Control methods and systems for weeding robots in photovoltaic environments
By generating weeding control decisions through multispectral recognition and multi-sensor obstacle avoidance technology, and optimizing weeding mode switching, the obstacle avoidance and mode switching problems of weeding robots in densely populated photovoltaic panel areas have been solved, improving weeding efficiency and stability.
CN120779939BActive Publication Date: 2026-04-03INNER MONGOLIA UNIV OF TECH
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Patent Information
- Application Number
- CN202510832670.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-20
- Publication Date
- 2026-04-03
- Estimated Expiration
- 2045-06-20
AI Technical Summary
Technical Problem
Existing weeding robots lack obstacle avoidance capabilities in areas with dense photovoltaic panels, and frequently switch weeding modes, resulting in low operating efficiency and unstable results.
Method used
A weed density map is generated by a multispectral recognition module, and a three-dimensional obstacle map is constructed by a multi-sensor obstacle avoidance module. Weeding path analysis and mode switching optimization are performed, and optimized switching parameters are generated to adjust weeding control decisions.
Benefits of technology
It improves weeding efficiency and operational reliability, solves the problems of insufficient obstacle avoidance and frequent mode switching, and achieves efficient and stable weeding results.
✦ Generated by Eureka AI based on patent content.
Abstract
This invention discloses a control method and system for a weeding robot suitable for photovoltaic environments, belonging to the field of intelligent control technology. The method includes: when the weeding robot autonomously moves to a starting point, it scans the gaps between photovoltaic panels, identifies weed areas based on the scanned images, and generates a weed density map; simultaneously collects obstacle data to construct a three-dimensional obstacle map; performs weeding path analysis and dual-mode weeding unit weeding parameter analysis to generate weeding control decisions; based on the weeding control decisions, it analyzes the frequency of weeding mode switching, establishes unstable switching intervals caused by frequent switching, optimizes stable switching of the weeding mode, and generates optimized switching parameters; after adjusting the weeding control decisions with the optimized switching parameters, the results are input to the control terminal of the weeding robot for weeding control. This solves the technical problem of low weeding efficiency in existing weeding robots, achieving the technical effect of improving weeding efficiency.
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