Safety-critical control is crucial but difficult in the applications of autonomous underwater vehicles (AUVs). This article proposes a novel hierarchical safety-critical framework for the control of AUVs consisting of waypoint-based optimal trajectory generation and tracking. The objective is to generate a smooth and feasible trajectory to conform to the dynamics of the AUV and then control the AUV to operate in a safe region. To this end, the offline trajectory generator and the online controller are designed. In the offline step, an improved minimum snap polynomial trajectory is generated as the tracking reference. Specifically, the trajectory is optimized by explicitly considering the practical constraints of the AUV's actuator, which alleviates the complexity of the controller design and computational burden compared with the real-time nonlinear optimization. In the online step, the time- and state-dependent high-order control barrier functions are incorporated into optimization through quadratic programming (QP) that modifies the backstepping-based nominal controller in a minimally invasive way. As a supplement and adjustment to the offline planning, the online step ensures that the system states are maintained in the safety set, thus ensuring obstacle avoidance. The online computational efficient QP structure guarantees convenience and scalability in practical implementation. Both the online static and dynamic obstacle avoidance simulations demonstrate the adherence to the safety constraints. Experimental results validate the effectiveness of the proposed method.

This article proposes a robust safety–critical control framework based on high order control barrier function (HOCBF) and disturbance observer (DOB) for control affine systems in the presence of unknown disturbances. Specifically, we apply the proposed framework for the obstacle avoidance of autonomous underwater vehicles (AUVs) to consider the nonlinear characteristics and current disturbance. The system stability is proved to be uniformly ultimately bounded (UUB). The proposed framework first estimates the unknown term of the Lie derivative of HOCBF by employing a first-order high-gain DOB. The bounded property of the DOB estimation error is combined with the Tunable Input-to-State Safe (TISSf) to compensate for the forward invariant condition of CBF. We construct the DOB-TISSf-based robust safety–critical control framework based on quadratic programming to guarantee safety and control performance simultaneously. We show that the safety–critical framework still renders the system UUB. Simulations and field experiments are validated on AUV to avoid obstacles. The results demonstrate that the proposed framework ensures the safety of AUV. Moreover, the framework allows for lower conservativeness of the disturbed system and thus a better tradeoff between system performance and safety.

This letter focuses on the constraint-driven optimal control for multiple Autonomous Underwater Vehicles (AUVs). The different tasks are formulated as multiple constraints on optimization. First, the task goals are formulated by the time-varying control barrier functions. Then, a novel cost-to-constraint transformation method is proposed for time-varying tasks to achieve the high-order constraint-driven task execution under AUV dynamics. The priority of each task is adjusted by the maximum allowed limit of the slack variable, thus relaxing the task constraints and avoiding potential constraint conflicts. Finally, the collision-free optimal energy-aware task execution is achieved by a minimum input norm objective that is incorporated into computationally efficient quadratic programming, which is scalable to different task behaviors. Comparative numerical simulations considering time-varying trajectory tracking and cooperative coverage tasks validate the effectiveness of the proposed method.