Autonomous Farming Vehicle Tilling Assembly Malfunction Detection
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Solution Overview
Problem
Existing autonomous farming systems lack effective real-time monitoring for malfunctions in tilling assemblies, leading to reduced tilling quality and potential equipment damage due to detached components or debris accumulation, which are difficult to detect under varying environmental conditions.
Innovation Solution
A detection system utilizing machine-learned models and sensors, such as cameras and thermal sensors, to continuously monitor the tilling assembly for malfunctions like loose or missing shanks, plugged components, and debris, providing real-time data analysis to classify operational and malfunctioning components and adjust the autonomous routine accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring of tilling assembly is used, then system complexity is reduced, but detection precision and rate of malfunctions decreases
Solution Approach 1:
The patent replaces manual visual inspection with automated image capture systems (cameras) and thermal sensing, substituting mechanical/physical monitoring methods with optical and thermal detection systems. This enables continuous automated monitoring of tilling assembly components without human intervention, significantly improving detection precision while accepting increased system complexity through integrated sensors and processing systems.
Solution Approach 2:
The system creates visual copies (images) of the tilling assembly components using cameras and thermal imaging. These image copies are then analyzed by machine learning models to detect malfunctions such as missing sweeps, loose shanks, or debris accumulation. This copying approach allows detailed examination without physical contact or disruption to the tilling operation.
2Productivity
If monitoring rate is increased to detect malfunctions faster, then productivity is improved, but use of energy increases
Solution Approach 1:
The system implements periodic monitoring at strategically determined intervals rather than continuous monitoring. The machine learning model processes images at rates optimized to detect malfunctions promptly while allowing the imaging and processing systems to operate in periodic cycles, reducing overall energy consumption while maintaining high detection rates for critical issues.
Solution Approach 2:
The system performs preliminary analysis of image data using machine learning models that can quickly identify normal versus abnormal conditions. By preprocessing and filtering data in advance, the system can rapidly respond to actual malfunctions without requiring sustained high-energy processing, thus improving detection responsiveness while managing energy consumption.
3Reliability
If machine-learned models are used to detect malfunctions, then reliability of tilling operation is improved, but device complexity increases
Solution Approach 1:
The machine learning models are trained on extensive datasets of normal and malfunctioning tilling assembly conditions, enabling the system to autonomously detect and classify issues without human intervention. The system self-calibrates and improves its detection capabilities through accumulated data, enhancing operational reliability while the automated nature reduces the need for complex manual monitoring protocols.
Solution Approach 2:
The machine learning model is designed to detect multiple types of malfunctions (missing sweeps, loose shanks, debris accumulation, improper depth) using a single integrated detection system. This multi-functional approach improves operational reliability across various failure modes while consolidating what could otherwise require multiple separate detection systems, thereby managing overall complexity.
4Measurement precision
If continuous monitoring is performed under all environmental conditions, then detection precision is maintained, but loss of energy increases
Solution Approach 1:
The system dynamically adjusts its monitoring and processing intensity based on environmental conditions and detected anomalies. During normal operation under favorable conditions, monitoring may operate at lower intensity. When malfunctions are detected or environmental conditions deteriorate (affecting image quality), the system increases monitoring frequency and processing depth, maintaining detection precision while optimizing energy usage and reducing unnecessary monitoring time.
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
Enhances the detection rate and accuracy of malfunctions, allowing for timely resolution and improving the overall quality of autonomous farming routines by continuously monitoring and adapting to environmental conditions, reducing equipment damage and maintaining tilling efficiency.
Implementation Method 1
a monochrome camera captures light reflected off of reflective markers, each marker coupled to a tilling shank
Implementation Method 2
accesses thermal sensor data corresponding to and representative of each of a set of below-ground tilling sweeps
Data Source
AI summary
A detection system detects malfunctions in an autonomous farming vehicle during an autonomous routine using one or more models and data from sensors coupled to the autonomous farming vehicle. The models may include machine-learned models trained on the sensor data and configured to identify objects indicative of an operational or malfunctioning component within a tilling assembly such as a tilling shank or sweep. Additionally, a machine-learned model may be trained on sensor data to detect whether debris has plugged the tilling assembly of the autonomous farming vehicle. In response to detecting a malfunction or a plug, the detection system may modify the autonomous routine (e.g., pausing operation) or provide information for the malfunction to be addressed (e.g., the likely location of a malfunctioning sweep that has detached from the tilling assembly).