Robotic motion planning with obstacle avoidance based on hierarchical deep reinforcement learning-Reference-Cited by-同舟云学术

Robotic motion planning with obstacle avoidance based on hierarchical deep reinforcement learning

Published:2023-05-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 3rd International Conference on Robotics and Control Engineering
language:
Short-container-title:

Author:

Zhao Guoquan¹^ORCID,Ying Fengkang¹^ORCID,Pang Zuowei²^ORCID,Liu Huashan³^ORCID

Affiliation:

1. College of Information Science and Technology, Donghua University, China and Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education, China

2. Huzhou Institute of Zhejiang University, China

3. College of Information Science andTechnology, Donghua University, China and Engineering Research Center of DigitizedTextile & Fashion Technology, China

Funder

Natural Science Foundation of Shanghai

Fundamental Research Funds for the Central Universities

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3598151.3598170

Reference11 articles.

1. Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

2. Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation

3. Rongxin Jiang . 2023. Robotic Obstacle-avoidable Trajectory Planning Based on Deep Reinforcement Learning. Master’s thesis . Donghua University . Rongxin Jiang. 2023. Robotic Obstacle-avoidable Trajectory Planning Based on Deep Reinforcement Learning. Master’s thesis. Donghua University.

4. A General Framework of Motion Planning for Redundant Robot Manipulator Based on Deep Reinforcement Learning

5. Timothy P Lillicrap Jonathan J Hunt Alexander Pritzel Nicolas Heess Tom Erez Yuval Tassa David Silver and Daan Wierstra. 2015. Continuous control with deep reinforcement learning. arxiv:1509.02971 Timothy P Lillicrap Jonathan J Hunt Alexander Pritzel Nicolas Heess Tom Erez Yuval Tassa David Silver and Daan Wierstra. 2015. Continuous control with deep reinforcement learning. arxiv:1509.02971