Bounded Rational RRT-QX: Multi-Agent Motion Planning in Dynamic Human-Like Environments Using Cognitive Hierarchy and Q-Learning
Author:
Affiliation:
1. Georgia Institute of Technology,Daniel Guggenheim School of Aerospace Engineering,USA
2. Virginia Tech,Bradley Department of Electrical and Computer Engineering,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9682670/9682776/09683761.pdf?arnumber=9683761
Reference19 articles.
1. Human-Like Motion Planning Based on Game Theoretic Decision Making
2. Robot navigation in dense human crowds: Statistical models and experimental studies of human–robot cooperation
3. Hierarchical Path Planning for Urban On-Demand Air Mobility
4. Kinodynamic motion planning
5. Robust Kinodynamic Motion Planning using Model-Free Game-Theoretic Learning
Cited by 3 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. Decentralized Multi-Agent Motion Planning in Dynamic Environments;2023 American Control Conference (ACC);2023-05-31
2. Online and Robust Intermittent Motion Planning in Dynamic and Changing Environments;IEEE Transactions on Neural Networks and Learning Systems;2023
3. Experimental Validation on Aerial Vehicles of Real -Time Motion Planning with Continuous -Time Q -Learning;IFAC PAPERSONLINE;2023
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