Experience-based Subproblem Planning for Multi-Robot Motion Planning

TL;DR

This paper introduces an experience-based planning method for multi-robot motion planning that constructs and uses databases of solutions for smaller subproblems, improving scalability and efficiency in complex scenarios.

Contribution

It presents a novel framework leveraging subproblem databases to efficiently solve large-scale multi-robot motion planning problems involving mobile and manipulator robots.

Findings

Significant improvements in scalability and planning efficiency.

Successful validation with up to 32 mobile and 16 manipulator robots.

Efficient handling of complex multi-robot scenarios.

Abstract

Multi-robot systems enhance efficiency and productivity across various applications, from manufacturing to surveillance. While single-robot motion planning has improved by using databases of prior solutions, extending this approach to multi-robot motion planning (MRMP) presents challenges due to the increased complexity and diversity of tasks and configurations. Recent discrete methods have attempted to address this by focusing on relevant lower-dimensional subproblems, but they are inadequate for complex scenarios like those involving manipulator robots. To overcome this, we propose a novel approach that %leverages experience-based planning by constructs and utilizes databases of solutions for smaller sub-problems. By focusing on interactions between fewer robots, our method reduces the need for exhaustive database growth, allowing for efficient handling of more complex MRMP scenarios. We validate our approach with experiments involving both mobile and manipulator robots, demonstrating significant improvements over existing methods in scalability and planning efficiency. Our contributions include a rapidly constructed database for low-dimensional MRMP problems, a framework for applying these solutions to larger problems, and experimental validation with up to 32 mobile and 16 manipulator robots.