A Touchless Control Interface for Low-Cost ROVs

In this paper, a fully touchless control interface for low cost remotely operated vehicles (ROVs) is presented. This interface aims to decrease training time, reduce workload, and ensure the operation ergonomics of ROV operators. Fully touchless control interface is achieved by a machine learning (ML) algorithm for ROV operator's face and orientation recognition, and controling the angle of an ROV-based camera. Furthermore, a Leap Motion sensor captures hand gestures and movements, thereby allowing the ROV operator to execute maneuvers and perform other functions (e.g., gripper or lighting control) based on pre-determined hand gestures. Fusion of face and hand gestures allows the operator to control ROV in a fully touchless way. The proposed system is tested in a realistic underwater simulation environment designed specifically for typical tasks that are present in student competitions. Trials with inexperienced operators show that the touchless interface can cut training times, speed up operations, reduce workload, and can provide the operator with a more natural feeling of command and control as well as better ergonomy.