Work-class ROVs equipped with robotic manipulators are extensively used for subsea intervention operations. Manipulators are teleoperated by human pilots relying on visual feedback from the worksite. Operating in a remote environment, with limited pilot perception and poor visibility, manipulator collisions which may cause significant damage are likely to happen. This paper presents a real-time collision detection algorithm for marine robotic manipulation. The proposed collision detection mechanism is developed, integrated into a commercial ROV manipulator control system, and successfully evaluated in simulations and experimental setup using a real industry standard underwater manipulator. The presented collision sensing solution has a potential to be a useful pilot assisting tool that can reduce the task load, operational time, and costs of subsea inspection, repair, and maintenance operations.

Many current and future applications of underwater robotics require real-time sensing and interpretation of the environment. As the vast majority of robots are equipped with cameras, computer vision is playing an increasingly important role it this field. This paper presents the implementation and experimental results of underwater StereoFusion, an algorithm for real-time 3D dense reconstruction and camera tracking. Unlike KinectFusion on which it is based, StereoFusion relies on a stereo camera as its main sensor. The algorithm uses the depth map obtained from the stereo camera to incrementally build a volumetric 3D model of the environment, while simultaneously using the model for camera tracking. It has been successfully tested both in a lake and in the ocean, using two different state-of-the-art underwater Remotely Operated Vehicles (ROVs). Ongoing work focuses on applying the same algorithm to acoustic sensors, and on the implementation of a vision based monocular system with the same capabilities.

The measurement of the motion of a small-scale wave energy device during wave tank tests is important for the evaluation of its response to waves and the assessment of power production. Usually, the motion of a small-scale wave energy converter (WEC) is measured using an optical motion tracking system with high precision and sampling rate. However, the cost for an optical motion tracking system can be considerably high and, therefore, the overall cost for tank testing is increased. This paper proposes a low-cost capture system composed of an inertial measurement unit and ultrasound sensors. The measurements from the ultrasound sensors are combined optimally with the measurements from the inertial measurement unit through an extended Kalman filter (EKF) in order to obtain an accurate estimation of the motion of a WEC.