This paper compares existing methods, but it is important to remark that the novelty is how they are structured within the general planning scheme. Hence, there is no need for theoretical demonstration, since they are empirical models, which extract information from the historical data of the missions carried out under supervision. Motivated by the above observations, this work compares several path planning artificial intelligence techniques, such as a data driven models, to resolve a multi-optimization nonlinear problem. Transforming these rules into quantitative constraints to improve the practicality of traffic in the sea environment and optimize results is needed. The COLREGs were created to prevent and avoid collisions between ships, and present requirements, which should be complied with by all vessels, in order to build modules that identify encounter situations, determine the action manner, and assess the collision risk, etc. In 2019, collision was considered the second factor that causes navigation accidents as reported in the annual overview of maritime casualties and incidents by the European Maritime Safety Agency. This wave comes from a resurging significant interest in uncrewed surface vehicles (USVs) and their intelligent motion control navigation is in full swing. This involves increasing innovation and development with a focus on the autonomy of aerial, ground, and underwater vehicles. Maritime transport represents 93% of global trade volume, which highlighted the demand for low-carbon and green transport. To improve both aspects, an energy-efficient new path planning algorithm approach based on AI techniques for computing feasible paths in compliance with the Convention on the International Regulations for Preventing Collisions at Sea (COLREG) rules and taking energy consumption into account according to wind and sea current data is proposed.Ĭlimate change and environmental degradation are existential threats facing the world. Sea current state and wind conditions significantly affect the USV energy consumption becoming the path planning approach key for navigation performance and endurance. This becomes even more challenging for USV technologies propelled by harvesting ocean energy from waves and wind. As a multi-optimization nonlinear problem, it should include computational time, optimal path, and maritime traffic standard procedures. Path planning is a key component of autonomy addressed to obstacle detection and avoidance. Unmanned surface vehicles (USVs) are increasingly used for ocean missions and services aimed for safer, more efficient, and sustainable routine operations.
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