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A Linear Objective Function-Based Heuristic for Robotic Exploration of Unknown Polygonal Environments.

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Autonomous agents require efficient exploration strategies for unknown environments.
  • Visibility-based deployment algorithms aim for complete spatial coverage using agent fields of view.
  • The Next Best Observation (NBO) problem is crucial for optimizing exploration paths.

Purpose of the Study:

  • To present a novel heuristic for determining the next best view location for autonomous agents.
  • To develop a method for calculating multiple NBO points simultaneously using linear programming.
  • To evaluate the heuristic's performance against random deployment methods in simulated environments.

Main Methods:

  • A heuristic approach for Next Best Observation (NBO) point calculation was developed.
  • Linear programming techniques were utilized to solve the NBO problem.
  • An algorithm was implemented and tested in a MATLAB-simulated polygonal environment with holes.

Main Results:

  • The proposed heuristic successfully deployed agents for complete visual coverage in complex environments.
  • The algorithm demonstrated superior efficiency compared to random deployment strategies.
  • Simulations confirmed the effectiveness of calculating multiple NBO points concurrently.

Conclusions:

  • The presented heuristic offers an efficient method for autonomous agent deployment in unknown environments.
  • Linear programming integration provides a robust solution for the NBO problem.
  • This approach enhances the performance of distributed visibility-based exploration tactics.