Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences

TL;DR

This paper introduces a method to speed up conflict-based search algorithms for multi-robot manipulation by utilizing online-generated experiences, enabling efficient planning for multiple robotic arms in complex environments.

Contribution

It presents a novel approach that leverages the repetitive nature of conflict-based search to improve planning efficiency in high-dimensional multi-robot manipulation scenarios.

Findings

Method preserves completeness and bounded sub-optimality guarantees.

Demonstrated effectiveness with up to 10 robotic arms in experiments.

Significantly reduces planning time in complex multi-arm environments.

Abstract

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.