Predator-Prey Reward Based Q-Learning Coverage Path Planning for Mobile Robot-Reference-Cited by-同舟云学术

Predator-Prey Reward Based Q-Learning Coverage Path Planning for Mobile Robot

Published:2023 Issue: Volume:11 Page:29673-29683
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Zhang Meiyan¹^ORCID,Cai Wenyu²^ORCID,Pang Lingfeng²

Affiliation:

1. College of Electrical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou, China

2. College of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China

Funder

Natural Science Foundation of Zhejiang Province

National Natural Science Foundation of China

National Innovation and Entrepreneurship Training Program for College Students

Scientific and Technological Innovation Activity Plan for College Students in Zhejiang Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10064303.pdf?arnumber=10064303

Reference30 articles.

1. PPCPP: A Predator–Prey-Based Approach to Adaptive Coverage Path Planning

2. Neural Dynamics-based Complete Coverage of Grid Environment by Mobile Robots

3. Research on Hierarchical Cooperative Algorithm Based on Genetic Algorithm and Particle Swarm Optimization

4. Adaptation in Natural and Artificial Systems

5. Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey

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