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Multipass Target Search in Natural Environments.

Michael J Kuhlman1, Michael W Otte2, Donald Sofge3

  • 1Institute for Systems Research, Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA. mkuhlman@umd.edu.

Sensors (Basel, Switzerland)
|November 4, 2017
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Summary
This summary is machine-generated.

This study introduces an anytime algorithm for autonomous drone search and rescue, optimizing multipass coverage plans to maximize information gain and minimize uncertainty in disaster scenarios. The method efficiently plans long-duration, dynamically feasible trajectories for faster survivor detection.

Keywords:
branch and boundcoverage planninginformation gatheringpath planningtarget search

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Search and Rescue Technology

Background:

  • Disaster scenarios necessitate rapid search and rescue in challenging environments.
  • Drones offer potential for faster survivor detection, but autonomous search planning is complex.
  • Maximizing information gain (mutual information) is key to reducing target location uncertainty.

Purpose of the Study:

  • To develop an anytime algorithm for autonomous multipass target search in uncertain environments.
  • To generate long-duration, dynamically feasible trajectories for drones.
  • To maximize mutual information for efficient search and rescue operations.

Main Methods:

  • An anytime algorithm based on best-first branch and bound.
  • Incorporation of ϵ-admissible heuristics to accelerate search.
  • Planning dynamically feasible multipass coverage trajectories considering vehicle dynamics and mission duration.

Main Results:

  • The algorithm generates long-duration, dynamically feasible multipass coverage plans.
  • It effectively maximizes mutual information, minimizing target location uncertainty.
  • Benchmarking shows superior information gathering compared to state-of-the-art methods in simulated natural environments.

Conclusions:

  • The proposed algorithm efficiently addresses long-duration multipass target search challenges.
  • It enables autonomous drones to gather the most information within a given search time.
  • This approach significantly enhances the potential for expedited rescue operations in disaster zones.