Thermal point cloud-enhanced potential field path planning for mobile robots in high-temperature environments

Abstract This paper addresses the challenge of robotic path planning in high-temperature operational environments. Aiming at the limitation of traditional methods that rely on geometric information while ignoring thermal radiation data, a potential field method path planning based on temperature-enhanced point cloud maps is proposed. By fusing the data of LiDAR and thermal imager, point cloud maps with temperature attributes are generated. Based on the traditional potential field method, the potential field function of thermal radiation repulsion was introduced. The temperature gradient of the heat source is transformed into the potential energy constraint of path planning to achieve path generation in high-temperature areas. The experimental results show that, compared with the traditional artificial potential field method, this study provides a new technical solution for the safe operation of robots in a thermal environment.