In dynamic and extreme space environments, current multi-robot systems inevitably encounter failures during autonomous task execution. Addressing these failures requires human-in-the-loop collaboration with astronauts, who first conduct fault analysis and then perform dynamic multi-robot task allocation (MRTA), a process critical for achieving mission objectives. This paper proposes a Knowledge Graph-guided Consensus-Based Bundle Algorithm (KG-CBBA) that integrates astronaut fault analysis generated by large language models (LLMs) into the fault recovery process for space exploration. Firstly, a knowledge graph (KG) is constructed to encode objective constraints and semantic triples between tasks and robots, enabling a unified representation of task feasibility and utility. Secondly, a semantic-enhanced utility allocation mechanism is designed to ensure consistent, feasible, and efficient task sequences under static allocation. When dynamic tasks arrive, KG-CBBA resolves conflicts and inserts new tasks while preserving the stability of existing task sequences. Numerical simulations validate the feasibility of KG-CBBA and demonstrate its superior performance compared with consensus-based bundle algorithm (CBBA), particle swarm optimization (PSO), and greedy baselines. In addition, a user study involving 96 participants shows that KG-CBBA, when integrated with LLMs, enhances collaborative fault recovery. Overall, KG-CBBA provides an effective solution for dynamic MRTA in space exploration and supports human-in-the-loop collaboration.
Wang et al. (Thu,) studied this question.