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1
Patel, Jay, and Mae Seto. "CDMA-based multi-domain communications network for marine robots." Journal of the Acoustical Society of America 148, no. 4 (October 2020): 2510. http://dx.doi.org/10.1121/1.5146976.
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Pradhan, Buddhadeb, Nirmal Baran Hui, Diptendu Sinha Roy, Gautam Srivastava, and Jerry Chun-Wei Lin. "Game-Theoretic Strategic Coordination and Navigation of Multiple Wheeled Robots." ACM Transactions on Internet Technology 21, no. 4 (July 16, 2021): 1–15. http://dx.doi.org/10.1145/3450521.
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Multiple robots negotiating in a dynamic workspace may lead to collisions. To avoid such issues, multi-robot navigation and coordination becomes necessary but is computationally very challenging, particularly when there are many robots. This article addresses the problem of multi-robot navigation where individual robots require coordination. Although a few such attempts for modeling multi-robot coordination and navigation have been studied, this work proposes a game-theoretic coordination strategy, also referred to as strategic coordination. We make use of a genetic algorithm tuned fuzzy logic–based motion planner. The proposed strategic coordination strategy has been pitted against a basic potential field-based motion planner, also referred to as the heuristic method, for performance comparison. Results are compared through computer simulation with 8 to 17 robots at different rounds. From the obtained results, it was observed that the proposed coordination scheme’s efficacy is strong for a larger number of robots. In addition, the proposed strategic coordination scheme with the genetic-fuzzy-based motion planner was found to outperform other combinations as far as the quality of solutions and time to reach the goal positions. The computational complexity of different methods has also been compared and presented.
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Matos, Diogo, Pedro Costa, José Lima, and Paulo Costa. "Multi AGV Coordination Tolerant to Communication Failures." Robotics 10, no. 2 (March 27, 2021): 55. http://dx.doi.org/10.3390/robotics10020055.
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Most path planning algorithms used presently in multi-robot systems are based on offline planning. The Timed Enhanced A* (TEA*) algorithm gives the possibility of planning in real time, rather than planning in advance, by using a temporal estimation of the robot’s positions at any given time. In this article, the implementation of a control system for multi-robot applications that operate in environments where communication faults can occur and where entire sections of the environment may not have any connection to the communication network will be presented. This system uses the TEA* to plan multiple robot paths and a supervision system to control communications. The supervision system supervises the communication with the robots and checks whether the robot’s movements are synchronized. The implemented system allowed the creation and execution of paths for the robots that were both safe and kept the temporal efficiency of the TEA* algorithm. Using the Simtwo2020 simulation software, capable of simulating movement dynamics and the Lazarus development environment, it was possible to simulate the execution of several different missions by the implemented system and analyze their results.
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Xiao, Qi, Yunchuan Qin, Cheng Xu, and Kenli Li. "Secure Key Establishment Mechanism for Smart Sensing System Based Robots Network." Sensors 20, no. 7 (April 1, 2020): 1970. http://dx.doi.org/10.3390/s20071970.
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The smart robot is playing an increasingly important role in the social economy, and multi-robot systems will be an important development in robotics. With smart sensing systems, the communications between sensors, actuators, and edge computing systems and robots are prone to be attacked due to the highly dynamic and distributed environment. Since smart robots are often distributed in open environments, as well as due to their limited hardware resources and security protection capabilities, the security requirements of their keys cannot be met with traditional key distribution algorithms. In this paper, we propose a new mechanism of key establishment based on high-order polynomials to ensure the safe key generation and key distribution. Experiments show that the key establishment mechanism proposed in this paper guarantees the security of keys; its storage cost and communication cost are smaller than state-of-the-art mechanisms; and it allows robot components to join and leave the network dynamically, which is more suitable for multi-robot systems.
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Miura, Ryu, Toshinori Kagawa, Fumie Ono, Lin Shan, Takashi Matsuda, and Fumihide Kojima. "Propagation Measurements of Multi-Hop Command and Telemetry Communications System in the 169 MHz Band for Drones." Journal of Robotics and Mechatronics 33, no. 2 (April 20, 2021): 363–70. http://dx.doi.org/10.20965/jrm.2021.p0363.
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The 169 MHz band was assigned for unmanned vehicles such as robots and drones by the Ministry of Internal Affairs and Communications (MIC) in 2016 in Japan, according to the reallocation of the TV band from VHF (analog) to UHF (digital). This band is expected to have a long range and good diffraction characteristics during drone operation over a long range with obstacles such as buildings, trees, or terrains. In this paper, we present the prototype system and its propagation experiments of command and telemetry communications at 169 MHz, which is added to a multi-hop communication unit in the 920 MHz band developed previously to enhance the beyond line-of-sight (BLOS) operation performance for drones.
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Wang, Hui, Jieren Cheng, Yichen Xu, Sirui Ni, Zaijia Yang, and Jiangpeng Li. "A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms." Security and Communication Networks 2023 (June 1, 2023): 1–16. http://dx.doi.org/10.1155/2023/7723164.
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Robot grasping is one of the most important abilities of modern intelligent robots, especially industrial robots. However, most of the existing robot arm’s grasp detection work is highly dependent on their edge computing ability, and the safety problems in the process of grasp detection are not considered enough. In this paper, we propose a new robotic arm grasping detection model with an edge-cloud collaboration method. With the scheme of multi-object multi-grasp, our model improves the mission success ratio of grasping. The model can not only complete the compression of full-resolution images but also achieve image compression at a limited bit rate. The image compression ratio reaches 2.03%; the structural difference value is higher than 0.91, and our average detection speed reaches 13.62 fps. Furthermore, we have packaged our model as a functional package of the ROS operating system, which can be easily used in actual robotic arm operations. Our solution can be fully applied to other work of robots to promote the development of the field of robotics.
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Hamed, Oussama, Mohamed Hamlich, and Mohamed Ennaji. "Hunting strategy for multi-robot based on wolf swarm algorithm and artificial potential field." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (January 1, 2022): 159. http://dx.doi.org/10.11591/ijeecs.v25.i1.pp159-171.
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The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.
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Bae, Sanghyeon, Sunghyeon Joo, Junhyeon Choi, Jungwon Pyo, Hyunjin Park, and Taeyong Kuc. "Semantic Knowledge-Based Hierarchical Planning Approach for Multi-Robot Systems." Electronics 12, no. 9 (May 6, 2023): 2131. http://dx.doi.org/10.3390/electronics12092131.
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Multi-robot systems have been used in many fields by utilizing parallel working robots to perform missions by allocating tasks and cooperating. For task planning, multi-robot systems need to solve complex problems that simultaneously consider the movement of the robots and the influence of each robot. For this purpose, researchers have proposed various methods for modeling and planning multi-robot missions. In particular, some approaches have been presented for high-level task planning by introducing semantic knowledge, such as relationships and domain rules, for environmental factors. This paper proposes a semantic knowledge-based hierarchical planning approach for multi-robot systems. We extend the semantic knowledge by considering the influence and interaction between environmental elements in multi-robot systems. Relationship knowledge represents the space occupancy of each environmental element and the possession of objects. Additionally, the knowledge property is defined to express the hierarchical information of each space. Based on the suggested semantic knowledge, the task planner utilizes spatial hierarchy knowledge to group the robots and generate optimal task plans for each group. With this approach, our method efficiently plans complex missions while handling overlap and deadlock problems among the robots. The experiments verified the feasibility of the suggested semantic knowledge and demonstrated that the task planner could reduce the planning time in simulation environments.
