Prioritized Multi-agent Path Finding for Differential Drive Robots

TL;DR

This paper introduces modifications to the prioritized multi-agent pathfinding algorithm AA-SIPP(m) to handle real-world constraints of differential drive robots, improving robustness and scalability in simulation and lab experiments.

Contribution

The paper presents a set of modifications to AA-SIPP(m) that lift key simplifying assumptions, enabling practical, collision-free path planning for differential drive robots in realistic scenarios.

Findings

Algorithm scales to hundreds of robots in simulation

Produces collision-free, robust trajectories for differential drive robots

Effective in real-world lab environment with imperfect trajectory following

Abstract

Methods for centralized planning of the collision-free trajectories for a fleet of mobile robots typically solve the discretized version of the problem and rely on numerous simplifying assumptions, e.g. moves of uniform duration, cardinal only translations, equal speed and size of the robots etc., thus the resultant plans can not always be directly executed by the real robotic systems. To mitigate this issue we suggest a set of modifications to the prominent prioritized planner -- AA-SIPP(m) -- aimed at lifting the most restrictive assumptions (syncronized translation only moves, equal size and speed of the robots) and at providing robustness to the solutions. We evaluate the suggested algorithm in simulation and on differential drive robots in typical lab environment (indoor polygon with external video-based navigation system). The results of the evaluation provide a clear evidence that the algorithm scales well to large number of robots (up to hundreds in simulation) and is able to produce solutions that are safely executed by the robots prone to imperfect trajectory following. The video of the experiments can be found at https://youtu.be/Fer_irn4BG0.