Predictive Material Dynamics Mapping for Mobile Machine Spillage Control
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Solution Overview
Problem
Mobile machines experience material shifting and spillage due to various factors like operation speed, terrain characteristics, and material properties, leading to instability, increased load, and reduced profitability.
Innovation Solution
Generate predictive maps using in-situ sensors and prior or predicted data to model material dynamics, allowing for automated control of mobile machines to mitigate material movement and spillage.
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 movement increase
Solution Approach 1:
The system performs preliminary actions by generating predictive maps before material spillage occurs. In-situ sensors detect material dynamics characteristics (movement, spillage, center of gravity changes) in real-time, and the predictive map generator creates spatial maps showing where spillage is likely to occur based on terrain, machine speed, and material properties. This allows the control system to take preventive actions before spillage happens.
Solution Approach 2:
The system implements continuous feedback by using in-situ sensors to monitor material dynamics characteristics during operation. The sensors detect material movement, spillage, and center of gravity changes, feed this data back to the predictive map generator, which updates the predictive maps in real-time. This closed-loop feedback enables dynamic adjustment of machine operations to prevent spillage while maintaining productivity.
2Productivity
If mobile machines carry larger loads to increase efficiency, then productivity improves, but machine stability deteriorates
Solution Approach 1:
The system predicts center of gravity changes and material distribution patterns before they cause stability issues. By analyzing terrain maps, machine configuration, and material properties in advance, the predictive map generator identifies potential stability problems and allows the control system to adjust machine operations proactively.
Solution Approach 2:
In-situ sensors continuously monitor material distribution and center of gravity changes during operation. This feedback is fed to the predictive map generator, which updates predictions of machine stability. The control system uses this real-time feedback to adjust machine operations, maintain proper load distribution, and prevent stability issues even when carrying large loads.
3Measurement precision
If detailed monitoring of material dynamics is implemented to reduce spillage, then measurement precision improves, but device complexity increases
Solution Approach 1:
The in-situ sensors are designed to perform multiple functions simultaneously. The same sensors detect material movement, spillage, center of gravity changes, and material distribution patterns. This multi-functionality reduces the need for separate specialized sensors for each measurement, thereby reducing overall system complexity while maintaining high measurement precision across all material dynamics characteristics.
Solution Approach 2:
The predictive map generator acts as an intermediary that processes data from multiple sensors and integrates it into unified predictive models. Rather than requiring complex direct control systems for each sensor, the predictive maps serve as an intermediate representation that simplifies the control logic while enabling precise monitoring and control of material dynamics.
Data Source
Figure 1
Figure 2A~2B
Figure 2C
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.