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Gharbi, Atef, and Saleh M. Altowaijri. "A Multi-Robot-Based Architecture and a Trust Model for Intelligent Fault Management and Control Systems." Electronics 12, no. 17 (August 31, 2023): 3679. http://dx.doi.org/10.3390/electronics12173679.
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One of the most important challenges in robotics is the development of a Multi-Robot-based control system in which the robot can make intelligent decisions in a changing environment. This paper proposes a robot-based control approach for dynamically managing robots in such a widely distributed production system. A Multi-Robot-based control system architecture is presented, and its main features are described. Such architecture facilitates the reconfiguration (either self-reconfiguration ensured by the robot itself or distributed reconfiguration executed by the Multi-Robot-based system). The distributed reconfiguration is facilitated through building a trust model that is based on learning from past interactions between intelligent robots. The Multi-Robot-based control system architecture also addresses other specific requirements for production systems, including fault flexibility. Any out-of-control fault occurring in a production system results in the loss of production time, resources, and money. In these cases, robot trust is critical for successful job completion, especially when the work can only be accomplished by sharing knowledge and resources among robots. This work introduces research on the construction of trust estimation models that experimentally calculate and evaluate the trustworthiness of robots in a Multi-Robot system where the robot can choose to cooperate and collaborate exclusively with other trustworthy robots. We compare our proposed trust model with other models described in the literature in terms of performance based on four criteria, which are time steps analysis, RMSD evaluation, interaction analysis, and variation of total feedback. The contribution of the paper can be summarized as follows: (i) the Multi-Robot-based Control Architecture; (ii) how the control robot handles faults; and (iii) the trust model.
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Rajesh, M., S. R. Nagaraja, and P. Suja. "Multi – Robot Exploration Supported by Enhanced Localization with Reduction of Localization Error Using Particle Swarm Optimization." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 15, no. 1 (March 29, 2024): 202–15. http://dx.doi.org/10.58346/jowua.2024.i1.014.
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Exploration of an area by a group of robots is an active research field of robotics as multi-robot exploration is applied extensively in several real life scenarios. The major challenges in such exploration are the availability of communication infrastructure as communication plays a key role in the coordination of team of robots for effective coverage of the area under exploration. But in disaster affected scenarios, there will be no existing communication infrastructure available and this makes the exploration ineffective and time consuming. Another challenge is in the localization process each robot is carrying out to update the map as well as for exchange of information with other robots. In this paper, an enhanced Multi-robot exploration strategy is introduced. The base of the exploration strategy is two techniques. The first one being localization of each robot involved in the exploration and this is done with the help of trilateration where three anchors are required which will be setup before the exploration starts. The second part is navigation and avoiding overlapping or missing out sectors while exploring. This is done with help of a navigation policy called frontier cell based approach. Further to this, the exploration strategy is supported with localization error reduction scheme in which the localization error is reduced with the help of Particle Swarm Optimization (PSO). The entire scheme is simulated and exploration time is analyzed for the same environment in different obstacle density and different number of robots to perform exploration. The results show the scheme is better than many existing multi-robot exploration strategies. Precisely, the proposed scheme is able to reduce the localization error to a threshold level of 0.02cm or below which can be considered as novel contribution towards the exploration strategies.
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Zhao, Dewei, Sheng Zhang, Faming Shao, Li Yang, Qiang Liu, Heng Zhang, and Zihan Zhang. "Path Planning for the Rapid Reconfiguration of a Multi-Robot Formation Using an Integrated Algorithm." Electronics 12, no. 16 (August 17, 2023): 3483. http://dx.doi.org/10.3390/electronics12163483.
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Path planning is crucial in the scheduling and motion planning of multiple robots. However, solving multi-robot path-planning problems efficiently and quickly is challenging due to their high complexity and long computational time, especially when dealing with many robots. This paper presents a unified mathematical model and algorithm for the path planning of multiple robots moving from one formation to another in an area with obstacles. The problem was initially simplified by constructing a cost matrix, and then the route planning was achieved by integrating an elite enhanced multi-population genetic algorithm and an ant colony algorithm. The performance of the proposed planning method was verified through numerical simulations in various scenarios. The findings indicate that this method exhibits high computational efficiency and yields a minimal overall path distance when addressing the path-planning problem of a multi-robot formation reconstruction. As a result, it holds promising potential for the path-planning problem of a multi-robot formation reconstruction.
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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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Lv, Zhihan, Liang Qiao, and Qingjun Wang. "Cognitive Robotics on 5G Networks." ACM Transactions on Internet Technology 21, no. 4 (July 16, 2021): 1–18. http://dx.doi.org/10.1145/3414842.
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Emotional cognitive ability is a key technical indicator to measure the friendliness of interaction. Therefore, this research aims to explore robots with human emotion cognitively. By discussing the prospects of 5G technology and cognitive robots, the main direction of the study is cognitive robots. For the emotional cognitive robots, the analysis logic similar to humans is difficult to imitate; the information processing levels of robots are divided into three levels in this study: cognitive algorithm, feature extraction, and information collection by comparing human information processing levels. In addition, a multi-scale rectangular direction gradient histogram is used for facial expression recognition, and robust principal component analysis algorithm is used for facial expression recognition. In the pictures where humans intuitively feel smiles in sad emotions, the proportion of emotions obtained by the method in this study are as follows: calmness accounted for 0%, sadness accounted for 15.78%, fear accounted for 0%, happiness accounted for 76.53%, disgust accounted for 7.69%, anger accounted for 0%, and astonishment accounted for 0%. In the recognition of micro-expressions, humans intuitively feel negative emotions such as surprise and fear, and the proportion of emotions obtained by the method adopted in this study are as follows: calmness accounted for 32.34%, sadness accounted for 34.07%, fear accounted for 6.79%, happiness accounted for 0%, disgust accounted for 0%, anger accounted for 13.91%, and astonishment accounted for 15.89%. Therefore, the algorithm explored in this study can realize accuracy in cognition of emotions. From the preceding research results, it can be seen that the research method in this study can intuitively reflect the proportion of human expressions, and the recognition methods based on facial expressions and micro-expressions have good recognition effects, which is in line with human intuitive experience.
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Hudson, Nicolas, Fletcher Talbot, Mark Cox, Jason Williams, Thomas Hines, Alex Pitt, Brett Wood, et al. "Heterogeneous Ground and Air Platforms, Homogeneous Sensing: Team CSIRO Data61’s Approach to the DARPA Subterranean Challenge." Field Robotics 2, no. 1 (March 10, 2022): 595–636. http://dx.doi.org/10.55417/fr.2022021.
