View-time based moving obstacle avoidance using stochastic prediction of obstacle motion

This paper proposes a new motion planning method of a mobile robot avoiding moving obstacles. To avoid moving obstacles, the trajectories of the obstacles are predicted using a stochastic model of obstacle motion. The obstacle motion is modeled as a random walk process. The method plans robot motion by the unit of view-time and view-period. View-time is defined as the time instant at which the robot senses the obstacle position and velocity. View-period is defined as the time interval during which the robot performs sensing, predicting and planning for collision-free motion. From the position and velocity at a view-time, we predict the future position of the obstacle. The random walk process model of obstacle motion is used to calculate the probability density that the predicted position is reached during the view-period. From the probability density function of the predicted position, the probability that a position can be swept by the obstacle during the view-period is calculated. Then artificial potential is assigned at every position by considering the probability. The force induced by the artificial potential field repels the robot away from the probable obstacle trajectory. This method is a look ahead scheme, and effective for moving obstacle avoidance. This method is applied to collision-free motion planning for a mobile robot in a dynamic and unknown environment with several moving and stationary obstacles.