Adaptive Safety-Constrained Control for Quadrotors Navigation

In this article, we investigate the adaptive safety-constrained control problem for quadrotor unmanned aerial vehicle (QUAV) clusters in dense forest environments to achieve adaptive navigation and obstacle avoidance. Compared to traditional methods, obstacle avoidance constraints are introduced for the first time, and the limitations of fixed formations and the need for prior data are eliminated. First, a cluster constraint mechanism is developed to constrain the distance between QUAVs within the cluster and the distance between the QUAV and the desired trajectory. Then, considering the lack of targeted obstacle avoidance constraint mechanisms in previous methods and the extensive prior data required by learning-based approaches, an obstacle constraint model is established to ensure that the QUAV maintains a safe distance from obstacles to avoid collisions. Finally, an adaptive safety control strategy for QUAV clusters is proposed by combining constraint conditions and stability criteria. Under the proposed control strategy, the QUAV clusters can achieve stable, safe, and efficient navigation and obstacle avoidance, and all constraints will always be satisfied. Furthermore, a numerical simulation experiment on a QUAV cluster navigation demonstrates the effectiveness and flexibility of this strategy.