HeRo: A State Machine-Based, Fault-Tolerant Framework for Heterogeneous Multi-Robot Collaboration

Abstract

Heterogeneous robots can work together to accomplish a variety of complex tasks and have shown great potential in many fields. There are many efforts to make robot task orchestration more efficient. However, current methods still have some limitations, including the lack of a high-level abstraction for programming method and fault handling mechanism. In this paper, we design a state machine-based, fault-tolerant framework for heterogeneous multi-robot collaboration named HeRo, to effectively support the development of heterogeneous multi-robot systems. HeRo has three key techniques: (1) a state machine-based programming language to flexibly model robot behaviors and tasks; (2) a state synchronization mechanism to achieve information exchange and maintain the consistency among heterogeneous robots in distributed environments; (3) a fault detection and recovery mechanism to monitor the system's runtime states and use Large Language Model (LLM) combined with Planning Domain Definition Language (PDDL) to enable automated recovery. We evaluate the effectiveness and fault recovery capability of the framework by setting up manufacturing task and fault scenarios with varying difficulty in the ARIAC simulation environment, achieving a 100% task completion rate, with low system overhead and flexible scalability.