Path planning for mobile manipulation using probabilistic cell decomposition

Abstract

In the field of autonomous robotics, manipulation planning is a problem of major significance. A very important component within a manipulation planner is a path planner that is able to connect two configurations by a feasible continuous path, provided that such a path exists. Recently, a new probabilistic path planning method, probabilistic cell decomposition (PCD), has been shown to perform well for, amongst other problems, motion planning for a robotic manipulator. In this paper we investigate how the performance of the general method can be further improved when used within the context of manipulation planning by incorporating knowledge of a specific manipulator. We propose pre-computation of a cell decomposition covering self-collision, adjustment of the cell splitting procedure to the articulated structure of the robot and tuning of distance metrics with respect to the robot. To evaluate the algorithms, we present simulations of a Puma 560 robot arm mounted on a Nomadic XR4000 mobile platform.