Abstract: Collective navigation is inspired by the long-distance migrations of biological swarms, which maintain stable collective behavior in the whole journey. Inspired by these biological navigation mechanisms, multi-agent systems can implement such strategies to eliminate their reliance on inter-agent communication. However, existing bio-inspired collective navigation methods often rely on noisy relative positioning measurements and lack sufficient fault tolerance for absolute positioning failures. To address these issues, we propose a robust bio-inspired collective navigation method for drone swarms based on information fusion and adaptive fault tolerance. At the perception level, an interactive multiple modelbased fusion framework integrates kinematic predictions with visual positioning measurements, thereby suppressing sensor noise and improving relative positioning accuracy. At the decision level, we develop an adaptive fault-tolerant bioinspired swarm decision-making algorithm, which consists of (1) a dual-gradient proximal term that enables rapid collision avoidance and swarm aggregation and (2) an adaptive migration term that allows agents to dynamically adjust decision weights based on their positioning confidence. During positioning failures, agents reduce reliance on the migration term and increase their dependence on neighbors. Numerical simulations and real-world flight experiments demonstrate that the proposed method significantly enhances visual relative positioning precision and maintains stable collective performance during positioning failures, thereby improving the collective navigation robustness.

Key words: Bio-inspired')">Bio-inspired, Drone swarm, Collective navigation, Communication denial, Swarm decision-making