DFASCN:A distributed flocking approach for UAV swarm collective navigation

In recent years, the application domains of unmanned swarms have been continuously expanding. Existing swarm navigation methods predominantly rely on communication networks for frequently information exchange to achieve stable navigation behavior. However, this reliance presents challenges in achieving coordinated cooperative behavior in communication-restricted and obstacle-rich environments. To ensure the task efficiency of swarms in such mission settings, we propose a distributed flocking framework to guide unmanned aerial vehicle (UAV) swarms in navigating from a starting point to a target in unknown environments.Our approach begins by employing Boyd’s OODA loop (Observe, Orient, Decide, Act), combined with a locally limited perception model, to develop an interactive decision-making process between individual UAVs and their external environment. We classify the roles of different UAV platforms within the swarm, enhancing cooperative flight efficiency through the guiding behavior of critical nodes. Each UAV utilizes a dynamic adjustment mechanism for control parameters, allowing adaptive modifications based on local flight states. Additionally, each UAV is equipped with a model predictive control (MPC) controller, which provides feasible control inputs to ensure robust and reliable operation in complex and dynamic scenarios.To evaluate the adaptability of our method, we conducted simulations across various task environments with differing obstacle densities and numbers of UAVs. The results validate the algorithm’s effectiveness and scalability, highlighting its robustness and potential applicability to real-world scenarios.