Abstract

Autonomous underwater vehicles (AUVs) are commonly used to conduct complex underwater tasks, such as marine infrastructure overhaul and maintenance, environmental monitoring, oceanographic mapping, and organism capture. These tasks require the ability of an AUV to perform autonomous navigation, especially when communication is limited in the underwater environment. This brief developed a new type of lightweight intervention AUV for autonomous navigation using data from multiple inertial sensors, where multi-sensor error state Kalman filter schemes are preferable to standard Kalman Filters in terms of the AUV’s motion estimates. Concerning target recognition, a color restoration method is provided for degraded underwater images and a You Only Look Once strategy is combined with topological analysis for object detection. In addition, the proposed design is robust in terms of its software components and mechanical structure, which provides a feasible platform for AUV’s secondary development. Experiments of surveying and object manipulation conducted in underwater environments demonstrate the functionality of the entire system and its potential applications in the fields of science and industry.