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

Orr, J. Walker; Dutta, Ayan

doi:10.3390/s23073625

Cited by 43 publications

(10 citation statements)

References 254 publications

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“…The development of time series forecasting can be traced back many years, when statistical methods such as moving averages and exponential smoothing were widely used (Orr and Dutta, 2023 ). However, with the rise of machine learning and deep learning, traditional methods are gradually being replaced by more powerful models.…”

Section: Related Workmentioning

confidence: 99%

Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model

Wang,

Liu

2024

Front. Neurorobot.

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IntroductionIn the field of logistics warehousing robots, collaborative operation and coordinated control have always been challenging issues. Although deep learning and reinforcement learning methods have made some progress in solving these problems, however, current research still has shortcomings. In particular, research on adaptive sensing and real-time decision-making of multi-robot swarms has not yet received sufficient attention.MethodsTo fill this research gap, we propose a YOLOv5-PPO model based on A3C optimization. This model cleverly combines the target detection capabilities of YOLOv5 and the PPO reinforcement learning algorithm, aiming to improve the efficiency and accuracy of collaborative operations among logistics and warehousing robot groups.ResultsThrough extensive experimental evaluation on multiple datasets and tasks, the results show that in different scenarios, our model can successfully achieve multi-robot collaborative operation, significantly improve task completion efficiency, and maintain target detection and environment High accuracy of understanding.DiscussionIn addition, our model shows excellent robustness and adaptability and can adapt to dynamic changes in the environment and fluctuations in demand, providing an effective method to solve the collaborative operation problem of logistics warehousing robots.

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Section: Related Workmentioning

confidence: 99%

Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model

Wang,

Liu

2024

Front. Neurorobot.

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“…Autonomous mobile robots (AMRs) are a type of robot system designed to move payloads or perform specific tasks, requiring efficient, precise, and streamlined workflows. 154,155 With the advancement of sensor and information storage technologies, AMRs are being deployed in increasingly complex environments. Common AMRs include vacuum cleaners, lawn mowers, and logistics robots.…”

Section: Excellent Electrochemical Performance Of Mabsmentioning

confidence: 99%

Metal–air batteries for powering robots

Zhong,

Wang,

Zuo

et al. 2023

J. Mater. Chem. A

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“…Diverse approaches were employed in developing the skills by other teams, such as utilizing reinforcement learning [8,9]. However, the decision-making aspect continued to rely on traditional methods within the STP architecture [10].…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Probability-Based Strategy for a Football Multi-Agent Autonomous Robot System

Ribeiro,

Lopes,

Ribeiro

et al. 2023

Robotics

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The strategies of multi-autonomous cooperative robots in a football game can be solved in multiple ways. Still, the most common is the “Skills, Tactics and Plays (STP)” architecture, developed so that robots could easily cooperate based on a group of predefined plays, called the playbook. The development of the new strategy algorithm presented in this paper, used by the RoboCup Middle Size League LAR@MSL team, had a completely different approach from most other teams for multiple reasons. Contrary to the typical STP architecture, this strategy, called the Probability-Based Strategy (PBS), uses only skills and decides the outcome of the tactics and plays in real-time based on the probability of arbitrary values given to the possible actions in each situation. The action probability values also affect the robot’s positioning in a way that optimizes the overall probability of scoring a goal. It uses a centralized decision-making strategy rather than the robot’s self-control. The robot is still fully autonomous in the skills assigned to it and uses a communication system with the main computer to synchronize all robots. Also, calibration or any strategy improvements are independent of the robots themselves. The robots’ performance affects the results but does not interfere with the strategy outcome. Moreover, the strategy outcome depends primarily on the opponent team and the probability calibration for each action. The strategy presented has been fully implemented on the team and tested in multiple scenarios, such as simulators, a controlled environment, against humans in a simulator, and in the RoboCup competition.

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

Cited by 43 publications

References 254 publications

Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model

Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model

Metal–air batteries for powering robots

Probability-Based Strategy for a Football Multi-Agent Autonomous Robot System

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