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human observation-inspired Universal Image Acquisition Paradigm Integrating Multi-Objective Motion Planning and Control for Robotics

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2463-2475页

作者： Haotian Liu Yuchuang Tong Zhengtao Zhang 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

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.

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

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