Safe Cooperative Pursuit for Multi-UAV Systems Based on Control Barrier Functions and Neurodynamic Optimization

This study focuses on the problem of multiunmanned aerial vehicle cooperative pursuit in complex obstacle environments and proposes a lightweight cooperative pursuit method with safety guarantees. By incorporating obstacle motion dynamics, an enhanced control barrier function constraint framework is constructed, effectively overcoming the limitations of traditional approaches that consider only geometric collision avoidance in dynamic environments. To enable the simultaneous pursuit of multiple evading targets, a hybrid task allocation strategy is designed without requiring complex optimization solvers. Building upon this strategy, a distributed quadratic optimization problem is formulated, aiming to minimize control input variations under safety constraints. A neurodynamic approach is employed to solve this optimization problem in real time, thereby achieving efficient and safe control during the cooperative pursuit process. Subsequently, the stability and safety of the closed-loop system are theoretically analyzed. Finally, extensive simulations and physical experiments demonstrate the effectiveness and practicality of the proposed method in multitarget cooperative pursuit tasks.