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Heterogeneous teams of robots, leveraging a balance between autonomy and human interaction, bring powerful capabilities to the problem of exploring dangerous, unstructured subterranean environments. Here we describe the solution developed by Team CSIRO Data61, consisting of CSIRO, Emesent, and Georgia Tech, during the DARPA Subterranean Challenge. These presented systems were fielded in the Tunnel Circuit in August 2019, the Urban Circuit in February 2020, and in our own Cave event, conducted in September 2020. A unique capability of the fielded team is the homogeneous sensing of the platforms utilized, which is used to obtain a decentralized multi-agent SLAM solution on each platform (both ground agents and UAVs) using peer-to-peer communications. This approach enabled a shift in focus from constructing a pervasive communications network to relying on multi-agent autonomy, motivated by experiences in early circuit events. These experiences also showed the surprising capability of rugged tracked platforms for challenging terrain, which in turn led to the heterogeneous team structure based on a BIA5 OzBot Titan ground robot and an Emesent Hovermap UAV, supplemented by smaller tracked or legged ground robots. The ground agents use a common CatPack perception module, which allowed reuse of the perception and autonomy stack across all ground agents with minimal adaptation.
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Li, Wenfeng, and Weiming Shen. "Swarm behavior control of mobile multi-robots with wireless sensor networks." Journal of Network and Computer Applications 34, no. 4 (July 2011): 1398–407. http://dx.doi.org/10.1016/j.jnca.2011.03.023.
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Quinton, Félix, Christophe Grand, and Charles Lesire. "Enchères pour le Maintien des Communications lors de l’Allocation de Tâches pour des Missions Multi-robots." Revue Ouverte d'Intelligence Artificielle 4, no. 2 (July 4, 2023): 97–122. http://dx.doi.org/10.5802/roia.58.
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Zhong, Junpei, Chaofan Ling, Angelo Cangelosi, Ahmad Lotfi, and Xiaofeng Liu. "On the Gap between Domestic Robotic Applications and Computational Intelligence." Electronics 10, no. 7 (March 27, 2021): 793. http://dx.doi.org/10.3390/electronics10070793.
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Aspired to build intelligent agents that can assist humans in daily life, researchers and engineers, both from academia and industry, have kept advancing the state-of-the-art in domestic robotics. With the rapid advancement of both hardware (e.g., high performance computing, smaller and cheaper sensors) and software (e.g., deep learning techniques and computational intelligence technologies), robotic products have become available to ordinary household users. For instance, domestic robots have assisted humans in various daily life scenarios to provide: (1) physical assistance such as floor vacuuming; (2) social assistance such as chatting; and (3) education and cognitive assistance such as offering partnerships. Crucial to the success of domestic robots is their ability to understand and carry out designated tasks from human users via natural and intuitive human-like interactions, because ordinary users usually have no expertise in robotics. To investigate whether and to what extent existing domestic robots can participate in intuitive and natural interactions, we survey existing domestic robots in terms of their interaction ability, and discuss the state-of-the-art research on multi-modal human–machine interaction from various domains, including natural language processing and multi-modal dialogue systems. We relate domestic robot application scenarios with state-of-the-art computational techniques of human–machine interaction, and discuss promising future directions towards building more reliable, capable and human-like domestic robots.
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Li, Bin. "Optimization of Multi-Intelligent Robot Control System Based on Wireless Communication Network." Wireless Communications and Mobile Computing 2021 (July 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/6457317.
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The robot is a very complex multi-input multioutput nonlinear system. It has time-varying, strong coupling, and nonlinear dynamic characteristics, and its control is very complicated. Due to the inaccuracy of measurement and modeling, coupled with changes in the load and the influence of external disturbances, it is actually impossible to obtain an accurate and complete dynamic model of the robot. We must face the existence of various uncertain factors of the robot. This paper analyzes the real-time communication protocol in the wireless network control system and confirms that the main way to improve the real-time performance of the wireless network control system is to implement the real-time media access control (MAC) protocol. This paper studies robots from the perspective of control and mainly discusses how to use artificial immune algorithms to design robust nonlinear proportion integral derivative (PID) controllers. A nonlinear PID controller is used to replace the classic PID controller. The nonlinear link can be adjusted with the change of the error, so as to achieve the purpose of improving the adaptability and robustness to obtain satisfactory tracking performance. We carried out selective compliance assembly robot arm (SCARA) robot remote control experiment, dual robot following experiment, SCARA and ABB robot collisionless motion planning experiment, and multirobot intelligent collaborative assembly experiment. The experimental results show that the C/S mode remote control system has good practicability and can complete remote tasks; the P2P communication system has good information transmission effects and can realize real-time information sharing between robots; the collision-free motion planning algorithm enables the dual robots to complete obstacle avoidance tasks well in complex operating environments; the functional modules of the system can closely cooperate to complete the tasks in coordination, and the multirobot system has a certain degree of intelligence.
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Afdila, Ryandika, Fahmi Fahmi, and Arman Sani. "Distributed formation control for groups of mobile robots using consensus algorithm." Bulletin of Electrical Engineering and Informatics 12, no. 4 (August 1, 2023): 2095–104. http://dx.doi.org/10.11591/beei.v12i4.3869.
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The increasing implementation of autonomous robots in industries and daily life demands the development of a robust control algorithm that enables robots to perform their tasks successfully. Critical tasks such as rescue missions and area exploration require robots to work cooperatively in a formation to accomplish the tasks quickly. Moreover, the existence of obstacles in the environment requires all the robots' to rapidly process environmental changes to maintain the formation pattern. Thus, this paper introduces a distributed robot formation control system using the consensus algorithm that enables a group of robots to establish and maintain formations using only the local information of the robots. Furthermore, an obstacle avoidance algorithm based on the distance and angle between robots and obstacles is introduced to ensure safe navigation for the group of robots. The algorithm's effectiveness is demonstrated by a multi-robot system with randomly generated starting positions and velocities, where it is shown that the robots can agree on the control variables and establish the required formation while also avoiding obstacles in the environment.
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Afdila, Ryandika, Fahmi Fahmi, and Arman Sani. "Distributed formation control for groups of mobile robots using consensus algorithm." Bulletin of Electrical Engineering and Informatics 12, no. 4 (August 1, 2023): 2095–104. http://dx.doi.org/10.11591/eei.v12i4.3869.
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The increasing implementation of autonomous robots in industries and daily life demands the development of a robust control algorithm that enables robots to perform their tasks successfully. Critical tasks such as rescue missions and area exploration require robots to work cooperatively in a formation to accomplish the tasks quickly. Moreover, the existence of obstacles in the environment requires all the robots' to rapidly process environmental changes to maintain the formation pattern. Thus, this paper introduces a distributed robot formation control system using the consensus algorithm that enables a group of robots to establish and maintain formations using only the local information of the robots. Furthermore, an obstacle avoidance algorithm based on the distance and angle between robots and obstacles is introduced to ensure safe navigation for the group of robots. The algorithm's effectiveness is demonstrated by a multi-robot system with randomly generated starting positions and velocities, where it is shown that the robots can agree on the control variables and establish the required formation while also avoiding obstacles in the environment.
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Zitouni, Farouq, Ramdane Maamri, and Saad Harous. "Towards a formal analysis of the multi-robot task allocation problem using set theory." Bulletin of Electrical Engineering and Informatics 10, no. 2 (April 1, 2021): 1092–104. http://dx.doi.org/10.11591/eei.v10i2.2395.
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Nowadays, the multi-robot task allocation problem is one of the most challenging problems in multi-robot systems. It concerns the optimal assignment of a set of tasks to several robots while optimizing a given criterion subject to some constraints. This problem is very complex, particularly when handling large groups of robots and tasks. We propose a formal analysis of the task allocation problem in a multi-robot system, based on set theory concepts. We believe that this analysis will help researchers understand the nature of the problem, its time complexity, and consequently develop efficient solutions. Also, we used that formal analysis to formulate two well-known taxonomies of multi-robot task allocation problems. Finally, a generic solving scheme of multi-robot task allocation problems is proposed and illustrated on assigning papers to reviewers within a journal.
