Continuous Motion Planning for Service Robots with Multiresolution in Time

We present an approach to continuous motion planning with multiresolution in time. Our approach is based on stochastic trajectory optimization for motion planning (STOMP) and designed to decrease the optimization time in order to enable frequent replanning. Since service robots operate in environments with dynamic obstacles, it is likely that planned trajectories become invalid over time. Thus, it is not necessary to provide trajectories with a uniform high resolution. Our multiresolutional approach implicitly considers the uncertainty of the future by providing a trajectory with a gradually coarser schedule, which is refined trough replanning. In addition to employing temporal multiresolution, we speed up trajectory optimization by initializing replanning with the previous plan. The proposed multiresolution STOMP is evaluated in simulation in comparison to the original STOMP implementation. Our experiments show that multiresolution STOMP reduces the planning time and, hence, is able to avoid dynamic obstacles.