Research on Path Planning for Mobile Robots Based on an Improved Jump Point Search Algorithm

Path planning for mobile robots requires efficient algorithms to navigate autonomously, yet traditional methods like A* and Jump Point Search (JPS) often struggle with excessive node expansion, suboptimal paths, and limited obstacle avoidance. This study proposes an enhanced JPS algorithm integrating three key optimizations: (1) an adaptive heuristic function combining map resolution and dynamic target-distance weighting to prioritize critical search directions; (2) a safe jump point strategy using angular checks to avoid collisions at corners; and (3) line-of-sight pruning to eliminate redundant waypoints. Experiments across 20×20 simple and complex maps, as well as 40×40 grids, reveal the algorithm's advantages. On the 20×20 simple map, it cuts expanded nodes by 33%, shortens paths by 4.8%, and reduces turning points by half. On the 20×20 complex map, it trims expanded nodes by 28%, shortens paths by 4%, and again halves turning points. These enhancements considerably boost the algorithm's efficiency, optimality, and practical viability, making it well - suited for complex scenarios and more aligned with real - world application needs.