The adoption of a robust collision avoidance module is required to realise fully autonomous Unmanned Surface Vehicles (USVs). In this work, collision detection and path planning methods for USVs are presented. Attention is focused on the difference between local and global path planners, describing the most common techniques derived from classical graph search theory. In addition, a dedicated section is reserved for intelligent methods, such as artificial neural networks and evolutionary algorithms. Born as optimisation methods, they can learn a close-to-optimal solution without requiring large computation effort under certain constraints. Finally, the deficiencies of the existing methods are highlighted and discussed. It has been concluded that almost all the existing method do not address sea or weather conditions, or do not involve the dynamics of the vessel while defining the path. Therefore, this research area is still far from being considered fully explored.
Kalman filter has been successfully applied to fuse the motion capture data collected from Kinect sensor and a pair of MYO armbands to teleoperate a robot. A new strategy utilizing the vector approach has been developed to accomplish a specific motion capture task. The arm motion of the operator is captured by a Kinect sensor and programmed with Processing software. Two MYO armbands with the inertial measurement unit embedded are worn on the operator's arm, which is used to detect the upper arm motion of the human operator. This is utilized to recognize and to calculate the precise speed of the physical motion of the operator's arm. User Datagram Protocol is employed to send the human movement to a simulated Baxter robot arm for teleoperation. In order to obtain joint angles for human limb utilizing vector approach, RosPy and Python script programming has been utilized. A series of experiments have been conducted to test the performance of the proposed technique, which provides the basis for the teleoperation of simulated Baxter robot.
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