Resilient Multi-Robot Coverage Path Redistribution Using Boustrophedon Decomposition for Environmental Monitoring

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.