Behavior Mixing with Minimum Global and Subgroup Connectivity Maintenance for Large-Scale Multi-Robot Systems

TL;DR

This paper introduces a real-time distributed framework for multi-robot systems that ensures minimal yet sufficient connectivity to enable behavior mixing, allowing robots to switch behaviors while maintaining communication links.

Contribution

The paper presents a provably minimum connectivity maintenance framework with a distributed MCCST algorithm for large-scale multi-robot systems, ensuring connectivity with minimal controller modifications.

Findings

Effective scalability demonstrated with up to 100 robots.

The MCCST algorithm preserves connectivity with minimal constraints.

Controllers are minimally modified using barrier certificates for safety and connectivity.

Abstract

In many cases the multi-robot systems are desired to execute simultaneously multiple behaviors with different controllers, and sequences of behaviors in real time, which we call \textit{behavior mixing}. Behavior mixing is accomplished when different subgroups of the overall robot team change their controllers to collectively achieve given tasks while maintaining connectivity within and across subgroups in one connected communication graph. In this paper, we present a provably minimum connectivity maintenance framework to ensure the subgroups and overall robot team stay connected at all times while providing the highest freedom for behavior mixing. In particular, we propose a real-time distributed Minimum Connectivity Constraint Spanning Tree (MCCST) algorithm to select the minimum inter-robot connectivity constraints preserving subgroup and global connectivity that are \textit{least likely to be violated} by the original controllers. With the employed safety and connectivity barrier certificates for the activated connectivity constraints and collision avoidance, the behavior mixing controllers are thus minimally modified from the original controllers. We demonstrate the effectiveness and scalability of our approach via simulations of up to 100 robots with multiple behaviors.