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IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  > In Press, Accepted Manuscript
H. Liu, Y. Tong, and Z. Zhang, “Human observation-inspired universal image acquisition paradigm integrating multi-objective motion planning and control for robotics,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 0, pp. 1–13, May 2024.
Citation: H. Liu, Y. Tong, and Z. Zhang, “Human observation-inspired universal image acquisition paradigm integrating multi-objective motion planning and control for robotics,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 0, pp. 1–13, May 2024.
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Human Observation-Inspired Universal Image Acquisition Paradigm Integrating Multi-Objective Motion Planning and Control for Robotics

Funds:  This work was 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)
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    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.

     

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