An unmanned ship path planning method based on information entropy field guidance

By using a path planning method guided by information entropy fields, the problems of blind searching and path feasibility of unmanned surface vessels (USVs) in complex water environments are solved. This method enables path optimization and the accumulation of historical experience, thereby improving the efficiency and success rate of path planning for USVs in complex environments.

CN122192334BActive Publication Date: 2026-07-24JIANGSU UNIV OF SCI & TECH
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Patent Information

Application Number
CN202610661338.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-05-14
Publication Date
2026-07-24
Estimated Expiration
2046-05-14

AI Technical Summary

Technical Problem

Existing unmanned surface vessel (USV) path planning algorithms suffer from problems such as blind searching, poor path feasibility and traceability, and lack of historical experience reuse mechanisms in complex water environments. These issues lead to wasted computing resources and potential collision risks, making it difficult to meet real-time and efficiency requirements.

Method used

The path planning method guided by information entropy field is adopted. By constructing an information entropy field to quantify environmental uncertainty, and combining target bias, historical utilization and intelligent sampling strategy guided by information entropy field, multi-stage path optimization is carried out, and a hybrid memory module is constructed to accumulate and utilize historical experience.

Benefits of technology

It significantly improves the convergence speed and path quality of the algorithm, reduces the difficulty of operation and energy consumption, and increases the success rate and planning efficiency in complex environments, making it suitable for scenarios with frequent and repeated planning.

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Abstract

The application provides an unmanned ship path planning method based on information entropy field guidance, and relates to the technical field of unmanned ship navigation. The method first acquires a target region image, labels obstacles, and calibrates the starting point and the ending point of the unmanned ship path; then designs an information entropy value function and gives each position in the target region an initial information entropy value to form an initial information entropy field; iteratively searches the unmanned ship path by using an intelligent sampling strategy combined with an RRT algorithm, and completes initial unmanned ship path planning; optimizes the initial unmanned ship path in four steps to obtain an optimized path, combines a hybrid memory module to accumulate historical path experience, then updates the information entropy value by local enhancement and global attenuation, and outputs the historical optimal unmanned ship path after repeated iteration. The application can comprehensively improve the efficiency, accuracy and practicality of the static obstacle path planning of the unmanned ship in a complex environment.
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