Closing the loop between motion planning and task execution using real-time GPU-based planners

Abstract

Many task execution techniques tend to repeatedly invoke motion planning algorithms in order to perform complex tasks. In order to accelerate the perform of such methods, we present a real-time global motion planner that utilizes the computational capabilities of current many-core GPUs (graphics processing units). Our approach is based on randomized sample-based planners and we describe highly parallel algorithms to generate samples, perform collision queries, nearest-neighbor computations, local planning and graph search to compute collision-free paths for rigid robots. Our approach can efficiently solve the single-query and multiquery versions of the planning problem and can obtain one to two orders of speedup over prior CPU-based global planning algorithms. The resulting GPU-based planning algorithm can also be used for real-time feedback for task execution in challenging scenarios. Copyright © 2010, Association for the Advancement of Artificial Intelligence. All rights reserved.