Autonomous Underwater Vehicle Adaptive Altitude Control Framework to Improve Image Quality

Autonomous underwater vehicles (AUVs) play a pivotal role in the exploration and monitoring of the sea floor. A primary challenge in surveying AUVs is consistently obtaining high-quality optical imagery data. A major cause of quality reduction is turbid water, which both attenuates and scatters light. The effects of turbidity can be minimized by lowering the operational altitude of the AUV, at the cost of increased survey duration and cost. Consequently, before conducting a survey, a trade-off must be made between the risk of acquiring suboptimal images and the additional time required to cover an area. In this research, we develop a computer-vision-based technique and control system that dynamically adjusts the altitude of an AUV based on real-time estimates of turbidity from collected images. Our testing in a simulated environment demonstrates that this system reliably improves the efficiency and quality of image collection.