Multi-Robot Environmental Coverage With a Two-Stage Coordination Strategy via Deep Reinforcement Learning
Author:
Affiliation:
1. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, China
2. School of Control Science and Engineering, Shandong University, Shandong, China
Funder
National Key Research and Development Project of New Generation Artificial Intelligence of China
National Natural Science Foundation of China
Natural Science Foundation of Shaanxi Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6979/10543072/10379517.pdf?arnumber=10379517
Reference47 articles.
1. Adapting to sensing and actuation variations in multi-robot coverage
2. H2GNN: Hierarchical-Hops Graph Neural Networks for Multi-Robot Exploration in Unknown Environments
3. Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs
4. Multi-Agent Reinforcement Learning for Visibility-based Persistent Monitoring
5. Real-time adaptive multi-robot exploration with application to underwater map construction
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