Collision Detection and Avoidance for Multi-UAV based on Deep Reinforcement Learning

Wang, Guanzheng; Liu, Zhihong; Xiao, Kejiang; Xu, Yinbo; Yang, Lingjie; Wang, Xiangke

doi:10.23919/ccc52363.2021.9550712

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Policy Gradients DDPG PPO Other [23,24,39,40,59, (QMIX), [38] (DDQN), [93] (DDQN), [94] (DDQN), [95] (DQN), [96] (DQN) [46][47][48][49][50][97][98][99][100][101][102][103][104][105][106], [22,[52][53][54][55][56][57] [86,88,129-140] (TRPO), [141] (TRPO), [81] (TRPO), [142] (TD3), [143] (SAC), [144] (SAC)…”

Section: Q-networkmentioning

confidence: 99%

“…Ground Robots Manipulators [23,24,38,46,53,56,57,68,76,83,85,91,104,106,110,110,120,123,134,135,138,139,141,142,161,167,171,172,186,205,207,[218][219][220]230,240,269] [ 22,39,49,52,54,55,59,70,71,[73][74][75][77][78][79]…”

Section: Aerial Robotsmentioning

confidence: 99%

“…Curriculum learning [ 197 ] has also been used for multi-robot path planning in [ 198 ], where the path planning is modeled as a lesson, going from easy to hard difficulty levels. An end-to-end MADRL system for multi-UAV collision avoidance using PPO has been proposed by Wang et al [ 57 ]. Asayesh et al [ 137 ] proposed a novel module for safety control of a system of robots to avoid collisions.…”

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

confidence: 99%

“…Another popular algorithm in the multi-robot domain is PPO, potentially because of its relatively simple implementation [ 43 ]. PPO-clip and PPO-penalty are its two primary variants that are used in robotics [ 52 , 53 , 54 , 55 , 56 , 57 ].…”

Section: Introductionmentioning

confidence: 99%

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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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