Predictive Material Dynamics Mapping for Spill-Stable Farm Machines
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Solution Overview
Problem
Mobile machines face challenges in managing material dynamics such as movement and spillage due to factors like operation speed, terrain characteristics, and material properties, leading to instability, increased load, waste, and reduced profitability.
Innovation Solution
Agricultural systems generate predictive material dynamics maps using in-situ sensors and historical data to model relationships between terrain, speed, crop moisture, and fill level, enabling control of mobile machines to prevent material spillage and movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mobile machines operate at higher speeds to increase productivity, then productivity improves, but material spillage and instability increase
Solution Approach 1:
The system performs preliminary sensing of material dynamics characteristics and predictive mapping before material spillage occurs. The in-situ sensors detect material movement trends and the predictive map generator creates forecasts of future material behavior, allowing the control system to take preventive action by adjusting machine speed or material containment settings before spillage happens.
Solution Approach 2:
The system implements continuous feedback by using in-situ sensors to monitor material dynamics characteristics in real-time during operation. The sensed data is fed back to the predictive map generator and control system, which continuously adjust operational parameters to maintain material stability while optimizing productivity, creating a closed-loop control mechanism.
2Productivity
If mobile machines carry larger material loads to increase efficiency, then productivity improves, but instability and material movement increase
Solution Approach 1:
The system dynamically adjusts operational parameters based on real-time material dynamics characteristics. As material load and stability conditions change during operation, the in-situ sensors continuously monitor material behavior and the control system adapts speed, acceleration, and containment settings to maintain optimal stability while maximizing transport capacity.
Solution Approach 2:
The system changes operational parameters such as speed, acceleration rates, and material containment settings based on sensed material dynamics characteristics. When material instability is detected, the control system modifies these parameters to restore stability, allowing the machine to safely operate with larger loads by dynamically adjusting conditions rather than maintaining fixed parameters.
3Stability of the object's composition
If predictive mapping systems are implemented to reduce material spillage, then material stability improves, but device complexity increases
Solution Approach 1:
The predictive mapping system is integrated directly into the mobile machine's existing sensor and control infrastructure. The in-situ sensors utilize the machine's own operational data and environment information to generate predictive maps, and the control system automatically applies these predictions without requiring external intervention or complex additional hardware, allowing the system to serve itself.
Data Source
AI summary
One or more information maps are obtained by an agricultural system. The one or more information maps map one or more characteristic values at different geographic locations in a worksite. An in-situ sensor detects a material dynamics characteristic value as a mobile machine operates at the worksite. A predictive map generator generates a predictive map that predicts a predictive material dynamics characteristic value at different geographic locations in the worksite based on a relationship between the values in the one or more information maps and the material dynamics characteristic value detected by the in-situ sensor. The predictive map can be output and used in automated machine control.