LAC-RRT: Constrained Rapidly-Exploring Random Tree With Configuration Transfer Models for Motion Planning-Reference-Cited by-同舟云学术

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LAC-RRT: Constrained Rapidly-Exploring Random Tree With Configuration Transfer Models for Motion Planning

Published:2023 Issue: Volume:11 Page:97654-97663
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ho Chi-Kai¹^ORCID,King Chung-Ta¹^ORCID

Affiliation:

1. Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan

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/ielx7/6287639/10005208/10244038.pdf?arnumber=10244038

Reference22 articles.

1. Optimal Robot Motion Planning in Constrained Workspaces Using Reinforcement Learning

2. Learning structured output representation using deep conditional generative models;sohn;Proc Adv Neural Inf Process Syst,2015

3. Bidirectional Potential Guided RRT* for Motion Planning

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