UUV route planning method with necessary points under obstacle environment

Abstract

The invention relates to a UUV route planning method with necessary points under the obstacle environment, belongs to the UUV control technology field and aims to realize the shortest path as UUV starts from a laying point, traverses all the necessary points and then returns to a recovery point. According to the method, under the obstacle environment, estimated route distances between the UUV laying point and the recovery point and between every two of the necessary points are calculated; the laying point is taken as the start point, the recovery point is taken as the terminal point, the sequence of the UUV traversing all the necessary points is planned through a TSP algorithm, the total estimated path of the UUV starting from the laying point, traversing all the necessary point and returning to the recovery point is made to be shortest; after optimization, the route can be acquired on the condition that the total estimated path of the UUV starting from the laying point, traversing all the necessary point, returning to the recovery point and can further avoid all obstacles is shortest. Path cost between two necessary points can be estimated through geometric determination, an ant colony algorithm is further utilized to plan a TSP scheme on the basis of the estimation cost, namely the necessary point traversing sequence, a variable sub goal point ant colony is further utilized to plane a route capable of realizing obstacle avoidance.

Description

technical field [0001] The invention relates to a UUV route planning method, which belongs to the technical field of UUV control. Background technique [0002] At present, there are many literatures on path planning, and most of the planning goals are to find the shortest obstacle-avoiding path from the deployment point to the recovery point that can avoid obstacles. In practical applications, the mission of UUV (unmanned underwater vehicle) may be to return to the recovery point after traversing several necessary points and completing corresponding tasks. This seems to be a TSP problem. However, the TSP problem in the obstacle environment is very different from the traditional TSP problem, because the random distribution of obstacles will cause the path cost between two points to be uncertain, which requires additional processing algorithms. At present, there are not many literatures that consider the necessary points in the obstacle environment, such as the representative...

AI Technical Summary

Problems solved by technology

[0003] Defects of the existing technology: due to the distribution of obstacles, the path cost between two must-pass points is uncertain, and the method of using traditional TSP planning to process intersecting line segments cannot guarantee the optimality of the path, such as figure 1 As shown, according to the traversal order of the original TSP, the path obtained after adding obstacles is unreasonable

The scheme proposed in literature [2] can get the optimal solution, but because it requires a lot of planning, the real-time performance is very poor

The method proposed in literature [3] has no simulation verification, and it is difficult to determine the advantages and disadvantages of the algorithm

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