Real-time optimization-based planning in dynamic environments using GPUs

We present a novel algorithm to compute collision-free trajectories in dynamic environments. Our approach is general and does not require a priori knowledge about the obstacles or their motion. We use a replanning framework that interleaves optimization-based planning with execution. Furthermore, we describe a parallel formulation that exploits a high number of cores on commodity graphics processors (GPUs) to compute a high-quality path in a given time interval. We derive bounds on how parallelization can improve the responsiveness of the planner and the quality of the trajectory.