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- W4310855087 abstract "As mobile robots are becoming more and more widely used, it is of great significance to design an efficient path planning method for multi-robot systems (MRS) that can adapt to complex and unknown environments. In this paper, we present a deep reinforcement learning (DRL) based method to navigate the MRS with collision avoidance in unknown dynamic environments, which consists of centralized learning and decentralized executing paradigm. The proposed policy maps original laser information into robot control commands without constructing global maps. The learned policy is tested in Gazebo environments with three robot systems, which shows the effective performance in terms of success rate, extra time rate, and formation maintenance rate." @default.
- W4310855087 created "2022-12-19" @default.
- W4310855087 creator A5065939196 @default.
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- W4310855087 date "2022-10-28" @default.
- W4310855087 modified "2023-10-14" @default.
- W4310855087 title "Mapless Path Planning of Multi-robot Systems in Complex Environments via Deep Reinforcement Learning" @default.
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- W4310855087 doi "https://doi.org/10.1109/docs55193.2022.9967756" @default.
- W4310855087 hasPublicationYear "2022" @default.
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