GIPD: Global Intent Prediction and Decomposition of Cooperative Multi-Robot System in Non-Communication Environments

Abstract

In complex multi-robot application scenarios, particularly in dynamically adversarial, hazardous, or disaster environments, traditional cooperation paradigms face significant challenges due to unreliable or absent communication links. Achieving efficient cooperation in the absence of communication has become a key bottleneck limiting the performance of multirobot systems. In this paper, we propose a Global Intent Prediction and Decomposition (GIPD) framework that enables robots to perform cooperative behavior without relying on communication. Each robot independently infers a globally consistent intent based solely on its local observations, ensuring implicit alignment across the system. Given the inferred global intent, robots autonomously determine their responsibilities and select the most appropriate tasks. They then base their local decision-making on the global intent, selected tasks, and individual observations, thereby facilitating effective execution and cooperation. We validate our approach using the MPE and SMAC benchmarks. Additionally, real-world experiments involving multiple ships demonstrate the effectiveness and practical applicability of the proposed GIPD method.