A Novel Cooperative Path Planning for Multi-robot Persistent Coverage with Obstacles and Coverage Period Constraints

Area of Science:

• Robotics
• Artificial Intelligence
• Operations Research

Background:

• Persistent coverage is crucial for monitoring tasks.
• Existing methods often overlook specific coverage period requirements for sub-regions.
• Cooperative and persistent coverage have typically been addressed separately.

Purpose of the Study:

• To develop a combinatorial path planning approach for multi-robot persistent coverage.
• To simultaneously address cooperative coverage and individual sub-region coverage period constraints.
• To minimize the number of robots required for persistent coverage while ensuring obstacle avoidance.

Main Methods:

• Sensor deployment to meet minimum cost coverage requirements.
• Solving the Traveling Salesman Problem for a closed path frame.
• Partitioning the closed path into minimal segments satisfying coverage periods.
• Generating individual closed routes for each robot.

Main Results:

• The proposed approach successfully plans persistent and cooperative coverage paths.
• Obstacle avoidance and specific coverage period constraints are met.
• The number of robots utilized is minimized.

Conclusions:

• The combinatorial approach is feasible for complex persistent coverage scenarios.
• It effectively integrates cooperative coverage, obstacle avoidance, and time-based constraints.
• This method optimizes robot resource allocation for persistent area monitoring.