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Automatic sensor placement for model-based robot vision.

S Y Chen1, Y F Li

  • 1Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong. mesychen@cityu.edu.hk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 17, 2004
PubMed
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This study introduces automatic sensor placement for robot vision, optimizing sensor viewpoints and paths for complete 3D object observation using genetic algorithms and pathfinding.

Area of Science:

  • Robotics
  • Computer Vision
  • Computational Geometry

Background:

  • Model-based robot vision requires multiple viewpoints for comprehensive 3D object observation.
  • Manual sensor placement is time-consuming and suboptimal.
  • Automated planning is crucial for efficient robotic sensing.

Purpose of the Study:

  • To develop an automated method for optimal sensor placement in model-based robot vision.
  • To determine the shortest path connecting these optimal viewpoints.
  • To create a practical tool for generating sensor placement plans.

Main Methods:

  • Object features are resampled with surface normals.
  • A genetic algorithm with a min-max criterion optimizes sensor placement.
  • Christofides algorithm calculates the shortest path between viewpoints.

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  • A Viewpoint Planner software integrates these functionalities.

    • Main Results:

    • The proposed method generates an optimal sensor placement graph.
    • The Viewpoint Planner successfully computes shortest paths.
    • Experimental validation on a real robot vision system confirms effectiveness.
    • The system allows for 3D animation, sensor specification, and parameter amendment.

    Conclusions:

    • The automated sensor placement method enhances efficiency and completeness in robot vision.
    • The Viewpoint Planner provides a comprehensive solution for sensor planning.
    • This approach is effective for real-world robotic applications.