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Xu, Qianli, Jamie S. L. Ng, Odelia Y. Tan, and Zhiyong Huang. "Needs and attitudes of Singaporeans towards home service robots: a multi-generational perspective." Universal Access in the Information Society 14, no. 4 (April 22, 2014): 477–86. http://dx.doi.org/10.1007/s10209-014-0355-2.
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Wang, Xiaohui, Xi Ma, and Zhaowei Li. "Research on SLAM and Path Planning Method of Inspection Robot in Complex Scenarios." Electronics 12, no. 10 (May 10, 2023): 2178. http://dx.doi.org/10.3390/electronics12102178.
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Factory safety inspections are crucial for maintaining a secure production environment. Currently, inspections are predominantly performed manually on a regular basis, leading to low efficiency and a high workload. Utilizing inspection robots can significantly improve the reliability and efficiency of these tasks. The development of robot localization and path planning technologies ensures that factory inspection robots can autonomously complete their missions in complex environments. In response to the application requirements of factory inspections, this paper investigates mapping, localization, and path planning methods for robots. Considering the limitations of cameras and laser sensors due to their inherent characteristics, as well as their varying applicability in different environments, this paper proposes SLAM application systems based on multi-line laser radar and visual perception for diverse scenarios. To address the issue of low efficiency in inspection tasks, a hybrid path planning algorithm that combines the A-star algorithm and time elastic band method is introduced. This approach effectively resolves the problem of path planning becoming trapped in local optima in complex environments, subsequently enhancing the inspection efficiency of robots. Experimental results demonstrate that the designed SLAM and path planning methods can satisfy the inspection requirements of robots in complex scenarios, exhibiting excellent reliability and stability.
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Gutiérrez Cejudo, Jorge, Francisco Enguix Andrés, Marin Lujak, Carlos Carrascosa Casamayor, Alberto Fernandez, and Luís Hernández López. "Towards Agrirobot Digital Twins: Agri-RO5—A Multi-Agent Architecture for Dynamic Fleet Simulation." Electronics 13, no. 1 (December 23, 2023): 80. http://dx.doi.org/10.3390/electronics13010080.
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In this paper, we propose a multi-agent-based architecture for a Unity3D simulation of dynamic agrirobot-fleet-coordination methods. The architecture is based on a Robot Operating System (ROS) and Agrobots-SIM package that extends the existing package Patrolling SIM made for multi-robot patrolling. The Agrobots-SIM package accommodates dynamic multi-robot task allocation and vehicle routing considering limited robot battery autonomy. Moreover, it accommodates the dynamic assignment of implements to robots for the execution of heterogeneous tasks. The system coordinates task assignment and vehicle routing in real time and responds to unforeseen contingencies during simulation considering dynamic updates of the data related to the environment, tasks, implements, and robots. Except for the ROS and Agrobots-SIM package, other crucial components of the architecture include SPADE3 middleware for developing and executing multi-agent decision making and the FIVE framework that allows us to seamlessly define the environment and incorporate the Agrobots-SIM algorithms to be validated into SPADE agents inhabiting such an environment. We compare the proposed simulation architecture with the conventional approach to 3D multi-robot simulation in Gazebo. The functioning of the simulation architecture is demonstrated in several use-case experiments. Even though resource consumption and community support are still an open challenge in Unity3D, the proposed Agri-RO5 architecture gives better results in terms of simulation realism and scalability.
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Pollmann, Kathrin. "The Modality Card Deck: Co-Creating Multi-Modal Behavioral Expressions for Social Robots with Older Adults." Multimodal Technologies and Interaction 5, no. 7 (June 29, 2021): 33. http://dx.doi.org/10.3390/mti5070033.
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Robots have been proposed as intelligent technology that can support the independent living and health of older adults. While significant advances are being made regarding hardware and intelligent software to support autonomous actions of robots, less emphasis has been put on designing robot behavior that is comprehensible and pleasant for older adults. However, good usability and user experience are crucial factors for acceptance and long-term use. One way to actively engage older adults in behavioral design for social robots is participatory design. The Modality Card Deck is proposed, a tool that helps to engage older adults in human-robot interaction design process and participate in design decision for robot behavior. The cards guide the users towards creating ideas for design solutions which are detailed enough to be implemented by interaction designers and software developers. This paper provides a detailed description of the Modality Card Deck and presents an evaluation of the tool in the scope of a case study. In the case study, the card deck was used in participatory design workshops with older adults to develop multi-modal robot behaviors for the Pepper robot and a quiz game application. After describing the procedure of the case study, the workshop results and learnings about working with the Modality Card Deck and older adults are presented.
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You, Jiangwei, Jianfang Jia, Xiaoqiong Pang, Jie Wen, Yuanhao Shi, and Jianchao Zeng. "A Novel Multi-Robot Task Assignment Scheme Based on a Multi-Angle K-Means Clustering Algorithm and a Two-Stage Load-Balancing Strategy." Electronics 12, no. 18 (September 11, 2023): 3842. http://dx.doi.org/10.3390/electronics12183842.
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A practical task assignment is one of the core issues of a multi-robot system. In this paper, a multi-robot task assignment strategy based on load balancing is proposed to effectively balance and plan out the execution cost of each robot when it has a large number of working task points. Considering the variability of the execution task cost in practical situations with different task point categories, the multi-robot task assignment (MRTA) problem is transformed into a multiple traveling salesman problem (MTSP) using a multi-angle K-means clustering algorithm. To solve the problem of unbalanced cost consumed by each robot after clustering assignment, which leads to low efficiency of system completion, a two-stage load-balancing strategy is presented. The first stage of this strategy makes a large adjustment to the unbalanced task set, and the second stage achieves a small fine-tuning to the unbalanced task set. The experimental results show that the standard deviation of the cost ratio of each set decreases when four robots perform the task between 100 and 550 work points using the load-balancing strategy. The reduction in standard deviation is between 3.53% and 83.44%. The maximum cost of individual robots decreases between 0.18% and 14.27%. The proposed method can effectively solve the uneven execution cost of each robot in the task assignment process and improve the efficiency of the system in completing tasks.
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Chen, Cheng-Hung, Cheng-Jian Lin, Shiou-Yun Jeng, Hsueh-Yi Lin, and Cheng-Yi Yu. "Using Ultrasonic Sensors and a Knowledge-Based Neural Fuzzy Controller for Mobile Robot Navigation Control." Electronics 10, no. 4 (February 14, 2021): 466. http://dx.doi.org/10.3390/electronics10040466.
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This study proposes a knowledge-based neural fuzzy controller (KNFC) for mobile robot navigation control. An effective knowledge-based cultural multi-strategy differential evolution (KCMDE) is used for adjusting the parameters of KNFC. The KNFC is applied in PIONEER 3-DX mobile robots to achieve automatic navigation and obstacle avoidance capabilities. A novel escape approach is proposed to enable robots to autonomously avoid special environments. The angle between the obstacle and robot is used and two thresholds are set to determine whether the robot entries into the special landmarks and to modify the robot behavior for avoiding dead ends. The experimental results show that the proposed KNFC based on the KCMDE algorithm has improved the learning ability and system performance by 15.59% and 79.01%, respectively, compared with the various differential evolution (DE) methods. Finally, the automatic navigation and obstacle avoidance capabilities of robots in unknown environments were verified for achieving the objective of mobile robot control.
