Velocity-scaled safe artificial potential field for mobile robot navigation in dynamic environments

Purpose This paper aims to address local path planning for autonomous mobile robots among static and dynamic obstacles, aiming to improve the robustness of the Artificial Potential Field (APF) algorithm against collisions with approaching obstacles. Design/methodology/approach A modification of the Safe APF (SAPF), called the Velocity-Scaled Safe Artificial Potential Field (VSSAPF), is proposed, in which the repulsive gain is scaled by the closing velocity, estimated online from successive distance measurements and smoothed by an exponential low-pass filter. Simulations sweep the obstacle speed from 0% to 100% of the robot’s maximum linear velocity along straight-line and circular trajectories, against potential-field baselines and the Velocity Obstacle (VO) method, followed by validation on a Husarion ROSbot 2.0 PRO. Findings Among the potential-field methods, VSSAPF attains the highest simulated success rate, 74.3% against 42.6% and 61.4% for APF with default and retuned gain, and 30.7%, 45.5% and 49.5% for SAPF, Vortex Potential Field and Adaptive SAPF. The VO reaches 100% on the straight-line trajectory, which matches its constant-velocity prediction model exactly, but only 51.7% on the circular one, where VSSAPF reaches 80.3% and is never outperformed. On hardware the mechanism is confirmed up to half the robot’s maximum speed. Beyond that every tested method collides. Originality/value The method introduces velocity-aware repulsive gain scaling requiring no prediction of the obstacle trajectory, no offline tuning and no sensors beyond distance measurements. Assuming nothing about how the obstacle moves, its performance degrades gradually rather than abruptly once the obstacle leaves a straight-line path, precisely where model-based schemes lose their advantage.