Docking Experiment in Dark Environments Using Active/Lighting Marker and HSV Correlation

Abstract

Recharging ability with underwater docking function would be a first primal step conducted to enable the AUV to operate independently of a surface vessel for extended periods. Therefore, the role of docking operation came in picture not only for battery recharging application but also other novel applications such as sleeping under mother ship, or new mission up and down loading. Moreover, docking capacity can be extended to provide navigation for other underwater vehicles on the way of their mission too. However, there are many challenging issues in achieving these applications that request high accuracy and robustness against disturbances that are provided by the underwater environment. The most challenging and unavoidable problems in sensing sphere for sea operations are, we think, turbidity and light changing. Turbidity is defined as cloudiness in a liquid caused by the presence of suspended particles that scatter and absorb light. Since underwater battery recharging are supposed as a first step to realize a full autonomous/intelligent robot, the deep-sea docking experiments cannot avoid turbidity and low light environment. In previous studies, we had conducted sea docking experiments using a passive (not lighting) marker and image-evaluation function based on only hue information, limiting its operational environment in lower turbid sea with sunshine. Whereas in this study, to improve our system removing above defects, we newly designed an active - light emitting - 3D marker and a fitness function determined by HSV color components to improve the performance of the system especially in a more turbid environment. The advantage of using an active 3D marker and HSV-evaluated function is to be thought as being tolerable and seeable despite clipped whites and scattered light on the camera images caused by turbidity. Additionally, we conducted the docking experiments to verify the robustness of the proposed approach against turbidity and compared recognition results between the previous method and the improved method.