Weeding Robot Obstacle Recognition Using Hue Histogram Peaks
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Solution Overview
Problem
Conventional weeding robots require manual calibration of weeding regions using boundary lines, consuming manpower and material resources, and are limited in shape due to corner restrictions, affecting recognition efficiency and accuracy.
Innovation Solution
An obstacle recognition method that utilizes hue and value information to generate a target hue histogram and determine peak information for obstacle detection in weeding regions, eliminating the need for manual boundary calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If boundary lines are buried to calibrate weeding regions, then the weeding region boundaries can be defined, but manpower and material resources are consumed and costs increase
Solution Approach 1:
The patent extracts the boundary calibration process from physical boundary lines and transforms it into image processing operations. The system captures images of the weeding region and uses computer vision algorithms to identify boundaries without requiring physical markers, thereby eliminating the consumption of materials and manpower associated with burying and maintaining boundary lines
Solution Approach 2:
The patent replaces the mechanical system of burying and physically defining boundaries with an optical and computational system. Instead of using physical boundary lines that require manual installation and maintenance, the system uses image capture devices and image processing algorithms to automatically detect and define boundaries, substituting mechanical operations with optical sensing and computational analysis
2Adaptability or versatility
If boundary lines are buried for calibration, then weeding regions can be defined, but the shape is limited due to corner restrictions
Solution Approach 1:
The patent introduces dynamic adaptability to the boundary definition system. Instead of fixed geometric constraints imposed by physical boundary lines, the system dynamically adjusts to any shape by processing images and automatically identifying boundaries based on visual features. This allows the weeding region to take any form without being constrained by corner restrictions or predefined geometries
Solution Approach 2:
The patent changes the fundamental parameters of boundary definition from physical coordinates and geometric constraints to image-based color and intensity thresholds. By adjusting image processing parameters such as hue, saturation, and brightness thresholds, the system can adapt to various shapes and conditions without requiring changes to the physical calibration system
3Measurement precision
If manual boundary calibration is performed, then weeding regions are defined, but recognition efficiency and accuracy of obstacles are reduced
Solution Approach 1:
The patent merges the boundary calibration function with the obstacle recognition function into a single integrated image processing workflow. Instead of performing boundary calibration and obstacle detection as separate manual operations, the system processes the image once to simultaneously identify both boundaries and obstacles, improving efficiency while maintaining or enhancing accuracy through consistent reference frames
Solution Approach 2:
The patent performs preliminary image processing to establish the weeding region boundaries and create a reference framework before obstacle detection begins. This preliminary action of segmenting the weeding region from the image data provides an accurate spatial context that enhances subsequent obstacle recognition, ensuring both high efficiency and precision
Data Source
AI summary
An obstacle recognition method includes the steps of obtaining hue information and value information of a candidate weeding region image; generating a target hue histogram of the candidate weeding region image according to the hue information, and obtaining peak information of the target hue histogram; and determining whether there are obstacles in the candidate weeding region image according to the peak information and the value information. Related apparatus, electronic devices, computer readable storage media, and weeding robots are disclosed.