Paper ID: 2412.00366 • Published Nov 30, 2024
Efficient Multi-Robot Motion Planning for Manifold-Constrained Manipulators by Randomized Scheduling and Informed Path Generation
Weihang Guo, Zachary Kingston, Kaiyu Hang, Lydia E. Kavraki
TL;DR
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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 and
decoupled methods either scale poorly or lack completeness, and hybrid methods
that compose 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 random
stops and coordination motion along each path) and generates paths that are
more 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 baselines on
challenging problems in manipulator cases.