This study proposes a path planning algorithm for marine vehicles based on machine learning. The algorithm considers the dynamic characteristics of the vehicle and disturbance effects in ocean environments. The movements of marine vehicles are influenced by various physical disturbances in ocean environments, such as wind, waves, and currents. In the present study, the effects of ocean currents are the primary consideration. A kinematic model is used to incorporate the nonholonomic motion characteristics of a marine vehicle, and the reinforcement learning algorithm is used for path optimization to generate a feasible path that can be tracked by the vehicle. The proposed approach determines a near-optimal path that connects the start and goal points with a reasonable computational cost when the map and current field data are provided. To verify the optimality and validity of the proposed algorithm, a set of simulations were performed in simulated and actual ocean current conditions, and their results are presented.

This paper addresses the development of an unmanned surface vehicle (USV) system by Team Angry‐Nerds from KAIST for the inaugural Maritime RobotX Challenge competition, which was held on October 20‐26, 2014, in Marina Bay, Singapore. The USV hardware was developed on a catamaran platform by integrating various system components, including propulsion, sensors, computer, power, and emergency systems. The competition comprised five mission tasks: 1) navigation and control, 2) underwater search and report, 3) automatic docking, 4) buoy search and observation, and 5) obstacle detection and avoidance. Onboard intelligence was a key factor for all of the mission tasks which needed to be performed autonomously with no human intervention. Software algorithms for vehicle autonomy were developed, and executable computer codes were implemented and integrated with the developed USV hardware system. This paper describes the development process of the USV system and its application to the competition mission tasks.

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