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Improved RRT-based underwater vehicle rolling planning algorithm capable of effectively improving search efficiency

A technology for underwater vehicles and aircraft, which is applied in three-dimensional position/channel control and other directions, and can solve problems such as meaningless calculation, low expansion efficiency, and slow expansion speed

Active Publication Date: 2019-03-29
HARBIN INST OF TECH AT WEIHAI
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AI Technical Summary

Problems solved by technology

The algorithm itself also has the following defects: (a) The fast search random tree algorithm uses a uniform step size for random expansion, without any biased search, meaningless calculations in open spaces, and low expansion efficiency in narrow areas
In the area where obstacles are widely distributed or without obstacles, the expansion speed is slow when using small step expansion, and the large step expansion can quickly pass through this area; in the complex obstacle distribution area or narrow passage, using large step expansion along the random The direction is easy to generate invalid nodes, resulting in low expansion efficiency

Method used

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  • Improved RRT-based underwater vehicle rolling planning algorithm capable of effectively improving search efficiency
  • Improved RRT-based underwater vehicle rolling planning algorithm capable of effectively improving search efficiency
  • Improved RRT-based underwater vehicle rolling planning algorithm capable of effectively improving search efficiency

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Embodiment

[0119] In order to prove the effectiveness of the proposed rolling path planning algorithm for underwater vehicles based on improved RRT, the proposed algorithm and the rolling path planning algorithm for underwater vehicles based on basic RRT are used for path planning in the same complex area (Fig. 17), The starting point is fixed (63,402) as blue nodes, and 10 target points are randomly set as red nodes. The algorithm parameters are set the same, where Δq is 5% of the diagonal length of the planning area, the number of planning in the local window is 50, and the size of the local window is 10% of the diagonal length of the planning area. The improved RRT algorithm uses Table 1-3 The corresponding relationship between the visibility and the expansion direction set and the expansion step set. Table 1-4 is the comparison of data corresponding to the two algorithms, in which each group of simulation runs 10 times to take the average value, and the data in the table are arranged...

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Abstract

The invention relates to the technical field of path planning, in particular to an improved RRT-based underwater vehicle rolling planning algorithm capable of effectively improving search efficiency.Compared with a basic RRT algorithm, the improved RRT algorithm utilizes node visibility to obtain a node extension direction and the node extension step length through 2-layer selection, the simulation experiment shows that the algorithm is higher in the search efficiency compared to the rolling planning based on the basic RRT, searches the improved RRT algorithm and the basic RRT algorithm in aplurality of complex environments, the experiment data shows that the improved RRT algorithm is less in the search time in the complex environments and fewer in the corresponding node quantity compared to the RRT algorithm.

Description

Technical field: [0001] The invention relates to the technical field of path planning, in particular to an improved RRT-based rolling planning algorithm for underwater vehicles that can effectively improve search efficiency. Background technique: [0002] In the path planning task, the underwater environment information is divided into fully known and partially known situations. For the unknown environmental information, the vehicle needs to obtain it gradually during the movement. The path planning with fully known underwater environment information is called global path planning, and the path planning with partially known underwater environment information is called local path planning. Common global path planning algorithms include visual graph method, evolutionary algorithm, heuristic search algorithm, etc. Common local path planning algorithms include artificial potential field method, path planning algorithm based on fuzzy logic, path planning algorithm based on case s...

Claims

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Application Information

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IPC IPC(8): G05D1/10
CPCG05D1/10Y02T10/40
Inventor 张永健徐阳帆
Owner HARBIN INST OF TECH AT WEIHAI
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