Distance Determination Method for Normally Distributed Obstacle Avoidance of Mobile Robots in Stochastic Environments

Obstacle avoidance methods require knowledge of the distance between a mobile robot and obstacles in the environment. However, in stochastic environments, distance determination is difficult because objects have position uncertainty. The purpose of this paper is to determine the distance between a robot and obstacles represented by probability distributions. Distance determination for obstacle avoidance should consider position uncertainty, computational cost and collision probability. The proposed method considers all of these conditions, unlike conventional methods. It determines the obstacle region using the collision probability density threshold. Furthermore, it defines a minimum distance function to the boundary of the obstacle region with a Lagrange multiplier method. Finally, it computes the distance numerically. Simulations were executed in order to compare the performance of the distance determination methods. Our method demonstrated a faster and more accurate performance than conventional methods. It may help overcome position uncertainty issues pertaining to obstacle avoidance, such as low accuracy sensors, environments with poor visibility or unpredictable obstacle motion