TY - GEN
T1 - Multi-robot Path Planning Based on Proximal Policy Optimization Algorithm
AU - Huo, Xiang
AU - Wu, Xinkai
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - The ‘goods to people’ model-based intelligent warehouse employs an automated dispatching system, primarily reliant on handling robots, thereby significantly reducing manpower and enhancing the efficiency of warehouse operations. This study tackles a paramount challenge in intelligent warehouse systems, specifically, multi-robot path planning for express package handling in the intelligent warehouse by comprehensively considering different movement speeds of robots and multiple target locations (package unloading locations) need to be arrived in sequence for each robot. To address the multi-robot path planning problem, we utilize the Proximal Policy Optimization (PPO) algorithm integrated with centralized, decentralized, and hybrid policy optimization networks. The simulation experiment indicates that the implemented PPO, in conjunction with a hybrid policy optimization network, outperforms in orchestrating multi-robot path planning.
AB - The ‘goods to people’ model-based intelligent warehouse employs an automated dispatching system, primarily reliant on handling robots, thereby significantly reducing manpower and enhancing the efficiency of warehouse operations. This study tackles a paramount challenge in intelligent warehouse systems, specifically, multi-robot path planning for express package handling in the intelligent warehouse by comprehensively considering different movement speeds of robots and multiple target locations (package unloading locations) need to be arrived in sequence for each robot. To address the multi-robot path planning problem, we utilize the Proximal Policy Optimization (PPO) algorithm integrated with centralized, decentralized, and hybrid policy optimization networks. The simulation experiment indicates that the implemented PPO, in conjunction with a hybrid policy optimization network, outperforms in orchestrating multi-robot path planning.
KW - Multi-robot
KW - Path planning
KW - Proximal policy optimization (PPO)
UR - https://www.scopus.com/pages/publications/85199316634
U2 - 10.1007/978-981-97-3336-1_29
DO - 10.1007/978-981-97-3336-1_29
M3 - 会议稿件
AN - SCOPUS:85199316634
SN - 9789819733354
T3 - Lecture Notes in Electrical Engineering
SP - 332
EP - 347
BT - Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control - Swarm Decision and Planning Technologies
A2 - Li, Xiaoduo
A2 - Song, Xun
A2 - Zhou, Yingjiang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023
Y2 - 24 November 2023 through 27 November 2023
ER -