Obstacle Detection and Avoidance Methods for Mobile Robot in Indoor Environment

Abstract In indoor environments, mobile robots are commonly equipped with depth cameras or 2-D laser rangefinders to detect obstacles and navigate through path planning algorithms. However, numerous challenges persist in achieving comprehensive detection and effective avoidance of collisions with obstacles in real indoor environments. The limitations of the 2-D laser rangefinder in accurately representing obstacles at varying heights, coupled with the inherent drawbacks of the rapidly-exploring random tree and the artificial potential field algorithms in obstacle avoidance. Therefore, in this paper, an obstacle detection and avoidance method for mobile robots in indoor environments is proposed, aiming to significantly enhance the efficiency and safety of robot navigation within indoor environments. Firstly, the depth cameras are fused with the 2-D laser rangefinder to expand the field of view of robot obstacle detection. Then, by adding the offset force to solve the problem that the mobile robot is trapped in the local minima caused by the traditional artificial potential field algorithm. On this basis, a series of path optimization methods are proposed to minimize path length while enhancing path smoothness. Experimental results demonstrate that the proposed method enables the mobile robot to detect obstacles comprehensively and acquire a concise and smooth collision-free path.