Sampling-Based Motion Planning on Sequenced Manifolds

Abstract

We address the problem of planning robot motions in constrained configuration spaces where the constraints change throughout the motion. The problem is formulated as a fixed sequence of intersecting manifolds, which the robot needs to traverse in order to solve the task. We specify a class of sequential motion planning problems that fulfill a particular property of the change in the free configuration space when transitioning between manifolds. For this problem class, the algorithm Planning on Sequenced Manifolds (PSM∗) is developed which searches for optimal intersection points between manifolds by using RRT∗ in an inner loop with a novel steering strategy. We provide a theoretical analysis regarding PSM∗s probabilistic completeness and asymptotic optimality. Further, we evaluate its planning performance on multi-robot object transportation tasks. Video: https://www.youtube.com/watch?v=Q8kbILTRxfU Code: https://github.com/etpr/sequential-manifold-planning