Real-time motion planning in the presence of moving obstacles

Safe operation of autonomous systems demands a collision-free motion trajectory at every time instant. This paper presents a method to calculate time-optimal motion trajectories for autonomous systems moving through an environment with both stationary and moving obstacles. To transform this motion planning problem into a small dimensional optimization problem, suitable for real-time optimization, the approach (i) uses a spline parameterization of the motion trajectory; and (ii) exploits spline properties to reduce the number of constraints. Solving this optimization problem with a receding horizon allows dealing with modeling errors and variations in the environment. In addition to extensive numerical simulations, the method is experimentally validated on a KUKA youBot. The average solving time of the optimization problem in the experiments is 0.05s, which is sufficiently fast for correcting deviations from the initial trajectory.