Global Map Assisted Multi-Agent Collision Avoidance via Deep Reinforcement Learning around Complex Obstacles-Reference-Cited by-同舟云学术

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Global Map Assisted Multi-Agent Collision Avoidance via Deep Reinforcement Learning around Complex Obstacles

Published:2023-10-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
language:
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Author:

Du Yuanyuan¹,Zhang Jianan²,Xu Jie³,Cheng Xiang²,Cui Shuguang³

Affiliation:

1. Future Network of Intelligence Institute (FNii), School of Science and Engineering (SSE), The Chinese University of Hong Kong,Shenzhen,Shenzhen,China

2. School of Electronics, Peking University,The State Key Laboratory of Advanced Optical Communication Systems and Networks,Beijing,P. R. China

3. The Chinese University of Hong Kong,SSE, FNii,Shenzhen,Shenzhen,China

Funder

National Key R&D Program of China

NSFC

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10341341/10341342/10341762.pdf?arnumber=10341762

Reference20 articles.

1. The dynamic window approach to collision avoidance

2. Trajectory modification considering dynamic constraints of autonomous robots[C];Rosmann

3. Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning

4. Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

5. Crowd-Robot Interaction: Crowd-Aware Robot Navigation With Attention-Based Deep Reinforcement Learning

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. High-Speed Racing Reinforcement Learning Network: Learning the Environment Using Scene Graphs;IEEE Access;2024

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