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

作     者：Haotian Liu Yuchuang Tong Zhengtao Zhang Haotian Liu;Yuchuang Tong;Zhengtao Zhang

作者机构：CAS Engineering Laboratory for Intelligent Industrial Vision Institute of Automation Chinese Academy of Sciences the School of Artificial Intelligence University of Chinese Academy of Sciences IEEE 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报(英文版))

年 卷 期：2024年第11卷第12期

页      面：2463-2475页

核心收录：

学科分类：080202[工学-机械电子工程] 08[工学] 080203[工学-机械设计及理论] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 

基　　金：supported in part by the National Natural Science Foundation of China(62303457, U21A20482) China Postdoctoral Science Foundation (2023M733737) the National Key Research and Development Program of China (2022YFB3303800) 

主　　题：Industrial robotics human observation-inspired meta-heuristic recurrent neural network motion planning and control universal image acquisition 

摘      要：Image acquisition stands as a prerequisite for scrutinizing surfaces inspection in industrial high-end *** imaging systems often exhibit inflexibility, being confined 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, seamlessly integrating multi-objective optimization trajectory planning and control scheme to harness measured point clouds for versatile, efficient, and highly accurate image acquisition across diverse structures and scenarios. Specifically, we introduce an energybased adaptive trajectory optimization(EBATO) method that combines deformation and deviation with dual-threshold optimization and adaptive weight adjustment to improve the smoothness and accuracy of imaging trajectory and posture. Additionally, a multi-optimization control scheme based on a meta-heuristic 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.