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Gaiduk, Anatoly, Sergey Kapustyan, Vladimir Plaksienko, and Ali El A. Kabalan. "Control of the UAV group at uncertain delays in communication channels." Science Bulletin of the Novosibirsk State Technical University, no. 2-3 (November 13, 2020): 37–56. http://dx.doi.org/10.17212/1814-1196-2020-2-3-37-56.
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The UAV groups and other unmanned robots are more and more widely used to solve production and special problems in view of their bigger efficiency in comparison with single robots. The efficiency of a group in many respects is defined by the opportunities of the communications means between members of the group and its leader, which influences the efficiency of the decisions made. The control of the UAV group is often exercised by a multivariable control system whose algorithms are implemented by a computer complex of the group UAV leader. The problem here is that in individual channels of information exchanges between the UAV-leader and other UAVs of the group there is a time delay. Any space distributions of the UAV in the neighborhood of the leader cause significantly different, uncertain delays in channels of its contact with the UAV group that can lead to a loss of control stability. It is proposed to apply a multivariable, robust to delay control system to overcome this difficulty. The design of this system is carried out on the basis of the matrix decoupling control and the method of an analytical design of systems with control on output and impact (ADSCOI) taking into account conditions of feasibility of controls on digital computing elements. Analytical expressions including the solution to the linear system algebraic equations and the creation procedure of the digital control algorithms are found in the article. This control provides robustness of the system to delays in communication channels. The efficiency of the proposed approach is illustrated by a numerical design example of the multivariable group control system of the UAV. The results obtained can be applied to create digital control systems by both single input-single-output and multi-input-multi-output objects with an uncertain delay in agricultural, food, power, machine-building and other industries.
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Kabir, Homayun, Mau-Luen Tham, Yoong Choon Chang, Chee-Onn Chow, and Yasunori Owada. "Mobility-Aware Resource Allocation in IoRT Network for Post-Disaster Communications with Parameterized Reinforcement Learning." Sensors 23, no. 14 (July 17, 2023): 6448. http://dx.doi.org/10.3390/s23146448.
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Natural disasters, including earthquakes, floods, landslides, tsunamis, wildfires, and hurricanes, have become more common in recent years due to rapid climate change. For Post-Disaster Management (PDM), authorities deploy various types of user equipment (UE) for the search and rescue operation, for example, search and rescue robots, drones, medical robots, smartphones, etc., via the Internet of Robotic Things (IoRT) supported by cellular 4G/LTE/5G and beyond or other wireless technologies. For uninterrupted communication services, movable and deployable resource units (MDRUs) have been utilized where the base stations are damaged due to the disaster. In addition, power optimization of the networks by satisfying the quality of service (QoS) of each UE is a crucial challenge because of the electricity crisis after the disaster. In order to optimize the energy efficiency, UE throughput, and serving cell (SC) throughput by considering the stationary as well as movable UE without knowing the environmental priori knowledge in MDRUs aided two-tier heterogeneous networks (HetsNets) of IoRT, the optimization problem has been formulated based on emitting power allocation and user association combinedly in this article. This optimization problem is nonconvex and NP-hard where parameterized (discrete: user association and continuous: power allocation) action space is deployed. The new model-free hybrid action space-based algorithm called multi-pass deep Q network (MP-DQN) is developed to optimize this complex problem. Simulations results demonstrate that the proposed MP-DQN outperforms the parameterized deep Q network (P-DQN) approach, which is well known for solving parameterized action space, DQN, as well as traditional algorithms in terms of reward, average energy efficiency, UE throughput, and SC throughput for motionless as well as moveable UE.
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Peng, Fei, Li Zheng, Zhu Duan, and Yu Xia. "Multi-Objective Multi-Learner Robot Trajectory Prediction Method for IoT Mobile Robot Systems." Electronics 11, no. 13 (July 4, 2022): 2094. http://dx.doi.org/10.3390/electronics11132094.
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Robot trajectory prediction is an essential part of building digital twin systems and ensuring the high-performance navigation of IoT mobile robots. In the study, a novel two-stage multi-objective multi-learner model is proposed for robot trajectory prediction. Five machine learning models are adopted as base learners, including autoregressive moving average, multi-layer perceptron, Elman neural network, deep echo state network, and long short-term memory. A non-dominated sorting genetic algorithm III is applied to automatically combine these base learners, generating an accurate and robust ensemble model. The proposed model is tested on several actual robot trajectory datasets and evaluated by several metrics. Moreover, different existing optimization algorithms are also applied to compare with the proposed model. The results demonstrate that the proposed model can achieve satisfactory accuracy and robustness for different datasets. It is suitable for the accurate prediction of robot trajectory.
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Abdessemed. "Optimization of Clustering Time by a Group of Autonomous Robots Making Use of an Exclusive Multi-Marking." Journal of Computer Science 6, no. 12 (December 1, 2010): 1465–73. http://dx.doi.org/10.3844/jcssp.2010.1465.1473.
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Grisales-Ramírez, Efraín, and Gustavo Osorio. "Multi-Objective Combinatorial Optimization Using the Cell Mapping Algorithm for Mobile Robots Trajectory Planning." Electronics 12, no. 9 (May 4, 2023): 2105. http://dx.doi.org/10.3390/electronics12092105.
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The use of optimal control theory for motion planning is a challenging task. Cell mapping offers a way to formulate combinatorial optimization problems, allowing the inclusion of complex cost functions as well as multi-objective optimization problems. This paper presents a suboptimal solution for a trajectory planning problem in a workspace with obstacles, for a differential drive mobile robot. This method relies on the use of any linearization technique that allows the regularization of the combinatorial optimization problem. We explore some classical problems in optimal control, i.e., distance, control effort, and navigation time), as well as the multi-objective optimization problem (MOP). We also performed a comparison with two classical path planning algorithms, namely A∗ and RRT∗, to validate the proposed method when the multi-objective optimization problem includes distance in the cost function, achieving a compromise of less than 2% for the worst-case scenario for our case study.
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Yang, Liu, Chunhui Li, Wenlong Song, and Zhan Li. "Innovation-Superposed Simultaneous Localization and Mapping of Mobile Robots Based on Limited Augmentation." Electronics 12, no. 3 (January 24, 2023): 587. http://dx.doi.org/10.3390/electronics12030587.
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In this paper, Aaiming at the problem of simultaneous localization mapping (SLAM) for mobile robots, a limited-augmentation innovation superposition (LAIS) is proposed to solve the problems occurring in SLAM. By extending the single-time innovation superposition to multi-time innovation, the error accumulation during the movement of mobile robots is reduced and the accuracy of the algorithm is improved. At the same time, when the number of feature points observed by the sensor exceeds the threshold, the sensor range is restricted. Therefore, only the qualified feature points are added to the system state vector, which reduces the calculation amount of the algorithm and improves the running speed. Simulation results show that compared with other algorithms, LAIS has higher accuracy and higher running speed in environmental maps with a different number of landmark points.
