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

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.