Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach

Li, Minghao; Jie, Yingrui; Kong, Yang; Cheng, Hui

doi:10.1109/icra46639.2022.9812163

Cited by 6 publications

(10 citation statements)

References 21 publications

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“…To further compare our proposed method with recent work on learning-based multi-robot control, we summarize the main points of distinction in Table 3. Existing methods can be categorized as reinforcement learning (RL) (such as Agarwal et al (2020); Li et al (2022); Yan et al (2022); Blumenkamp et al (2022)) or learning from demonstration (LfD) (such as Li et al (2020); Tolstaya et al (2020a) and our work), depending on the training paradigm. The RL method does not require an expert policy, but its trial-and-error nature could make training intractable for multi-robot systems.…”

Section: Discussionmentioning

confidence: 99%

“…The intractability issue is exacerbated when a realistic environment is considered, in which the dimensionalities of the robot state and observation spaces increase dramatically. As shown in the table, the RL-based methods (Blumenkamp et al, 2022;Li et al, 2022;Yan et al, 2022) were only validated for up to five robots when a realistic robot model was considered. It is noteworthy that Li et al (2022) incorporated LfD into RL to mitigate the training intractability issue.…”

Section: Discussionmentioning

confidence: 99%

“…Both compounding errors and latency are well-known issues that impact task performance and success in robotics. Learning-based methods, as another control design paradigm, have proven to be successful in learning control policies from data (Kahn et al, 2018;Devo et al, 2020;Li et al, 2020;Li et al, 2022). In particular, owing to the feature representation capability of deep neural networks (DNNs), control policies can be modeled to synthesize control commands directly from a raw sensor observations.…”

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confidence: 99%

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End-to-end decentralized formation control using a graph neural network-based learning method

Jiang,

Huang,

Guo

2023

Front. Robot. AI

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Multi-robot cooperative control has been extensively studied using model-based distributed control methods. However, such control methods rely on sensing and perception modules in a sequential pipeline design, and the separation of perception and controls may cause processing latencies and compounding errors that affect control performance. End-to-end learning overcomes this limitation by implementing direct learning from onboard sensing data, with control commands output to the robots. Challenges exist in end-to-end learning for multi-robot cooperative control, and previous results are not scalable. We propose in this article a novel decentralized cooperative control method for multi-robot formations using deep neural networks, in which inter-robot communication is modeled by a graph neural network (GNN). Our method takes LiDAR sensor data as input, and the control policy is learned from demonstrations that are provided by an expert controller for decentralized formation control. Although it is trained with a fixed number of robots, the learned control policy is scalable. Evaluation in a robot simulator demonstrates the triangular formation behavior of multi-robot teams of different sizes under the learned control policy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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End-to-end decentralized formation control using a graph neural network-based learning method

Jiang,

Huang,

Guo

2023

Front. Robot. AI

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“…Lin et al [ 112 ] developed a novel deep reinforcement learning approach for coordinating the movements of an MRS such that the geometric center of the robots reached a target destination while maintaining a connected communication graph throughout the mission. Similarly, Li et al [ 183 ] proposed a DRL method for multi-robot navigation while maintaining connectivity among the robots. The presented technique used constrained policy optimization [ 184 ] and behavior cloning.…”

Section: Multi-robot System Applications Of Multi-agent Deep Reinforc...mentioning

confidence: 99%

Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey

Orr

Dutta

2023

Sensors

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Deep reinforcement learning has produced many success stories in recent years. Some example fields in which these successes have taken place include mathematics, games, health care, and robotics. In this paper, we are especially interested in multi-agent deep reinforcement learning, where multiple agents present in the environment not only learn from their own experiences but also from each other and its applications in multi-robot systems. In many real-world scenarios, one robot might not be enough to complete the given task on its own, and, therefore, we might need to deploy multiple robots who work together towards a common global objective of finishing the task. Although multi-agent deep reinforcement learning and its applications in multi-robot systems are of tremendous significance from theoretical and applied standpoints, the latest survey in this domain dates to 2004 albeit for traditional learning applications as deep reinforcement learning was not invented. We classify the reviewed papers in our survey primarily based on their multi-robot applications. Our survey also discusses a few challenges that the current research in this domain faces and provides a potential list of future applications involving multi-robot systems that can benefit from advances in multi-agent deep reinforcement learning.

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“…Li et al propose a reinforcement learning-based method where the inputs are: distance sensor data and the position of other robots, and the output is robot control commands, aiming to navigate while maintaining the connection [52].…”

Section: ) Mrs Connectivity Maintenancementioning

confidence: 99%

Multi-Robot Systems and Cooperative Object Transport: Communications, Platforms, and Challenges

Lin

et al. 2023

IEEE Open J. Comput. Soc.

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Multi-robot systems gain considerable attention due to lower cost, better robustness, and higher scalability as compared with single-robot systems. Cooperative object transport, as a well-known use case of multi-robot systems, shows great potential in real-world applications. The design and implementation of a multi-robot system involve many technologies, specifically, communication, coordination, task allocation methods, experimental platforms, and simulators. However, most of recent multi-robot system studies focus on coordination and task allocation problems, with little focus on communications among multiple robots. In this review, we focus on the communication, validation platform, and simulator of multi-robot systems, and discuss one of the important applications, cooperative object transport. First, we study the multi-robot system fundamentals and comprehensively review the multi-robot system communication technologies. Then, the multi-robot system validating platform, testbed, simulator, and middleware used in academia and industry are investigated. Finally, we discuss recent advances in cooperative object transport, and challenges and possible future research directions for multi-robot systems.

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Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach

Cited by 6 publications

References 21 publications

End-to-end decentralized formation control using a graph neural network-based learning method

End-to-end decentralized formation control using a graph neural network-based learning method

Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey

Multi-Robot Systems and Cooperative Object Transport: Communications, Platforms, and Challenges

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