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Information-driven sensor path planning by approximate cell decomposition.

Chenghui Cai1, Silvia Ferrari

  • 1Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA. cc88@duke.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 6, 2009
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Summary
This summary is machine-generated.

This study introduces a new robotic sensing strategy for target classification in cluttered environments. The method optimizes sensor paths, outperforming traditional approaches in simulated mine-hunting scenarios.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Traditional path planning methods are insufficient for robots with limited fields of view (FOV).
  • Classifying multiple fixed targets in obstacle-populated workspaces requires specialized sensing strategies.
  • Robotic sensor deployment for target identification necessitates efficient path planning.

Purpose of the Study:

  • To develop a novel methodology for planning robotic sensing strategies.
  • To address the limitations of existing path planning techniques for bounded FOV sensors.
  • To compute optimal sensing strategies for target classification in complex environments.

Main Methods:

  • Developed an approximate cell-decomposition method representing workspace elements as bounded subsets.
  • Constructed a connectivity graph using observation cells.
  • Pruned the graph and transformed it into a decision tree for optimal strategy computation.

Main Results:

  • Demonstrated the effectiveness of the optimal sensing strategies in a mine-hunting application.
  • Numerical experiments showed superior performance compared to shortest path, complete coverage, random, and grid search strategies.
  • Confirmed applicability to non-overpass capable robots avoiding both targets and obstacles.

Conclusions:

  • The proposed methodology provides an effective approach for planning robotic sensing strategies in cluttered environments.
  • The developed technique offers significant improvements over existing search and path planning methods.
  • This approach enables efficient target classification for robots with bounded fields of view.