Fork Alignment Control Using Optical Sensing and AI
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Solution Overview
Problem
There is a need for an improved system and method to determine when a ground engaging tool, such as a pair of forks on a wheel loader or skid steer, is aligned with an object to be moved, as existing methods lack precision and efficiency in this alignment process.
Innovation Solution
A method and control system that utilize an optical sensor to capture image data of the forks and object, process it with an electronic processor, and apply an artificial neural network to identify alignment, adjusting the vehicle's operation based on this information to ensure proper alignment for moving the object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual alignment methods are used for forks and objects, then the system complexity is low, but the alignment precision and operational efficiency are insufficient
Solution Approach 1:
The patent replaces manual visual alignment methods with an automated optical sensing system. The optical sensor captures images of the forks and object, the electronic processor analyzes the image data to determine alignment status, and the system provides automated guidance. This substitution of mechanical/manual alignment with an optical-electronic system directly resolves the contradiction by significantly improving alignment precision while accepting increased system complexity as a necessary trade-off for enhanced measurement capability.
2Productivity
If automated alignment systems are implemented, then alignment precision improves, but the device complexity increases
Solution Approach 1:
The patent implements automated alignment by replacing manual alignment operations with an electronic system comprising an optical sensor, electronic processor, and control unit. This automation eliminates time-consuming manual alignment procedures, directly improving operational efficiency. The system complexity increase is justified by the substantial gains in productivity and reduced operational time.
Solution Approach 2:
The system incorporates self-service capabilities through automated image capture, processing, and analysis. The optical sensor automatically captures images, the electronic processor automatically analyzes alignment status, and the system automatically provides guidance information without requiring constant operator intervention. This self-service automation improves productivity while making the complexity management more efficient.
3Reliability
If visual inspection methods are used to determine fork alignment, then the system remains simple, but the reliability and safety of object movement are compromised
Solution Approach 1:
The patent replaces unreliable manual visual inspection with a reliable optical sensing and electronic processing system. The optical sensor captures precise images of the forks and object, the electronic processor objectively analyzes alignment status without human error, and the system provides reliable guidance. This substitution dramatically improves alignment reliability and safety, with the increased control system complexity being a necessary investment for enhanced operational safety and reduced risk of damage.
Data Source
AI summary
A work vehicle comprising a pair of forks and an optical sensor. The optical sensor is configured to capture image data that includes the pair of forks and a moveable object. An electronic processor is configured to perform an operation by controllably adjusting the pair of forks, receive image data captured by the optical sensor, apply an artificial neural network to identify whether the pair of forks are aligned for moving the moveable object based on the image data, wherein the artificial neural network is trained to receive the image data as input and to produce as the output an indication of whether the pair of forks are aligned for moving the moveable object, access operation information corresponding to whether the pair of forks are aligned for moving the moveable object from a non-transitory computer-readable memory, and automatically adjust an operation of the work vehicle based on the operation information.