On Enhancing Structural Resilience of Multirobot Coverage Control with Bearing Rigidity

TL;DR

This paper introduces a hierarchical control framework that enhances the resilience of multi-robot coverage systems by maintaining bearing rigidity, enabling recovery from robot failures and localization errors through decentralized MPC.

Contribution

It proposes a novel decentralized MPC-based control scheme that enforces bearing rigidity, improving fault tolerance and structural resilience in multi-robot coverage tasks.

Findings

The control framework guarantees network recovery after robot loss.

Simulations demonstrate improved resilience and coverage performance.

The approach maintains a minimally rigid structure during failures.

Abstract

The problem of multi-robot coverage control has been widely studied to efficiently coordinate a team of robots to cover a desired area of interest. However, this problem faces significant challenges when some robots are lost or deviate from their desired formation during the mission due to faults or cyberattacks. Since a majority of multi-robot systems (MRSs) rely on communication and relative sensing for their efficient operation, a failure in one robot could result in a cascade of failures in the entire system. In this work, we propose a hierarchical framework for area coverage, combining centralized coordination by leveraging Voronoi partitioning with decentralized reference tracking model predictive control (MPC) for control design. In addition to reference tracking, the decentralized MPC also performs bearing maintenance to enforce a rigid MRS network, thereby enhancing the structural resilience, i.e., the ability to detect and mitigate the effects of localization errors and robot loss during the mission. Furthermore, we show that the resulting control architecture guarantees the recovery of the MRS network in the event of robot loss while maintaining a minimally rigid structure. The effectiveness of the proposed algorithm is validated through numerical simulations.