Fault-Resilience Role Engine for an Autonomous Cooperative Multi-Robot System using E-CARGO

Abstract

In safety-critical applications, where several mobile robots and autonomous agents are being utilized for a mission, a fault-resilience behavior of the system is necessary. The fault resilience mechanism mostly uses the robot’s redundancy and tasks reassignment to recover malfunctioning and increase operating efficiency. The E-CARGO (Environments - Classes, Agents, Roles, Groups, and Objects) model designed for the Role-Based Collaboration (RBC) approach has been used successfully on cooperative Multi-Robot Systems (MRSs). Role-based characteristics of E-CARGO will facilitate cooperative decision-making and simplify handling failure. This paper develops an extended E-CARGO model for a fault resilience role engine. Agents use factor graphs to update the process role and manage the potential failure in each time step. We apply hybrid control in this paper. By “hybrid” we mean that evaluating and assigning initial roles are centralized, and role-playing is decentralized based on the local observations. The RBC life cycle and a Bayesian consensus will maintain fault resilience behaviors. Potential failure can be identified in a Bayesian way by updating agents’ reliability and calling the central unit to assign new process roles to guarantee robustness. Simulation experiments show that the proposed role engine can increase performance and tolerate failures in multi-robot path planning scenarios.