Predictive Machine Characteristic Mapping for Slope-Aware Harvester Control
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Solution Overview
Problem
Agricultural harvesters face performance degradation due to topographic features such as slopes, which affect pitch, roll, stability, and material distribution, leading to issues like grain loss and quality variation.
Innovation Solution
Generate a predictive machine characteristic map using in-situ sensors and prior data to model relationships between topographic characteristics and machine performance, enabling automated control adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the harvester operates on sloped terrain, then harvesting coverage is maintained, but machine performance and stability deteriorate due to pitch and roll effects
Solution Approach 1:
The system performs preliminary mapping of topographic characteristics (slopes, elevations) before harvesting operations. This advance knowledge allows the control system to pre-adjust machine settings and operational parameters when approaching challenging terrain, maintaining performance while continuing harvesting coverage.
Solution Approach 2:
The harvester uses its own in-situ sensors to detect real-time pitch and roll conditions, and the control system automatically adjusts operational parameters without external intervention. This self-adjusting capability maintains harvesting performance on sloped terrain without requiring operator input or external support systems.
2Reliability
If the operator manually adjusts control upon encountering slopes, then machine performance is maintained, but response time and operational efficiency are reduced
Solution Approach 1:
The system continuously monitors topographic conditions using in-situ sensors and feeds this information back to the control system in real-time. The control system automatically adjusts operational parameters based on this feedback loop, eliminating the delay associated with manual operator detection and adjustment of slope conditions.
Solution Approach 2:
The patent replaces manual operator mechanical adjustment with an automated electronic control system that uses sensors and algorithms to detect and respond to topographic changes. This substitution of manual mechanical control with automated sensor-based control dramatically reduces response time while maintaining performance.
3Loss of information
If traditional mapping methods are used, then map data is obtained, but real-time machine characteristic prediction is insufficient
Solution Approach 1:
The system merges traditional pre-harvesting topographic maps with real-time in-situ sensor data collected during harvesting operations. This combination creates a comprehensive dataset that provides both complete spatial coverage and accurate real-time machine characteristic predictions, overcoming the limitations of either method alone.
Solution Approach 2:
The mapping system transitions from static pre-harvesting maps to dynamic real-time mapping that continuously updates machine characteristic predictions based on current operational conditions. This dynamic approach maintains complete spatial information while providing accurate predictive data for real-time control adjustments.
Data Source
AI summary
One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.