Path planning method for unmanned ship based on dynamic obstacle environment

Abstract

The invention discloses a path planning method for an unmanned ship based on a dynamic obstacle environment, which is suitable for the fields of lake water quality sampling and path planning. Firstly, the UAV is used to collect images of the lake environment, and then the grid method is used to segment, and the starting point and sampling point are set on the grid map; the improved D*Lite algorithm is used to plan the path of the starting point and sampling point; After the optimal sampling path is obtained, it is smoothed; finally, the unmanned ship moves to the sampling point according to the optimal path and returns. The invention satisfies the path planning requirements of the unmanned ship in the dynamic unknown environment by improving the heuristic function in the D*Lite algorithm and smoothing the path, and effectively solves the situation that the number of extended nodes in the traditional D*Lite algorithm is large, which is extremely Greatly shorten the path planning time and reduce equipment consumption costs.

Description

technical field [0001] The invention mainly belongs to the field of lake water quality sampling and dynamic path planning, in particular to a path planning method which can solve the dynamic obstacle environment. Background technique [0002] With the development of scientific and technological civilization, the impact of human life and work on the environment has become more and more problems, especially the impact on the water environment. Therefore, the sampling and monitoring of the water environment is extremely important. With the improvement of intelligence, most of today's unmanned boats are used to sample water quality. However, the problem of path planning for unmanned ships has always been the focus of many scholars in this field. Path selection in a dynamic environment directly affects the sampling efficiency and cost of cruise ships. Finding a method that can adapt to the unknown environment and quickly plan the optimal sampling path can significantly improve t...

AI Technical Summary

Problems solved by technology

However, the problem of path planning for unmanned ships has always been an important and difficult point of concern for many scholars in this field. The path selection in a dynamic environment directly affects the sampling efficiency and cost of cruise ships.

However, when the environment map is relatively large, the number of nodes that need to be expanded and judged during the reverse search process of the traditional D*Lite algorithm increases sharply, which increases the time complexity of the search and affects the efficiency of path planning.

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