Agricultural Row Steering Sensor Fusion for Obscured Crop Guidance
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Solution Overview
Problem
Agricultural machines face challenges in accurately guiding along crop rows due to unreliable sensor information, often obscured by environmental factors such as tall crops or weeds, leading to deviations from the intended path and potential damage to crops.
Innovation Solution
A system that combines and selectively switches between multiple sensors, such as vision and radar, to enhance the reliability and accuracy of guidance parameters by using confidence values to determine the best sensor data for controlling the agricultural machine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single sensor system is used for guidance, then the device complexity is low, but the reliability of guidance parameters deteriorates due to environmental interference
Solution Approach 1:
The patent combines multiple sensor systems (e.g., vision sensors, radar, LIDAR, GPS) into a unified guidance system. The controller receives and processes data from all sensors, fusing their information to generate guidance parameters. This merging approach ensures that if one sensor fails or provides unreliable data due to environmental conditions, other sensors can compensate, thereby improving the overall reliability of guidance parameters while accepting increased system complexity.
Solution Approach 2:
The system dynamically changes operational parameters by selecting different sensor combinations and data processing methods based on environmental conditions. The controller adjusts which sensors are active and how their data is weighted according to current field conditions (e.g., crop height, weather, terrain), allowing the system to maintain reliable guidance parameters across varying environments without requiring a completely different system configuration.
2Reliability
If multiple sensors are combined to improve reliability, then the reliability of guidance parameters improves, but the device complexity increases
Solution Approach 1:
The controller is designed as a universal processing unit that can handle data from multiple different sensor types using the same fundamental processing algorithms. The system employs multi-functional sensor assemblies that can detect various characteristics (position, orientation, environmental conditions) and the controller adapts its processing based on which sensors are available, reducing the need for sensor-specific complex processing logic and thereby managing device complexity more effectively.
Solution Approach 2:
The controller acts as an intermediary that standardizes and harmonizes data from different sensor types. It implements a common data interface and processing framework that translates diverse sensor outputs into unified guidance parameters, reducing the complexity burden of integrating multiple sensors by providing a single point of coordination rather than requiring direct integration between all sensor pairs.
3Productivity
If sensor data is used in obscured conditions, then the productivity is maintained, but the measurement precision deteriorates due to tall crops or weeds
Solution Approach 1:
The system dynamically adapts its sensor selection and data processing based on real-time environmental conditions detected by the sensors. When crops or weeds are detected to be obscuring the view of certain sensors, the controller automatically adjusts by weighting data from unobscured sensors more heavily or switching to alternative sensing methods, thereby maintaining measurement precision and enabling continuous productive operation without manual intervention.
4Productivity
If automated guidance is implemented, then the productivity increases, but the loss of information increases when sensors provide unreliable data
Solution Approach 1:
The system implements continuous feedback loops where the controller monitors the quality and reliability of data from each sensor in real-time. When unreliable data is detected (e.g., from obscured sensors), the feedback mechanism triggers automatic adjustments in data processing, sensor selection, or system alerts to the operator. This feedback ensures that decisions are based on accurate information while maintaining automated operation, preventing information loss that would otherwise compromise productivity.
Data Source
AI summary
A row steering system of an agricultural machine is provided. The row steering system includes a first sensor assembly configured to detect a first orientation of the agricultural machine relative to a path reference in a field using a first sensor configured to measure a first characteristic. The system also includes a second sensor assembly configured to detect a second orientation of the agricultural machine using a second sensor configured to measure a second characteristic. The system further includes a control module including a first evaluation module to obtain a first confidence in the detected first orientation, a second evaluation module to obtain a second confidence in the detected second orientation, and a selector module to selectively provide one or more of the detected first orientation or the detected second orientation to a machine controller of the agricultural machine based on the first and second confidences.