Assessment of collision avoidance strategies for an underwater transportation system

Abstract

Transportation using multiple autonomous vehicles with detection avoidance capability is useful for military applications. It is important for such systems to avoid collisions with underwater obstacles in an effective way, while keeping track of the target location. In this paper, sensor-based and path-planning methods of external collision avoidance were investigated for an underwater transportation system. In particular, sensor-based wall-following and hard-switching collision avoidance strategies and an offline RRT∗ pathplanning method was implemented on the simulation model of the transportation system of four Hovering Autonomous Underwater Vehicles (HAUVs). Time-domain motion simulations were performed with each method and their ability to avoid obstacles was compared. The hard-switching method resulted in high yaw moments which caused the vehicle to travel towards the goal by a longer distance. Conversely, in the wallfollowing method, the yaw moment was kept to zero. Moreover, the wall-following method was found to be better than the hardswitching method in terms of time and power efficiency. The comparison between the offline RRT∗ path-planning and wallfollowing methods showed that the fuel efficiency of the former is higher whilst its time efficiency is poorer. The major drawback of RRT∗ is that it can only avoid the previously known obstacles. In future, offline RRT∗ and wall following can be blended for a better solution. The outcome of this paper provides guidance for the selection of the most appropriate method for collision avoidance for an underwater transportation system.