In extensive outdoor collaborative exploration tasks, multiple robots require efficient path planning methods to ensure rapid and comprehensive map construction.However, current collaborative mapping algorithms often integrate poorly with path planning, especially under limited communication conditions.Such conditions can complicate data exchange, leading to inefficiencies and missed areas in real-world environments.This paper introduces a path planning approach specifically designed for Tool distributed collaborative mapping tasks, aimed at enhancing map completeness, mapping efficiency, and communication robustness under communication constraints.We frame the entire task as a k-Chinese Postman Problem (k-CPP) and optimize it using a genetic algorithm (GA).
This method fully leverages topology maps to efficiently plan subpaths for multiple robots, ensuring thorough coverage of the mapping area without the need for prior navigation maps.Additionally, we incorporate communication constraints into our path planning to ensure stable data exchange among robots in environments with only short-range communication capabilities.Field experiment results Western Pads Tough 1 highlight the superior performance of our method in terms of stability, efficiency, and robust inter-robot communication.