Improved Deep Deterministic Policy Gradient for Dynamic Obstacle Avoidance of Mobile Robot-Reference-Cited by-同舟云学术

Improved Deep Deterministic Policy Gradient for Dynamic Obstacle Avoidance of Mobile Robot

Author:

Gao Xiaoshan¹^ORCID,Yan Liang¹^ORCID,Li Zhijun²^ORCID,Wang Gang³^ORCID,Chen I-Ming⁴^ORCID

Affiliation:

1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

2. Department of Automation, University of Science and Technology of China, Hefei, China

3. School of Automation, Beijing University of Posts and Telecommunications, Beijing, China

4. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Nanyang Ave, Singapore

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

National Key Research and Development Program

Ningbo Key Scientific and Technological Project

Science and Technology on Aircraft Control Key Laboratory and Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Control and Systems Engineering,Software

Link

Reference30 articles.

Cited by 5 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

3. RAC-SAC: An improved Actor-Critic algorithm for Continuous Multi-task manipulation on Robot Arm Control;Proceedings of the 12th International Symposium on Information and Communication Technology;2023-12-07

5. Obstacle Avoidance for Automated Guided Vehicles Based on Deep Reinforcement Learning;IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society;2023-10-16