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Related Experiment Video

Updated: May 28, 2026

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories
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Human-Inspired Dexterity-Oriented Perception and Trajectory Optimization for Robotic Surface Inspection.

Menghan Zou1,2,3, Yuchuang Tong1,2,3, Tianbo Yang1,2,3

  • 1Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

Biomimetics (Basel, Switzerland)
|May 26, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a robotic framework for industrial surface inspection, improving coverage and efficiency in complex environments. It uses human-like strategies to optimize image acquisition, reducing path length and solution time.

Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Manufacturing Technology

Background:

  • Industrial surface inspection is crucial for manufacturing quality control.
  • Challenges include occlusions and the resolution-coverage trade-off in robotic image acquisition.
  • Existing methods struggle with complex geometries and efficient data capture.

Purpose of the Study:

  • To develop a hierarchical trajectory optimization framework for robotic image acquisition.
  • To emulate human visual inspection behaviors for improved perception-action coordination.
  • To address challenges in complex geometries, occlusions, and kinematic constraints.

Main Methods:

  • A multi-constraint preprocessing model for occlusion-aware viewpoint generation.
  • A multi-objective trajectory optimization for viewpoint sequencing, path length, and motion smoothness.
Keywords:
biomimetic perceptionhierarchical trajectory optimizationindustrial surface inspectionposture enhancement networkrobotic image acquisitionviewpoint and path planning

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  • A Pose Reachability Augmented Generative Adversarial Network (PRAGAN) for adaptable imaging postures.

    • Main Results:

    • Achieved 100% coverage of key surface areas in physical experiments.
    • Reduced path length by 47.0% and solution time by 37.5% compared to baseline.
    • Ensured collision-free operation and validated effectiveness in simulation and real-world tests.

    Conclusions:

    • The proposed framework effectively integrates human-inspired perception and motion coordination.
    • It offers a practical and scalable solution for complex industrial surface inspection tasks.
    • Demonstrates significant improvements in efficiency, coverage, and adaptability.