Visual perception digital twin cotton weeding robot identification obstacle avoidance method

By establishing a digital cotton and weed database and twin virtual scenes, combined with the MADPPO model and the yolov8 weed recognition model, the problem of inaccurate weeding strategies of weeding robots in dynamic time-varying environments was solved, and efficient autonomous obstacle avoidance and accurate weeding were achieved.

CN120779946APending Publication Date: 2025-10-14XINJIANG UNIVERSITY
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Patent Information

Application Number
CN202510919102.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-04
Publication Date
2025-10-14

AI Technical Summary

Technical Problem

Existing weeding robots find it difficult to quickly adapt and make correct and accurate weeding strategies in dynamic, unstructured, and time-varying environments, especially due to the complexity and randomness of image information caused by changes in the direction and intensity of sunlight.

Method used

By establishing a digital cotton and weed database, building a twin cotton and weed virtual scene, using cloud servers and virtual simulation platforms for real-time data communication, combining machine learning algorithms and agronomic knowledge base, constructing a time-varying digital cotton field scene, using the MADPPO model for multi-agent strategy autonomous obstacle avoidance, combining the yolov8 weed recognition model and channel attention mechanism for image processing, and optimizing the weeding path.

Benefits of technology

It improves the consistency between virtual scenes and real environments, enhances the weeding accuracy and autonomous obstacle avoidance capabilities of the weeding robot, and enables it to adapt to complex environments with dynamic lighting changes.

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Abstract

The invention belongs to the technical field of agricultural robots, and discloses a visual perception digital twinborn cotton weeding robot identification obstacle avoidance method, which comprises the following specific steps: step 1, establishing a digital cotton and weed database, cotton field image information and cotton field environment information of real scenes in the same area under the condition of changing illumination in different time periods of the whole day are collected through a data collection system, and timestamps during collection and collected real-time data are transmitted to a cloud server. Through mutual cooperation of the data acquisition system, the cloud server and the virtual simulation platform, a cotton and weed virtual scene is established, through simulation of a cotton field and a weeding robot on the simulation platform, time-varying digital scene construction is perfected, and the time-varying digital scene construction efficiency is improved. The problem that an existing digital scene is not perfect in reduction of the time-varying process of the influence of illumination on the cotton field in the real environment is solved, and therefore the consistency of the virtual scene and the real environment and the weeding accuracy are further improved.
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