Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences
This addresses the challenge of efficient multi-robot manipulation planning for robotics applications, but it is incremental as it builds on existing conflict-based methods.
The paper tackled the problem of planning concurrent motions for multiple robotic arms in high-dimensional spaces, which is intractable with existing methods, by proposing an approach that accelerates conflict-based search algorithms using online-generated experiences, demonstrating practical efficacy with up to 10 arms while preserving completeness and bounded sub-optimality guarantees.
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.