In context
In 2023, mobile robot path planning remained a core challenge in industrial automation, with algorithms like JPS, A*, and RRT balancing efficiency and path quality. The need for smoother, safer trajectories in real-world environments drove research into hybrid optimization approaches.
What was reported
Researchers from Beijing Information Science & Technology University proposed a goal-oriented hierarchical smoothing optimization JPS (GHSO-JPS) algorithm to address JPS limitations, including lack of direction guidance and redundant path points. The method integrates an attractive potential field to focus search toward the goal, reducing irrelevant jump points.
A two-layer smoothing strategy removes redundant and turning points, shortening path length and improving smoothness. Trajectory optimization uses multi-segment polynomials with a repulsive potential field collision penalty for safety.
In real-vehicle tests across laboratory and campus areas, GHSO-JPS outperformed JPS, A*, and RRT. Compared to traditional JPS, it cut search time by 41.6%, jump points by 55.5%, path length by 3.22 m, and path points and total turning angle by 89.8% and 81.8%, respectively. Planning time dropped 70.4% versus A* and 93.7% versus RRT.
Why it mattered
This work demonstrated a practical path planning enhancement that improves efficiency and trajectory quality for mobile robots, offering a viable option for industrial environments requiring fast, smooth, and safe navigation.
Compared with the traditional JPS algorithm, the search time of the proposed algorithm is reduced by 41.6%, the amount of jump points is reduced by 55.5%.
Source: 《机器人》期刊 (robot.sia.cn) · Published 2023-07-08 · “基于目标导向和分层平滑优化JPS算法的移动机器人运动规划”