Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences
arxiv(2024)
摘要
An exciting frontier in robotic manipulation is the use of multiple arms at
once. However, planning concurrent motions is a challenging task using current
methods. The high-dimensional composite state space renders many well-known
motion planning algorithms intractable. Recently, Multi-Agent Path-Finding
(MAPF) algorithms have shown promise in discrete 2D domains, providing rigorous
guarantees. However, widely used conflict-based methods in MAPF assume an
efficient single-agent motion planner. This poses challenges in adapting them
to manipulation cases where this assumption does not hold, due to the high
dimensionality of configuration spaces and the computational bottlenecks
associated with collision checking. To this end, we propose an approach for
accelerating conflict-based search algorithms by leveraging their repetitive
and incremental nature – making them tractable for use in complex scenarios
involving multi-arm coordination in obstacle-laden environments. We show that
our method preserves completeness and bounded sub-optimality guarantees, and
demonstrate its practical efficacy through a set of experiments with up to 10
robotic arms.
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