Primate-Inspired Cooperation Emergence and Strategy Generation in Heterogeneous Robot Swarm

Biological groups exhibit an intrinsic capacity for self-organization, engendering complex and intelligent cooperative behaviors-a capability highly sought after for robotic swarms. This paper proposes an architecture for constructing swarm intelligence in cooperative tasks, termed Cooperation Emergence and Strategy Generation (CE&SG). The architecture empowers robot swarms with the ability to dynamically synthesize composite strategies through self-organized interactions, setting it apart from traditional passive or fixed approaches. CE&SG is inspired by biological observations of cooperation in primate groups, from which we extract an explanatory mechanism for self-organized strategy generation and implement it in robot swarms using a hyper-heuristic algorithm. Through this approach, we demonstrate that sophisticated swarm cooperation can emerge via continuous strategy generation. Concurrently, the framework addresses long-standing challenges in robotics, including adaptability to dynamic changes, system scalability, and strategic interpretability. In a proof-of-concept task of air defense suppression, the proposed approach outperforms several existing advanced methods. The task is executed collaboratively by robots with diverse functionalities. We tested scalability and fault tolerance mechanisms against multiple disruptive disturbances at scales of up to 250 robots and 1,500 tasks in scheduling-oriented discrete-event simulations. Physical feasibility is further validated in a high-fidelity environment with continuous kinematics and collision constraints. The evolutionary patterns of strategies are analyzed through case studies.