A Research on the Application of Theta* Algorithm in Path Planning Based on JPS Optimization

This study proposes an enhanced Theta* algorithm optimized with Jump Point Search (JPS) to improve the efficiency and performance of path planning. Integrating JPS preprocessing and path merging optimizations enhances path smoothness by refining heuristic information and jump points. During the path planning process with the Theta* algorithm, heuristic information generated by JPS is used to intelligently select the next expansion node, thereby bypassing unnecessary search steps. This approach aims to increase planning efficiency and reduce search time and memory consumption while generating more optimal paths. Simulation comparisons across various map sizes and environmental complexities validate the high applicability and effectiveness of the optimized fusion algorithm. Compared to traditional JPS, Theta*, and other complex environment planning algorithms such as RRT and PBS, the proposed method demonstrates significant advantages.