Efficient Multi-Robot Motion Planning for Manifold-Constrained Manipulators by Randomized Scheduling and Informed Path Generation

TL;DR

This paper presents StAC, a hybrid multi-robot motion planning method that efficiently combines individual robot paths using scheduling and informed sampling, significantly reducing computational effort in complex manipulator scenarios.

Contribution

Introduction of StAC, a hybrid planning approach that improves path composition efficiency for multi-robot manipulators through scheduling and feedback-informed sampling.

Findings

StAC reduces the number of paths needed by 10 to 100 times compared to baselines.

StAC effectively handles high degree-of-freedom manipulators in narrow spaces.

The method demonstrates superior scalability and feasibility in complex scenarios.

Abstract

Multi-robot motion planning for high degree-of-freedom manipulators in shared, constrained, and narrow spaces is a complex problem and essential for many scenarios such as construction, surgery, and more. Traditional coupled methods plan directly in the composite configuration space, which scales poorly; decoupled methods, on the other hand, plan separately for each robot but lack completeness. Hybrid methods that obtain paths from individual robots together require the enumeration of many paths before they can find valid composite solutions. This paper introduces Scheduling to Avoid Collisions (StAC), a hybrid approach that more effectively composes paths from individual robots by scheduling (adding stops and coordination motion along all paths) and generates paths that are likely to be feasible by using bidirectional feedback between the scheduler and motion planner for informed sampling. StAC uses 10 to 100 times fewer paths from the low-level planner than state-of-the-art hybrid baselines on challenging problems in manipulator cases.