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Cardoso, Rafael C., Angelo Ferrando, Daniela Briola, Claudio Menghi, and Tobias Ahlbrecht. "Agents and Robots for Reliable Engineered Autonomy:A Perspective from the Organisers of AREA 2020." Journal of Sensor and Actuator Networks 10, no. 2 (May 14, 2021): 33. http://dx.doi.org/10.3390/jsan10020033.
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Multi-agent systems, robotics and software engineering are large and active research areas with many applications in academia and industry. The First Workshop on Agents and Robots for reliable Engineered Autonomy (AREA), organised the first time in 2020, aims at encouraging cross-disciplinary collaborations and exchange of ideas among researchers working in these research areas. This paper presents a perspective of the organisers that aims at highlighting the latest research trends, future directions, challenges, and open problems. It also includes feedback from the discussions held during the AREA workshop. The goal of this perspective is to provide a high-level view of current research trends for researchers that aim at working in the intersection of these research areas.
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Bravo-Arrabal, Juan, Manuel Toscano-Moreno, J. J. Fernandez-Lozano, Anthony Mandow, Jose Antonio Gomez-Ruiz, and Alfonso García-Cerezo. "The Internet of Cooperative Agents Architecture (X-IoCA) for Robots, Hybrid Sensor Networks, and MEC Centers in Complex Environments: A Search and Rescue Case Study." Sensors 21, no. 23 (November 25, 2021): 7843. http://dx.doi.org/10.3390/s21237843.
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Cloud robotics and advanced communications can foster a step-change in cooperative robots and hybrid wireless sensor networks (H-WSN) for demanding environments (e.g., disaster response, mining, demolition, and nuclear sites) by enabling the timely sharing of data and computational resources between robot and human teams. However, the operational complexity of such multi-agent systems requires defining effective architectures, coping with implementation details, and testing in realistic deployments. This article proposes X-IoCA, an Internet of robotic things (IoRT) and communication architecture consisting of a hybrid and heterogeneous network of wireless transceivers (H2WTN), based on LoRa and BLE technologies, and a robot operating system (ROS) network. The IoRT is connected to a feedback information system (FIS) distributed among multi-access edge computing (MEC) centers. Furthermore, we present SAR-IoCA, an implementation of the architecture for search and rescue (SAR) integrated into a 5G network. The FIS for this application consists of an SAR-FIS (including a path planner for UGVs considering risks detected by a LoRa H-WSN) and an ROS-FIS (for real-time monitoring and processing of information published throughout the ROS network). Moreover, we discuss lessons learned from using SAR-IoCA in a realistic exercise where three UGVs, a UAV, and responders collaborated to rescue victims from a tunnel accessible through rough terrain.
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M. Abed, Baraa, and Wesam M. Jasim. "Hybrid approach for multi-objective optimization path planning with moving target." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 1 (January 1, 2022): 348. http://dx.doi.org/10.11591/ijeecs.v29.i1.pp348-357.
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Path planning algorithms are the most significant area in the robotics field. Path planning (PP) can be defined as the process of determining the most appropriate navigation path before a mobile robot moves. Path planning optimization refers to finding the optimal or near-optimal path. Multi-objective optimization (MOO) is concerned with finding the best solution values that satisfy multiple objectives, such as shortness, smoothness, and safety. MOO present the challenge of making decisions while balancing these contradictory issues through compromise (tradeoff). As a result, there is no single solution appropriate for all purposes in MOO, but rather a range of solutions. Several objectives are considered as part of this study, including path security, length, and smoothness, when planning paths for autonomous mobile robots in a dynamic environment with a moving target. Particle swarm optimization (PSO) algorithms are combined with bat algorithms (BA) to make a balance between exploration and exploitation. PSO algorithms used to optimize two important parameters of the bat algorithm. The proposed solution is tested through several simulations based on varying scenarios. The results demonstrate that mobile robots can travel clearly and safely along short paths and smoothly, proving this method's efficiency.
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Jiang, Yafeng, Liang Zhang, Mingxin Yuan, and Yi Shen. "A Novel Online Path Planning Algorithm for Multi-Robots Based on the Secondary Immune Response in Dynamic Environments." Electronics 13, no. 3 (January 30, 2024): 562. http://dx.doi.org/10.3390/electronics13030562.
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To solve the online path planning of multi-robots in dynamic environments, a novel secondary immune responses-based immune path planning algorithm (SIRIPPA) is presented. The algorithm comprises two immune stages. In the primary immune stage, the antibodies are mainly designed for obstacle avoidance and a primary immune kinetic model is designed in terms of the different impacts of obstacles on robot behaviors. The primary immune antibodies and their concentration values are mainly taken as the prior knowledge to accelerate the secondary immune response. In the secondary immune stage, aiming at the same obstacle antigens, which invade once more, the immune system quickly produces many behavior antibodies. Combining the primary immune results and secondary immune response results, the path planning performance of multi-robots is improved. The simulation experiment indicates that, in static environment tests, compared to corresponding immune planning algorithms, the SIRIPPA exhibits an average reduction of 6.22% in the global path length, a decrease of 23.00% in the average smoothness, and an average energy consumption reduction of 27.55%; the algorithm exhibits a better performance for path planning. The simulation test in a dynamic environment shows the good flexibility and stability of the SIRIPPA. Additionally, the experimental results in a real environment further support the validity of the SIRIPPA.
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Lu, Yongkang, Yuanqing Wu, Wenjian Zhong, Yanzhou Li, and Meng Chen. "Spatial Path Smoothing for Car-like Robots Using Corridor-Based Quadratic Optimization." Electronics 12, no. 4 (February 6, 2023): 819. http://dx.doi.org/10.3390/electronics12040819.
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The global planning module of car-like robots usually plans a coarse spatial path, which may be non-smooth and kinematically infeasible for car-like robots. This study proposes an efficient spatial path smoothing approach, which is capable of optimizing a rough spatial path to be a high quality one. Two novel designs contribute to the proposed approach. One is a direct corridor construction method that provides an optimization region for path optimization. Based on a redefined path representation in the generated corridor, the second one is a core spatial path optimization method, where the optimization problem is formulated as a multi-objective quadratic programming (QP) with corridor and maximum-curvature constraints. Meanwhile, integrating a two-steps strategy into an optimization process yields a good trade-off between efficiency and quality. Experiment results validate that the proposed approach has the ability to online generate a high quality path.
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Lu, Wenpeng, Rui Yu, Shoujin Wang, Can Wang, Ping Jian, and Heyan Huang. "Sentence Semantic Matching Based on 3D CNN for Human–Robot Language Interaction." ACM Transactions on Internet Technology 21, no. 4 (July 16, 2021): 1–24. http://dx.doi.org/10.1145/3450520.
