Autonomous underwater vehicle optimal path planning method for seabed terrain matching navigation

To solve the problem of existing methods of terrain matching having low precision in the areas with small eigenvalues, this work presents an Autonomous Underwater Vehicle optimal path planning method for seabed terrain matching navigation to avoid these areas. The method demonstrates high matching precision on each match area. This method has built the field map and value map that represents obstacle and matching performance, respectively, and the planning algorithm, which includes dynamic matching algorithm, cost function, search length and min-length, second-goal point and dynamic path planing algorithm, was proposed on basic of A star algorithm. Terrain-entropy and terrain-variance-entropy were introduced as criteria in the cost function to represent the matching performance. Then, joint criteria, which were calculated by a Back Propagation Neural Network, and fuzzy criteria were introduced and proved to be feasible through simulation experiments. The path planning method on the basic of fuzzy criteria, in terms of time consumption, was a more suitable method than the one based on joint criteria for the same terrain matching accuracy.