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

Abstract

Image acquisition stands as a prerequisite for scrutinizing surfaces inspection in industrial high-end manufacturing. Current 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 energy-based 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.