An Object Recognition Grasping Approach Using Proximal Policy Optimization With YOLOv5-Reference-Cited by-同舟云学术

An Object Recognition Grasping Approach Using Proximal Policy Optimization With YOLOv5

Published:2023 Issue: Volume:11 Page:87330-87343
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Zheng Qingchun¹,Peng Zhi²^ORCID,Zhu Peihao¹^ORCID,Zhao Yangyang³^ORCID,Zhai Ran²,Ma Wenpeng⁴

Affiliation:

1. Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin, China

2. School of Mechanical Engineering, Tianjin University of Technology, Tianjin, China

3. School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China

4. National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin, China

Funder

National Natural Science Foundation of China

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/ielx7/6287639/10005208/10217816.pdf?arnumber=10217816

Reference39 articles.

1. A Graph-Based Deep Reinforcement Learning Approach to Grasping Fully Occluded Objects

2. Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5

3. Deep reinforcement learning based moving object grasping

4. Defect Identification of Adhesive Structure Based on DCGAN and YOLOv5

5. Design of mobile garbage collection robot based on visual recognition

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