Sampling-Based Motion Planning on Sequenced Manifolds

TL;DR

This paper introduces PSM*, a novel sampling-based algorithm for robot motion planning in constrained spaces that change over time, using a sequence of intersecting manifolds, with proven optimality and practical evaluation.

Contribution

The paper presents PSM*, an innovative algorithm combining RRT* with a new steering strategy for sequential manifold planning, including theoretical guarantees and empirical validation.

Findings

PSM* is probabilistically complete and asymptotically optimal.

The algorithm effectively plans multi-robot transportation tasks.

Performance benchmarks show improved planning efficiency.

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