In context
In 2025, autonomous mobile robots were increasingly deployed in warehousing, logistics, and industrial settings, where reliable path planning remains a core challenge. Traditional artificial potential field (APF) methods, though simple and real-time, suffered from well-known drawbacks such as target inaccessibility and local minima, limiting their practical use in complex environments. This paper from researchers at Shenyang Ligong University and the Chinese Academy of Sciences aimed to overcome these limitations.
What was reported
The study introduced three key improvements to the classical APF algorithm. First, a sine distance factor was added to the repulsive field function, dynamically reducing repulsion near the target to solve the target inaccessibility problem. Second, an obstacle boundary point group tangent vector method was proposed, which accounts for obstacle size and shape by creating virtual boundary points and calculating temporary sub-targets to escape local minima. Third, an adaptive step size was implemented, adjusting iteration length based on obstacle congestion to improve efficiency and avoid repulsion near obstacles.
Simulation results showed the improved algorithm effectively resolved both target inaccessibility and local minima. In scenarios without these issues, path length remained close to traditional APF, while planning time was reduced by approximately 23.52%. Comparative experiments against mainstream local planners confirmed superior planning efficiency and path quality. Real-robot tests validated feasibility and effectiveness.
Why it mattered
By addressing fundamental APF weaknesses while preserving its simplicity and real-time nature, this work offered a practical enhancement for mobile robot navigation in industrial environments. The adaptive step size and boundary-aware approach could improve reliability in cluttered factory floors, potentially reducing downtime and enabling more efficient autonomous material handling.
"The improved algorithm can effectively solve the problems of target inaccessibility and local minima in the traditional APF."
Source: 《机器人》期刊 (robot.sia.cn) · Published 2025-09-16 · “基于改进人工势场算法的移动机器人路径规划方法”