Work Machine Control Zones With Real-Time Field Feedback
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Solution Overview
Problem
Current systems for controlling work machines, such as agricultural harvesters, rely on predictive maps generated from a priori data to manage machine operations, but these systems often lack real-time adaptability and accuracy, particularly in dynamic field conditions.
Innovation Solution
The system identifies control zones on thematic maps and adjusts work machine actuators based on sensed positions, using real-time observed and estimated condition values, with a near real-time display showing both actual and predicted conditions to enhance operational control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If predictive maps based on a priori data are used to control work machines, then machine operation can be automated and guided, but the system lacks real-time adaptability and accuracy in dynamic field conditions
Solution Approach 1:
The system performs preliminary actions by collecting a priori data and generating predictive maps before machine operation. These predictive maps provide advance guidance for machine control while the system maintains ability to adapt in real-time through continuous data collection and dynamic updates during operation.
2Extent of automation
If predictive maps based on a priori data are used to control work machines, then machine operation can be automated and guided, but the system lacks accuracy in dynamic field conditions
Solution Approach 1:
The system implements feedback mechanisms by continuously collecting actual field data during machine operation and comparing it with predictive map data. This feedback loop enables real-time corrections and updates to improve measurement precision and accuracy of condition values while maintaining automated operation.
3Manufacturing precision
If control zones are identified and actuators are adjusted based on real-time sensor data, then machine control precision and adaptability improve, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the work area into discrete control zones with specific characteristics. Each zone has predefined actuator settings, allowing precise control while managing complexity through modular zone-based management rather than continuous complex calculations across the entire workspace.
Solution Approach 2:
The system implements local quality by applying different actuator settings and control parameters to different control zones based on their specific characteristics. This allows optimized precision for each local area while maintaining overall system manageability through zone-based differentiation.
Data Source
AI summary
Control zones are identified on a thematic map and work machine actuator settings are identified for each control zone. A position of the work machine is sensed and actuators on the work machine are controlled based on the control zone that the work machine is in, and based upon the actuator settings corresponding to the control zone. The control zone is then divided, on a display, into a harvested portion of the control zone on which an observed condition value is shown, and control zone that has yet to be harvested, on which an estimated value of the condition is shown.