A Dynamic Clustering Algorithm for Relative Localization of Multi-Quadrotors

This paper presents an optimized clustering approach for relative localization in large-scale quadrotor networks operating in GNSS-denied environments. Addressing challenges of limited sensor range, measurement noise, and computational constraints, we propose a decentralized clustering strategy that balances spatial proximity, information quality, and resource efficiency. This approach utilizes a novel cost function for the optimal clustering of quadrotors for cooperative estimation, which integrates different mechanisms such as inter-quadrotor distances, observability matrix metrics, graph connectivity, and cluster size minimization. The method improves both accuracy and scalability by emphasizing geometrically favorable configurations and information-rich measurements while minimizing computational requirements. Simulation results support the effectiveness of the proposed cost function in improving the accuracy and efficiency for large-scale quadrotor relative position estimation.

کلیدواژه ها:

Relative localization ، Cooperative navigation ، Swarm ، Decentralized estimation ، Observability analysis ، Cluster optimization