Coverage Path Planning with Dynamic Target Selection for Search and Rescue

This paper proposes a novel coverage path planning algorithm for autonomous robots in grid-based environments, focusing on search and rescue missions. Efficient exploration and mapping of disaster areas are crucial for locating survivors and guiding rescue operations. The algorithm addresses complex, dynamic environments by dynamically selecting target points using a weighted scoring function that balances exploration and exploitation, minimizing travel distance and energy consumption. It incorporates visibility constraints to simulate real-world sensor limitations, ensuring accurate coverage updates and thorough exploration. The integration of the A* algorithm enables optimal navigation around obstacles, enhancing efficiency in cluttered spaces. This approach is vital for mission success and survival rates in time-sensitive rescue scenarios.