Row Vision Edge Detection for GPS-Limited Tractor Steering
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Solution Overview
Problem
Existing automated vehicle systems face challenges in reliably detecting edges between surfaces, particularly in farming environments, due to limitations in GPS reliability, processing power, and accuracy of computer vision systems.
Innovation Solution
A row vision system that uses edge detection models and operator input to identify candidate edges between surfaces, weighting them based on distance from user-input locations, and selecting the best fit edge to modify vehicle operations such as steering direction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If GPS information is used for automated navigation, then the vehicle can receive wireless positioning data, but the reliability deteriorates due to fading, shadowing, and interference from transmitters
Solution Approach 1:
The patent introduces visual markers as an intermediary medium between the vehicle and the environment. These markers are detected by cameras to provide positioning and navigation information, serving as a reliable alternative to GPS when wireless signals are unreliable. The markers act as a mediator that translates environmental features into actionable navigation data.
2Measurement precision
If computer vision systems fully classify objects and boundaries, then navigation accuracy may improve, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent extracts only the essential visual features needed for navigation - specifically edges between surfaces - rather than performing full classification of all objects and boundaries. By focusing extraction on edge detection between different surface types (e.g., soil and crops), the system achieves sufficient navigation accuracy with minimal processing time and computational resources.
3Extent of automation
If embedded controllers process all vision data, then automated operation can be controlled onboard, but processing power and computational resources are limited causing delays
Solution Approach 1:
The patent extracts only the critical edge information from visual data rather than processing complete images or performing comprehensive object classification. This extraction approach reduces computational load on embedded controllers, enabling onboard automated operation within limited processing power constraints while maintaining timely response.
4Power
If mobile devices are used for automated operation, then processing power improves over embedded controllers, but delays still occur due to processing and power constraints
Solution Approach 1:
The patent extracts only edge information between surfaces rather than performing full image processing or object classification. This minimal extraction approach allows mobile devices to process navigation data quickly, reducing processing delays while utilizing their available processing power efficiently.
Data Source
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AI summary
A row vision system modifies automated operation of a vehicle based on edges detected between surfaces in the environment in which the vehicle travels. The vehicle may be a farming vehicle (e.g., a tractor) that operates using automated steering to perform farming operations that track an edge formed by a row of field work completed next to the unworked field area. A row vision system may access images of the field ahead of the tractor and apply models that identify surface types and detect edges between the identified surfaces (e.g., between worked and unworked ground). Using the detected edges, the system determines navigation instructions that modify the automated steering (e.g., direction) to minimize the error between current and desired headings of the vehicle, enabling the tractor to track the row of crops, edge of field, or edge of field work completed.