Asymmetry Elliptical Likelihood Potential Field for Real-Time Three-Dimensional Collision Avoidance in Industrial Robots

Industrial robots play a crucial role in modern manufacturing, but ensuring safe human–robot collaboration remains a challenge. Traditional collision avoidance methods, such as physical barriers and emergency stops, are limited in efficiency and flexibility. This study proposes the Asymmetry Elliptical Likelihood Potential Field (AELPF) algorithm, a novel real-time collision avoidance system inspired by autonomous driving technologies. The AELPF method leverages LiDAR sensors to dynamically compute an asymmetric elliptical repulsive field, enabling precise obstacle detection and avoidance in 3D environments. Unlike conventional potential field approaches, the AELPF accounts for both vertical and horizontal deviations, allowing it to adapt to complex industrial settings. To quantify the performance of AELPF, we compare it to two commonly used algorithms: the Vector Field Histogram (VFH) and the Follow the Gap Method (FGM). In terms of processing time, the VFH algorithm requires 50 ms per cycle, while the FGM algorithm operates at 22 ms. In contrast, the the AELPF, when using only a single channel, processes at 12 ms, which is significantly faster than both the VFH and FGM. These results indicate that the AELPF not only provides faster decision-making but also ensures smoother, more responsive navigation in dynamic environments. Both simulation and physical experiments confirm that the AELPF significantly improves obstacle avoidance, particularly in the z-axis direction, reducing the risk of collisions while maintaining operational efficiency.