Optical Sensor Contact Detection for Ground Engaging Tools
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Solution Overview
Problem
Existing systems for automatically controlling ground engaging tools, such as crawlers and motor graders, lack an effective method to determine when the tool is in contact with a surface, which is crucial for precise operation and surface preparation.
Innovation Solution
A control system utilizing an optical sensor coupled to the work vehicle to capture image data, which is processed by an electronic processor applying an artificial neural network to identify contact with the surface, allowing for automatic adjustments in the vehicle's operation based on the analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional contact detection methods are used, then the system is simple, but the measurement precision of surface contact is insufficient
Solution Approach 1:
The patent replaces traditional mechanical contact detection methods with an optical sensing system. The optical sensor captures images of the ground engaging tool and surface, while an artificial neural network processes these images to detect contact. This substitution of mechanical sensing with optical and computational methods enables precise contact detection without requiring complex mechanical measurement devices.
Solution Approach 2:
The patent introduces an artificial neural network as an intermediary between the optical sensor and the control system. The neural network processes the image data captured by the optical sensor, extracting features and determining surface contact status. This intermediary layer enables sophisticated contact detection while keeping the overall system architecture manageable and modular.
2Productivity
If manual control of ground engaging tool is used, then the operation is flexible, but the productivity is reduced
Solution Approach 1:
The patent implements a feedback control system where the optical sensor continuously monitors the ground engaging tool's contact with the surface, and the artificial neural network processes this information in real-time. The control system receives feedback about contact status and automatically adjusts the tool's operation. This closed-loop feedback enables automatic control that maintains flexibility while significantly improving productivity by eliminating manual intervention.
Solution Approach 2:
The system enables the work vehicle to automatically detect and respond to surface contact conditions without continuous manual input. The optical sensor and neural network system allows the vehicle to self-monitor and self-adjust its operation based on real-time contact detection, reducing the need for manual control while maintaining operational flexibility and adaptability.
3Manufacturing precision
If contact detection is not implemented, then the device complexity is low, but the manufacturing precision of surface preparation is insufficient
Solution Approach 1:
The patent replaces complex mechanical measurement and control systems with an optical sensing and computational approach. By using the optical sensor to capture images and the artificial neural network to analyze contact conditions, the system achieves high surface preparation precision without requiring complex mechanical gauges, sensors, or adjustment mechanisms.
Solution Approach 2:
The patent changes the detection parameter from mechanical force or displacement measurements to optical image analysis. By capturing visual information about the ground engaging tool and surface interaction, and processing this information through the neural network, the system achieves precise contact detection and surface preparation control through parameter transformation rather than direct mechanical measurement.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise detection of surface contact, enabling the work vehicle to automatically adjust its operation, improving the accuracy and efficiency of surface preparation tasks.
Implementation Method 1
an optical sensor that is coupled to the work vehicle. The optical sensor is configured to capture image data that includes an implement
Data Source
AI summary
A work vehicle that operates on a surface comprising an implement and an optical sensor. The optical sensor is configured to capture image data that includes the implement. An electronic processor is configured to perform an operation by controllably adjusting a position of the implement relative to the work vehicle, receive image data captured by the optical sensor, apply an artificial neural network to identify whether the implement is in contact with the surface based on the image data from the optical sensor, 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 implement is in contact with the surface, access operation information corresponding to whether the implement is in contact with the surface from a non-transitory computer-readable memory, and automatically adjust an operation of the work vehicle based on the accessed operation information.