Autonomous hierarchical multi-level clustering for multi-uav systems

The strategy of clustering is introduced to enable coordination in decentralized multi-UAV systems. Each cluster is an organized unit comprised of several cluster-members and one cluster-head. We propose the concept of multi-level clustering, in which cluster-heads form successively higher-level clusters, resulting in a tree-shaped hierarchy. Multi-level clustering provides a mechanism for aggregating local states and disseminating the information needed for system coordination. Related work shows that aggregate information is beneficial for efficient UAV path planning using reinforcement learning. We propose rules for scalable multi-level cluster-formation, taking into consideration the computational and communication loads associated with cluster maintenance and information aggregation and dissemination. The viability of the proposed concept is demonstrated in preliminary simulations. The scenarios considered examine the effects of agent motion, takeoff, and landing on multi-level clustering. The simulation results show that multi-level clustering is robust to the dynamics of multi-UAV environments.