首页|Human Observation-Inspired Universal Image Acquisition Paradigm Integrating Multi-Objective Motion Planning and Control for Robotics

Human Observation-Inspired Universal Image Acquisition Paradigm Integrating Multi-Objective Motion Planning and Control for Robotics

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
Human Observation-Inspired Universal Image Acquisition Paradigm Integrating Multi-Objective Motion Planning and Control for Robotics
Image acquisition stands as a prerequisite for scruti-nizing surfaces inspection in industrial high-end manufacturing.Current imaging systems often exhibit inflexibility,being con-fined to specific objects and encountering difficulties with diverse industrial structures lacking standardized computer-aided design(CAD)models or in instances of deformation.Inspired by the multidimensional observation of humans,our study introduces a universal image acquisition paradigm tailored for robotics,seam-lessly integrating multi-objective optimization trajectory planning and control scheme to harness measured point clouds for versa-tile,efficient,and highly accurate image acquisition across diverse structures and scenarios.Specifically,we introduce an energy-based adaptive trajectory optimization(EBATO)method that combines deformation and deviation with dual-threshold opti-mization and adaptive weight adjustment to improve the smooth-ness and accuracy of imaging trajectory and posture.Addition-ally,a multi-optimization control scheme based on a meta-heuris-tic beetle antennal olfactory recurrent neural network(BAORNN)is proposed to track the imaging trajectory while addressing posture,obstacle avoidance,and physical constraints in industrial scenarios.Simulations,real-world experiments,and comparisons demonstrate the effectiveness and practicality of the proposed paradigm.

-Industrial roboticshuman observation-inspiredmeta-heuristic recurrent neural networkmotion planning and con-troluniversal image acquisition

Haotian Liu、Yuchuang Tong、Zhengtao Zhang

展开 >

CAS Engineering Laboratory for Intelligent Industrial Vision,Institute of Automation,Chinese Academy of Sciences,Beijing 100190

School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China

-Industrial robotics human observation-inspired meta-heuristic recurrent neural network motion planning and con-trol universal image acquisition

2024

10.1109/JAS.2024.124512

自动化学报(英文版)
中国自动化学会,中国科学院自动化研究所,中国科技出版传媒股份有限公司

自动化学报(英文版)

CSTPCDEI
ISSN：2329-9266
年,卷(期)：2024.11(12)