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The development of cognitive robotics brings an attractive scenario where humans and robots cooperate to accomplish specific tasks. To facilitate this scenario, cognitive robots are expected to have the ability to interact with humans with natural language, which depends on natural language understanding ( NLU ) technologies. As one core task in NLU, sentence semantic matching ( SSM ) has widely existed in various interaction scenarios. Recently, deep learning–based methods for SSM have become predominant due to their outstanding performance. However, each sentence consists of a sequence of words, and it is usually viewed as one-dimensional ( 1D ) text, leading to the existing available neural models being restricted into 1D sequential networks. A few researches attempt to explore the potential of 2D or 3D neural models in text representation. However, it is hard for their works to capture the complex features in texts, and thus the achieved performance improvement is quite limited. To tackle this challenge, we devise a novel 3D CNN-based SSM ( 3DSSM ) method for human–robot language interaction. Specifically, first, a specific architecture called feature cube network is designed to transform a 1D sentence into a multi-dimensional representation named as semantic feature cube. Then, a 3D CNN module is employed to learn a semantic representation for the semantic feature cube by capturing both the local features embedded in word representations and the sequential information among successive words in a sentence. Given a pair of sentences, their representations are concatenated together to feed into another 3D CNN to capture the interactive features between them to generate the final matching representation. Finally, the semantic matching degree is judged with the sigmoid function by taking the learned matching representation as the input. Extensive experiments on two real-world datasets demonstrate that 3DSSM is able to achieve comparable or even better performance over the state-of-the-art competing methods.
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Peck, Robert H., Jon Timmis, and Andy M. Tyrrell. "Self-Assembly and Self-Repair during Motion with Modular Robots." Electronics 11, no. 10 (May 17, 2022): 1595. http://dx.doi.org/10.3390/electronics11101595.
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Self-reconfigurable modular robots consist of multiple modular elements and have the potential to enable future autonomous systems to adapt themselves to handle unstructured environments, novel tasks, or damage to their constituent elements. This paper considers methods of self-assembly, bringing together robotic modules to form larger organism-like structures, and self-repair, removing and replacing faulty modules damaged by internal events or environmental phenomena, which allow group tasks for the multi-robot organism to continue to progress while assembly and repair take place. We show that such “in motion” strategies can successfully assemble and repair a range of structures. Previously developed self-assembly and self-repair strategies have required group tasks to be halted before they could begin. This paper finds that self-assembly and self-repair methods able to operate during group tasks can enable faster completion of the task than previous strategies, and provide reliability benefits in some circumstances. The practicality of these new methods is shown with physical hardware demonstrations. These results show the feasibility of assembling and repairing modular robots whilst other tasks are in progress.
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Zhang, Kai, Jihao Gao, YunFei Wang, and MingLiang Liang. "Hardware Realization of Kinematic Mechanism and Control System of Multifunctional Industrial Robot." Security and Communication Networks 2022 (September 10, 2022): 1–5. http://dx.doi.org/10.1155/2022/1940708.
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In order to improve the position control accuracy of industrial robots and solve the problems of poor real-time and reconfigurability of traditional motion controllers, a hardware implementation method for the motion mechanism and control system of multi-functional industrial robots is proposed. The video acquisition system is suitable for infrared detectors containing 4 channels or readout circuits in 4 channels. The video display system is compatible with the old AV/S-Video interface, and is also suitable for computer system video graphics array (VGA) analog video interface and high-definition video interface. Multimedia Interface (HDMI) digital high-definition video interface display terminal equipment. The hardware circuit of video acquisition and corresponding video interface image output is designed, including signal amplification circuit, analog-to-digital conversion circuit, video information buffer circuit, video display digital-to-analog conversion circuit, and interface hardware circuit, to realize the digitization of image information acquisition and image information. Display simulation, construct VGA, AV/S-Video, and HDMI timing sequence with hardware description language through Field programmable gate array (FPGA) to complete the display of corresponding terminal equipment. The experimental results show that the experimental data was substituted into the formula and the variance σ = 0.09 mm was found, indicating that the detection error of the system is less than 0.27 mm, which meets the detection requirements. Through the 3D contour reconstruction experiment of the workpiece, the expected function realization of each module is proved, and the feasibility of the system software and hardware system is verified. This design has good scalability and stability, reducing labor costs.
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Oliveros, Juan Carlos, and Hashem Ashrafiuon. "Multi-Vehicle Navigation Using Cooperative Localization." Electronics 12, no. 24 (December 8, 2023): 4945. http://dx.doi.org/10.3390/electronics12244945.
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This paper assesses the effectiveness of cooperative localization for improving the performance of closed-loop control systems for networks for autonomous multi-vehicle navigation. Nonlinear dynamic models of two- and three-dimensional vehicles are presented along with their linearized forms. A nonlinear control algorithm is then presented based on the dynamic model. Relative position measurement equations and their linearized forms are introduced. The state and measurement equations are then employed for the propagation and update steps of an EKF-based cooperative localization algorithm. Initially, a series of experiments with networks of quadcopters and mobile robots are presented to validate the performance of cooperative localization for state estimation with the continuous or intermittent presence of absolute measurements or their complete absence. Finally, the performance of the control algorithm is evaluated with and without cooperative localization to demonstrate its effectiveness for improving performance.
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Yiğit, Tuncay, and Şadi Fuat Çankaya. "Implementation of Machine Learning Algorithms on Multi-Robot Coordination." Electronics 11, no. 11 (June 4, 2022): 1786. http://dx.doi.org/10.3390/electronics11111786.
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Occasionally, professional rescue teams encounter issues while rescuing people during earthquake collapses. One such issue is the localization of wounded people from the earthquake. Machines used by rescue teams may cause crucial issues due to misleading localization. Usually, robot technology is utilized to address this problem. Many research papers addressing rescue operations have been published in the last two decades. In the literature, there are few studies on multi-robot coordination. The systems designed with a single robot should also overcome time constraints. A sophisticated algorithm should be developed for multi-robot coordination to solve that problem. Then, a fast rescuing operation could be performed. The distinctive property of this study is that it proposes a multi-robot system using a novel heuristic bat-inspired algorithm for use in search and rescue operations. Bat-inspired techniques gained importance in soft-computing experiments. However, there are only single-robot systems for robot navigation. Another original aspect of this paper is that this heuristic algorithm is employed to coordinate the robots. The study is devised to encourage extended work related to earthquake collapse rescue operations.
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Yusuf, Luthfiansyah Ilhamnanda, and Aina Musdholifah. "A new hybrid parallel genetic algorithm for multi-destination path planning problem." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 1 (April 1, 2024): 584. http://dx.doi.org/10.11591/ijeecs.v34.i1.pp584-591.
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This paper proposes a new parallel approach of multi objective genetic algorithm for path planning problem. The main contribution of this work is to reduce the population size that effect in decreasing processing times of finding the optimum path for multi destination problem. This is achieved by combining the local population of island parallel approach and global population of global parallel approach. Various experiments have been conducted to evaluate the new hybrid parallel genetic algorithm (HPGA) in solving multi-objective path planning problems. Three different test areas with 2 destinations were used to assess the performance of HPGA. Furthermore, this work compares HPGA and sequential genetic algorithm (SeqGA), as well as compared to other existing parallel genetic algorithm (GA) methods. From experimental results show that proposed HPGA outperform others, in term of processing time i.e., up to 3.6 times speedup faster, and lowest GA parameter values. This proposed HPGA can be utilized to design robots with fast and consistent path planning, especially with various obstecles.
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Xu, Yiqi, Qiongqiong Li, Xuan Xu, Jiafu Yang, and Yong Chen. "Research Progress of Nature-Inspired Metaheuristic Algorithms in Mobile Robot Path Planning." Electronics 12, no. 15 (July 29, 2023): 3263. http://dx.doi.org/10.3390/electronics12153263.
