intelligent platform provides a satisfactory control of the vehicle, allowing the movement of the submarine on the three spatial axes. Because of the satisfactory fast response of the sensors, one can determine the acceleration and inclination as well as the attitude in relation to the trajectory instantaneously taken. This vehicle will be able to monitor the physical integrity of dams, making acquisition and storage of environmental parameters such as temperature, dissolved oxygen, pH, and conductivity, as well as document images of the biota from reservoir lake HPPs, with minimal cost, high availability, and low dependence on a skilled workforce to operate it.