Visual Guidance Algorithm for AUV Recovery Based on CNN Object Tracking

Abstract

The development of autonomous undersea vehicle recovery technology is the main approach to solve problems pertaining to energy and information transmission and to enhance the underwater detection and concealment capabilities of unmanned systems. In this study, an underwater visual guidance scheme is designed for recovery with funnel-shaped docking stations in an actual environment. Additionally, an improved detect-by-tracking algorithm based on a convolutional neural network(CNN) is proposed. First, the CNN is trained using a docking station dataset to detect the target. Next, the improved tracking algorithm is combined with the position and attitude spatial information to achieve robust tracking. Finally, based on an improved PnP-P3P position and attitude estimation framework, the problem of insufficient observable beacons under a large offset is solved, and the underwater visual guidance workspace is effectively expanded. The beacon array design and algorithm are validated via workspace simulation, and relevant effective workspace indexes are proposed. An optical guidance experiment is performed in a pool, and acousto–optic joint guidance is performed based on an ultrashort baseline in an actual lake test. The feasibility of the proposed framework for engineering is confirmed by the results obtained.

Keywords

• autonomous undersea vehicle
• underwater recovery
• visual guidance
• convolutional neural network
• position and attitude estimation