Realization of skeletonization based on wave propagation with the enhanced grassfire and path planning application

Abstract This paper introduces a new method of realizing and implementing skeletonization based on wave propagation using the Grassfire Algorithm. The Grassfire Algorithm uses the traditional method of skeletonization and enhances it by taking the most optimized path, which prioritizes the longest common points, to find an optimized path of travel that exhibits navigation methods. In the absence of any common points at distance, the algorithm limits degradation by using the synchronization, repetition rules, or fallback of wave propagation. Our proposed algorithm can support adaptable responses from the system that allows for the cancellation of path offset, and simultaneous response to changes in the environment, while maintaining skeleton shape even in cluttered or dynamic environments. Path Planning is a key problem in autonomous navigation requiring efficient, safe traversal, and real-time responses. The experimental results of the proposed algorithm on various grid maps demonstrate the proposed method's ability to generate collision-free, geometrically related paths appropriate for complex, real-world applications, such as UAV, or multi-agent systems. The Grassfire Method, then, is both a lightweight, topological method with potential for improving real-time robotic path planning in autonomous systems.