On the advantages of task motion multigraphs for efficient mobile manipulation

This paper addresses the problem of computing the sequence of motion plans necessary for a mobile manipulator to execute a given task. In our previous work, we have demonstrated that computational advantages can be obtained when solving this problem by using the notion of a task motion multigraph (TMM). TMMs represent the state spaces that correspond to various hardware components of the robot, and they convey this information to the motion planning level. In this paper, we present and evaluate an algorithm that further exploits TMMs and explores multiple state spaces simultaneously. Since tasks to be performed by mobile manipulators often allow solutions that use only a subset of the robot's hardware components, motion plans can be found in lower dimensional state spaces. The resulting solutions tend to be shorter, more natural and faster to compute. We show that when planning under geometric constraints only, information gained while exploring lower dimensional spaces can be reused to obtain solutions in higher dimensional spaces, if necessary. The reuse of information implicitly provides the ability to compute decoupled motion plans. If solutions are not found while planning in a decoupled fashion, the algorithm resorts to planning in the robot's full state space. Our experiments indicate speedups of 200% and solutions up to four times shorter when compared to an analogous approach that does not employ TMMs.