Ensuring Progress for Multiple Mobile Robots via Space Partitioning, Motion Rules, and Adaptively Centralized Conflict Resolution

TL;DR

This paper introduces a novel framework for multi-robot coordination that guarantees progress without strict assumptions on individual planning strategies, using environment partitioning and adaptive conflict resolution.

Contribution

It presents a new environment partitioning and motion rule system that ensures progress for all robots without requiring shared planning strategies or environment discretization.

Findings

Framework guarantees progress for all robots.

No reliance on environment grid or strong communication assumptions.

Effective in simulated polygonal environments.

Abstract

In environments where multiple robots must coordinate in a shared space, decentralized approaches allow for decoupled planning at the cost of global guarantees, while centralized approaches make the opposite trade-off. These solutions make a range of assumptions - commonly, that all the robots share the same planning strategies. In this work, we present a framework that ensures progress for all robots without assumptions on any robot's planning strategy by (1) generating a partition of the environment into "flow", "open", and "passage" regions and (2) imposing a set of rules for robot motion in these regions. These rules for robot motion prevent deadlock through an adaptively centralized protocol for resolving spatial conflicts between robots. Our proposed framework ensures progress for all robots without a grid-like discretization of the environment or strong requirements on robot communication, coordination, or cooperation. Each robot can freely choose how to plan and coordinate for itself, without being vulnerable to other robots or groups of robots blocking them from their goals, as long as they follow the rules when necessary. We describe our space partition and motion rules, prove that the motion rules suffice to guarantee progress in partitioned environments, and demonstrate several cases in simulated polygonal environments. This work strikes a balance between each robot's planning independence and a guarantee that each robot can always reach any goal in finite time.