Dynamic Evasion Point System for Mobile Robots

In this paper, a novel local path planner based on a complete Obstacle Avoidance Algorithm (OAA) and a Proportional-Derivative (PD) controller is developed for Mobile Robots (MR). The main contribution of this work is a local planner that generates global evasion points instead of just angles, and uses a normalized cost function that weights between the total Euclidean route distance and the orientation turning cost, separating the evasion algorithm from the pose tracking controller. ”Individual obstacles detected by the LiDAR (Light Detection and Ranging sensor) are classified by measuring the distance between their corners. The OAA determines whether the mobile robot can drive through them or considers them as a single obstacle. A target point (an evasion point or the final goal) is selected by minimizing a normalized cost function. Various real experiments were carried out comparing the VPH and our method to illustrate the advantages and efficiency in both scenarios, achieving successful evasion in complex environments, such as ’U’ structures, and maintaining stable CPU usage. Our method uses a PD controller to drive both wheels and allows the mobile robot to reach its destination point in a smooth and efficient manner.