High-Speed Obstacle Avoidance of a Large-Scale Underactuated Autonomous Underwater Vehicle Under a Finite Field of View

This paper addresses the problem of high-speed waypoint tracking and real-time obstacle avoidance for large-scale underactuated autonomous underwater vehicles (AUVs) in the vertical plane. Specifically, a robust nonlinear model predictive control (RNMPC) scheme is proposed, considering different types of constraints including the scale of the AUV, the finite field of view of the sensor, the input saturation, the physical limits on system state, and the influence of the vertical underactuated velocity. To navigate in the completely unknown environment with nonconvex obstacles, a dynamic sensing and collision avoidance scheme is proposed so that the collision avoidance can be properly formulated into convex constraints in the RNMPC optimization problem. Recursive feasibility and closed-loop stability are proved rigorously. Through the high-fidelity simulations with graph and data visualization techniques, the proposed algorithm has higher waypoint tracking accuracy, safer obstacle avoidance ability, and better multiple constraints handling capability than the existing dynamic virtual AUV (DVA) technique.
Note to Practitioners
—This article was motivated by the problem of high-speed waypoint tracking and obstacle avoidance for large-scale underactuated autonomous underwater vehicles (AUVs) in an unknown environment. In practical engineering, the obstacles’ useful information (e.g., shapes and positions) cannot be directly reflected by the raw data (distance or acoustic image) of multi-beam sonars. And the multi-beam sonars are also limited by detection range and angle. Another practical problem is that such type of AUV suffers from different constraints including the scale of the vehicle, the physical limits on system poses and velocities. In addition, the influence of the underactuated velocity cannot be ignored due to the high speed of the vehicle. Considering the above practical engineering problems, we design a robust nonlinear model predictive control (RNMPC) scheme from the kinematic level of the AUV to achieve high-speed waypoint tracking and obstacle avoidance. And then we developed a dynamic sensing and collision avoidance scheme to formulate nonconvex collision avoidance into convex constraints in the RNMPC optimization problem. We demonstrate the effectiveness of the proposed control method in a high-fidelity simulation environment with large size terrain and shipwrecks as obstacles. In this environment, the AUV can dynamically read the distance data from obstacles to the sensor through the virtual multi-beam sonar equipped in the head of the vehicle, which can simulate the actual engineering experiment with high-fidelity. This work could be applied to other underactuated systems, such as unmanned aerial vehicles, unmanned surface vehicles, etc. In future research, we will consider more complex practical working cases, such as the presence of dynamic obstacles in an unknown environment and the disturbance of current.