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Summary
This summary is machine-generated.

This study presents a robust multi-robot path planning method that ensures continuous coverage even if robots fail. It adaptively redistributes tasks to maintain efficiency and balance workloads across the remaining robots.

Keywords:
boustrophedon decompositionmulti-robot coverage path planningmulti-robot systemspropagation

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Multi-robot systems require robust task allocation for continuous operation.
  • Robot failures can disrupt coverage missions and overload remaining robots.

Purpose of the Study:

  • To develop a resilient multi-robot coverage path planning approach.
  • To ensure adaptive task redistribution and balanced workloads upon robot failure.

Main Methods:

  • Utilized Boustrophedon Cell Decomposition for area partitioning.
  • Implemented a propagation-based strategy for dynamic task reallocation.
  • Simulated the approach in diverse environmental conditions.

Main Results:

  • Demonstrated robustness and adaptability across various environments.
  • Achieved fast and efficient task reallocation with minimal propagation cycles.
  • Reduced task variance and maintained balanced coverage.

Conclusions:

  • The proposed method ensures minimal disruption and efficient workload distribution in multi-robot coverage.
  • The approach is suitable for real-time applications in complex, dynamic environments.