Hough Transform Row Guidance for Agricultural Equipment
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Solution Overview
Problem
Existing GPS navigation systems for agricultural equipment require initialization operations that can be tedious and error-prone, especially when row locations were not recorded at planting or are unavailable, leading to higher costs and potential errors in navigation.
Innovation Solution
A computer vision-based navigation system using a slope-intercept Hough transform (SLIHT) to determine track-angle error and cross-track distance, allowing for effective row guidance without the need for manual initialization, by analyzing sensor data from cameras mounted on equipment to identify crop rows and furrows and adjust steering accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS initialization operation is used to determine row positions, then navigation accuracy is improved, but the process becomes tedious and error-prone when row locations were not recorded at planting
Solution Approach 1:
The system uses the agricultural equipment itself to capture images and extract row features during normal operation, eliminating the need for separate initialization operations. The equipment's own movement and sensors are leveraged to automatically establish navigation references without requiring manual intervention or pre-recorded data.
Solution Approach 2:
The patent replaces the manual GPS initialization process with an automated computer vision system using cameras and image processing algorithms. The mechanical/manual operation of recording row positions is substituted with optical sensing and automated feature extraction through the Hough transform.
2Reliability
If manual initialization is performed to record row positions, then navigation reliability is improved, but time consumption and potential for human error increase
Solution Approach 1:
The system performs row feature extraction and vanishing point determination continuously during equipment operation rather than requiring a separate preliminary initialization phase. The navigation system is established incrementally as the equipment moves through the field, converting a pre-operation task into an ongoing automated process.
Solution Approach 2:
The system uses the agricultural equipment itself to capture images and extract row features during normal operation, eliminating the need for separate initialization operations. The equipment's own movement and sensors are leveraged to automatically establish navigation references without requiring manual intervention or pre-recorded data.
3Adaptability or versatility
If computer vision with Hough transform is used to determine row positions, then the need for initialization data is eliminated, but the complexity of the navigation system increases
Solution Approach 1:
The patent introduces the Hough transform as an intermediary mathematical tool that bridges the gap between raw image data and meaningful row position information. This intermediary technique automatically extracts linear features from images, enabling the system to determine row positions without relying on pre-recorded initialization data or complex manual procedures.
4Ease of operation
If GPS units are used to record movement during planting, then subsequent navigation is facilitated, but costs increase and the system requires prior recording operations
Solution Approach 1:
The system uses the agricultural equipment itself to capture images and extract row features during normal operation, eliminating the need for separate initialization operations. The equipment's own movement and sensors are leveraged to automatically establish navigation references without requiring manual intervention or pre-recorded data.
Solution Approach 2:
The patent replaces the mechanical/manual operation of recording row positions with GPS units with an automated optical sensing system. Cameras mounted on the equipment capture images of the field, and image processing algorithms automatically extract row positions, replacing the need for GPS-based recording operations.
Data Source
AI summary
Systems and techniques for row guidance parameterization with Hough transform are described herein. An electronic representation of a field (ERF) can be received. The ERF can include a set of feature sets including one of a set of crop row features or a set of furrow features. A first parameter space can be produced by applying a slope-intercept Hough transform (SLIHT) to members of a feature set. Peaks in the first parameter space can be identified. A second parameter space can be produced by application of the SLIHT to the peaks. A vanishing point can be calculated based on a vanishing point peak in the second parameter space. A track-angle error can be calculated from the vanishing point.