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, we develop the algorithm Planning on Sequenced Manifolds (PSM*) 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://youtu.be/Q8kbILTRxfU Code: https://github.com/etpr/sequential-manifold-planning
Cite
@article{arxiv.2006.02027,
title = {Sampling-Based Motion Planning on Sequenced Manifolds},
author = {Peter Englert and Isabel M. Rayas Fernández and Ragesh K. Ramachandran and Gaurav S. Sukhatme},
journal= {arXiv preprint arXiv:2006.02027},
year = {2021}
}