Motion Planning for Mobile Robots Using Uncertain Obstacle Estimation

The collision-free movement of a mobile robot in the presence of dynamic obstacles remains a significant challenge. In addition to self-localization, we also need to worry about the location of the moving obstacles, taking into account the noise in the sensors and the uncertainty in the movement of these obstacles. In this paper, we propose an approach for omnidirectional robot maneuvering in a 2D workspace that combines a particle filter for the estimation of the obstacles from LiDAR laser sensor data and a variation of the Velocity Obstacles (VO) reactive motion planning method. The position and the velocity vector of the obstacles, as well as the uncertainty degree is estimated by the particle filter. These outputs are combined with the VO algorithm to achieve motion planning that takes into account the current level of uncertainty as well as a cost function that expresses the risk tolerance of the user. We validate the approach in simulation and in experiments with a physical robot.