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The research of mobile robot path planning has shifted from the static environment to the dynamic environment, from the two-dimensional environment to the high-dimensional environment, and from the single-robot system to the multi-robot system. As the core technology for mobile robots to realize autonomous positioning and navigation, path-planning technology should plan collision-free and smooth paths for mobile robots in obstructed environments, which requires path-planning algorithms with a certain degree of intelligence. Metaheuristic algorithms are widely used in various optimization problems due to their algorithmic intelligence, and they have become the most effective algorithm to solve complex optimization problems in the field of mobile robot path planning. Based on a comprehensive analysis of existing path-planning algorithms, this paper proposes a new algorithm classification. Based on this classification, we focus on the firefly algorithm (FA) and the cuckoo search algorithm (CS), complemented by the dragonfly algorithm (DA), the whale optimization algorithm (WOA), and the sparrow search algorithm (SSA). During the analysis of the above algorithms, this paper summarizes the current research results of mobile robot path planning and proposes the future development trend of mobile robot path planning.
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Vartholomaios, Panagiotis, Nacim Ramdani, Christophoros Christophorou, Dimosthenis Georgiadis, Thomas Guilcher, Myriam Blouin, Mohamed Rebiai, et al. "ENDORSE Concept." International Journal of Reliable and Quality E-Healthcare 8, no. 3 (July 2019): 47–59. http://dx.doi.org/10.4018/ijrqeh.2019070104.
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Hospitals are considered a field of logistic robotics of high commercial potential and therefore a handful of mobile robot solutions exist. However, they have failed to trigger widespread acceptance by the market. The ENDORSE system will pursue 4 innovation pillars: an infrastructure-less multi-robot navigation, i.e. minimum installation of sensors and communications buses inside the building for the localization of robots, targets and docking stations; advanced HRI for resolving deadlocks and achieving efficient sharing of space resources in crowded environments; the deployment of ENDORSE software as a cloud-based service facilitating its integration with corporate software solutions such as ERP and CRM, complying with GDPR data security requirements; and allowing for reconfigurable and modular hardware architectures so that diverse modules can be easily swapped. ENDORSE functionality will be demonstrated via the integration of an e-diagnostic support module for vital signs monitoring, facilitating connectivity to cloud-based EHR, and validated in an operational hospital environment for realistic assessment.
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Ni, Jianjun, Yu Gu, Guangyi Tang, Chunyan Ke, and Yang Gu. "Cooperative Coverage Path Planning for Multi-Mobile Robots Based on Improved K-Means Clustering and Deep Reinforcement Learning." Electronics 13, no. 5 (February 29, 2024): 944. http://dx.doi.org/10.3390/electronics13050944.
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With the increasing complexity of patrol tasks, the use of deep reinforcement learning for collaborative coverage path planning (CPP) of multi-mobile robots has become a new hotspot. Taking into account the complexity of environmental factors and operational limitations, such as terrain obstacles and the scope of the task area, in order to complete the CPP task better, this paper proposes an improved K-Means clustering algorithm to divide the multi-robot task area. The improved K-Means clustering algorithm improves the selection of the first initial clustering point, which makes the clustering process more reasonable and helps to distribute tasks more evenly. Simultaneously, it introduces deep reinforcement learning with a dueling network structure to better deal with terrain obstacles and improves the reward function to guide the coverage process. The simulation experiments have confirmed the advantages of this method in terms of balanced task assignment, improvement in strategy quality, and enhancement of coverage efficiency. It can reduce path duplication and omission while ensuring coverage quality.
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Tranzatto, Marco, Mihir Dharmadhikari, Lukas Bernreiter, Marco Camurri, Shehryar Khattak, Frank Mascarich, Patrick Pfreundschuh, et al. "Team CERBERUS Wins the DARPA Subterranean Challenge: Technical Overview and Lessons Learned." Field Robotics 4, no. 1 (January 10, 2024): 249–312. http://dx.doi.org/10.55417/fr.2024009.
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This article presents the CERBERUS robotic system-of-systems, which won the DARPA Subterranean Challenge Final Event in 2021. The Subterranean Challenge was organized by DARPA with the vision to facilitate the novel technologies necessary to reliably explore diverse underground environments despite the grueling challenges they present for robotic autonomy. Due to their geometric complexity, degraded perceptual conditions combined with lack of GNSS support, austere navigation conditions, and denied communications, subterranean settings render autonomous operations particularly demanding. In response to this challenge, we developed the CERBERUS system which exploits the synergy of legged and flying robots, coupled with robust control especially for overcoming perilous terrain, multi-modal and multi-robot perception for localization and mapping in conditions of sensor degradation, and resilient autonomy through unified exploration path planning and local motion planning that reflects robot-specific limitations. Based on its ability to explore diverse underground environments and its high-level command and control by a single human supervisor, CERBERUS demonstrated efficient exploration, reliable detection of objects of interest, and accurate mapping. In this article, we report results from both the preliminary runs and the final Prize Round of the DARPA Subterranean Challenge, and discuss highlights and challenges faced, alongside lessons learned for the benefit of the community.
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Zhang, Zhenqiang, Xiangyuan Jiang, Zhenfa Yang, Sile Ma, Jiyang Chen, and Wenxu Sun. "Scalable Multi-Robot Task Allocation Using Graph Deep Reinforcement Learning with Graph Normalization." Electronics 13, no. 8 (April 19, 2024): 1561. http://dx.doi.org/10.3390/electronics13081561.
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Task allocation plays an important role in multi-robot systems regarding team efficiency. Conventional heuristic or meta-heuristic methods face difficulties in generating satisfactory solutions in a reasonable computational time, particularly for large-scale multi-robot task allocation problems. This paper proposes a novel graph deep-reinforcement-learning-based approach, which solves the problem through learning. The framework leverages the graph sample and aggregate concept as the encoder to extract the node features in the context of the graph, followed by a cross-attention decoder to output the probability that each task is allocated to each robot. A graph normalization technique is also proposed prior to the input, enabling an easy adaption to real-world applications, and a deterministic solution can be guaranteed. The most important advantage of this architecture is the scalability and quick feed-forward character; regardless of whether cases have a varying number of robots or tasks, single depots, multiple depots, or even mixed single and multiple depots, solutions can be output with little computational effort. The high efficiency and robustness of the proposed method are confirmed by extensive experiments in this paper, and various multi-robot task allocation scenarios demonstrate its advantage.
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Rabeea Hashim Al-Dahhan, Mohammed, Ruqayah Rabeea Al-Dahhan, and Ali Tariq Radeef. "Multi-layer perceptron neural network mobile robot navigator in unknown environment." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 2 (August 1, 2023): 725. http://dx.doi.org/10.11591/ijeecs.v31.i2.pp725-733.
Full textAbstract:
<span lang="EN-US">Recently, navigation in an unknown environment without hitting obstacles was considered a big challenge faced by researchers. The difficulty in finding a good mathematical model for the different systems is deciding to use artificial intelligent controllers to control the mobile robot movement. In this paper, designing two multi-layer-perceptron neural networks (MLP-NN) was done to control the movement of mobile robots in an unknown environment. The first MLP-NN is to control the linear velocity on the x-axis and angular velocity of the robot’s movement while the other MLP-NN is designed to avoid the static and dynamic obstacles faced by the robot while navigating in an unknown environment. The results show each controller's advantages in performing navigation tasks and avoiding obstacles in different environments.</span>