PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning

Published:2024-05-13 Issue: Volume: Page:9278-9284
ISSN:
Container-title:2024 IEEE International Conference on Robotics and Automation (ICRA)
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Author:

Yu Wenhao¹,Peng Jie²,Qiu Quecheng³,Wang Hanyu²,Zhang Lu⁴,Ji Jianmin³

Affiliation:

1. University of Science and Technology of China (USTC),Institute of Advanced Technology,Hefei,China,230026

2. USTC,School of Data Science,Hefei,China,230026

3. USTC,School of Computer Science and Technology,Hefei,China,230026

4. Hefei Comprehensive National Science Center,Institute of Artificial Intelligence,China,230088

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10609961/10609862/10610107.pdf?arnumber=10610107

Reference28 articles.

1. Motion planning and control for mobile robot navigation using machine learning: a survey

2. Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors

3. Exploration and exploitation in reinforcement learning;Coggan;Research supervised by Prof. Doina Precup, CRA-W DMP Project at McGill University,2004

4. Implementation of the pure pursuit path tracking algorithm;Coulter,1992

5. Follow the Past: a path-tracking algorithm for autonomous vehicles

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