Control Zone Maps for Real-Time Harvester Actuator Control
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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, which may not accurately reflect real-time conditions, leading to inefficiencies and potential errors in machine operation across different geographic areas.
Innovation Solution
The system identifies control zones on a thematic map and adjusts work machine actuator settings accordingly, displaying near real-time observed and estimated condition values to optimize machine operation by dividing the map into completed and future-processed areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If predictive maps generated from a priori data are used to control work machines, then machine operation can be automated across different geographic areas, but the control accuracy deteriorates because the maps may not accurately reflect real-time conditions
Solution Approach 1:
The system performs preliminary actions by generating predictive maps from a priori data before the work machine arrives at the worksite. These predictive maps provide advance guidance for automated machine operation across different geographic areas, allowing the system to prepare control parameters in advance while maintaining the ability to adapt to real-time conditions when the machine actually operates
Solution Approach 2:
The system implements feedback mechanisms by comparing predicted condition values from the map with actual sensed conditions at the worksite. This feedback loop allows the system to detect deviations between predicted and actual conditions, then adjust control parameters accordingly to maintain accuracy despite using pre-generated predictive maps for automated operation
2Adaptability or versatility
If control parameters are adjusted for different geographic areas, then adaptability of machine operation improves, but system complexity increases due to multiple parameter settings
Solution Approach 1:
The system applies local quality by associating different control parameters with specific geographic locations or control zones on the worksite map. Each location has optimized parameters tailored to local conditions (such as yield variations, terrain characteristics, or crop density), allowing the machine to automatically adapt to different geographic areas without requiring complex manual reconfiguration
Solution Approach 2:
The system achieves universality through a centralized control system that manages multiple control parameters across different geographic areas using a unified approach. The single system handles diverse functions including map generation, parameter storage, location tracking, and automatic parameter selection, eliminating the need for separate control systems for each geographic area and thereby reducing overall system complexity
3Loss of information
If the entire map is displayed in near real-time, then information completeness improves, but processing time and computational load increase
Solution Approach 1:
The system segments the worksite map into multiple control zones with distinct boundaries and characteristics. Each control zone contains specific control parameters and condition data. This segmentation allows the system to process and display information zone-by-zone rather than attempting to process the entire map simultaneously, reducing computational load and processing time while maintaining complete information coverage across all zones
Solution Approach 2:
The system performs preliminary actions by pre-dividing the map into control zones and pre-calculating control parameters for each zone before the work machine arrives. This advance preparation reduces the computational burden during real-time operation, as the system only needs to determine which pre-defined zone the machine is currently in and retrieve the corresponding parameters, rather than calculating everything in real-time
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 near real-time 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.