Multi-AUV Formation Reconfiguration Obstacle Avoidance Algorithm Based on Affine Transformation and Improved Artificial Potential Field under Ocean Currents Disturbance

In this paper, the formation obstacle avoidance problem of autonomous underwater vehicles (AUVs) under the disturbances of ocean currents is studied. A variable formation reconfiguration and obstacle avoidance control scheme based on affine transform and the improved artificial potential field (AT-IAPF) is designed, which enable AUVs to avoid both static and dynamic obstacles under external interference, and maintain the desired time-varying formation. Because of the robustness and strong effectiveness of the time-varying control of AT and the obstacle avoidance control law of IAPF. The AT-IAPF algorithm improves the multi-AUV systems' environmental adaptability and obstacle avoidance performance. Using the Lyapunov function's stability constraint guarantees stability of a multi-AUV system. A series of simulation results based on MATLAB verify that AUVs can effectively avoid obstacles with different formation shapes. Obstacle avoidance experiments on bionic robotic fish demonstrate the proposed method's feasibility. Note to Practitioners - This paper was motivated by the problem of formation reconfiguration and obstacle avoidance for AUVs. Still, it also applies to unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The existing formation control methods usually solve the problems of formation acquisition and time-invariant maneuvering, and rarely consider the problem of formation obstacle avoidance. This paper presents a new formation obstacle avoidance method using affine transformation (AT) and improved artificial potential field (IAPF) techniques. We use the IAPF method to plan a possible path for the formation in the obstacle environment. At the same time, the appropriate formation shape is selected according to the obstacle information to better adapt to the environment. The preliminary experiments of two bionic robot fish in near-surface positions show that this method is feasible. During the experiment, UWB is used for positioning, and a Zigbee module is used to communicate and transmit data. But it still needs to solve the problem of underwater communication, and it has yet to be tested on multiple bionic robot fish. In future studies, we will conduct multiple actual AUV formation obstacle avoidance experiments or do 3D formation control experiments underwater.