A Novel Path Planning Method Based on Extreme Learning Machine for Autonomous Underwater Vehicle

Most existing path planning algorithms focus on generating piecewise linear functions, while the smoothness of the path is usually ignored. Route stability and energy saving are key issues for an autonomous underwater vehicle (AUV) working in the underwater environment. To ensure that AUV can pass through the target area steadily and safely, a path smoother is required. This paper puts forward a novel path planning method based on extreme learning machine (ELM), which generates a smooth and safe path at a quite fast speed. First a Voronoi preprocessor is employed to label the obstacles in the map and generate a rough path connecting the initial and the goal positions. Due to the property of nonlinear separating surface as well as fast learning speed, ELM is then applied to regenerate and smooth the path to ensure that the vehicle drives automatically and safely. Related experiments also validate the performance of our proposed method as expected. More analysis and some possible limitations are also discussed.