Whole-Body Self-Collision Distance Detection for a Heavy-Duty Manipulator Using Neural Networks-Reference-Cited by-同舟云学术

Whole-Body Self-Collision Distance Detection for a Heavy-Duty Manipulator Using Neural Networks

Published:2024-02 Issue:2 Volume:9 Page:1380-1387
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Liu Hua¹^ORCID,Wang Hengsheng²^ORCID,Guo Xinping¹^ORCID

Affiliation:

1. College of Mechanical and Electrical Engineering, Central South University, Changsha, China

2. State Key Laboratory for High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/10360389/10356839.pdf?arnumber=10356839

Reference27 articles.

1. A Collision-Free MPC for Whole-Body Dynamic Locomotion and Manipulation

2. Humanoid Self-Collision Avoidance Using Whole-Body Control with Control Barrier Functions

3. A Bayesian-Based Controller for Snake Robot Locomotion in Unstructured Environments

4. Adaptive neural network sliding mode control for serially connected hydraulic cylinders of a heavy-duty hydraulic manipulator

5. Efficient collision detection using a dual OBB-sphere bounding volume hierarchy

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