Online Calibration of Earthmoving Vehicle Control Surfaces
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Solution Overview
Problem
Current autonomous control systems for earth moving vehicles face challenges in adapting to changing soil conditions and varying models of earth moving vehicles, due to complex control surface layouts and actuation methods, which hinders efficient autonomous operation.
Innovation Solution
An autonomous earth moving system that integrates sensors to record environmental and vehicle data, uses machine learning models for online learning to select optimal tool paths and control signals, and calibrates based on real-time responses to adapt to soil parameters and vehicle characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional control systems are used for earth moving vehicles, then the system structure is simple, but the adaptability to changing soil conditions and different vehicle models is poor
Solution Approach 1:
The control system dynamically adapts to changing soil conditions and different vehicle models by continuously learning from operational data. The system modifies its control parameters in real-time based on sensor feedback about soil properties and vehicle response, transforming a static control system into a dynamic one that evolves with operating conditions.
Solution Approach 2:
The system changes control parameters such as actuation forces, joint velocities, and control gains based on detected soil conditions and vehicle model characteristics. By adjusting these parameters adaptively, the system achieves versatility across different soil types and vehicle models without requiring completely different control architectures for each scenario.
2Productivity
If manual operation is used for earth moving vehicles, then the adaptability to changing conditions is good, but the operation cost is high
Solution Approach 1:
The control system performs self-calibration and self-adjustment by automatically learning the relationship between control inputs and vehicle responses. Through online learning mechanisms, the system independently adapts to different vehicle models and soil conditions without requiring manual reconfiguration or continuous operator intervention, enabling autonomous operation while maintaining high adaptability.
3Manufacturing precision
If calibration data from one earth moving vehicle model is applied to another, then the setup time is reduced, but the control precision deteriorates due to different control surface layouts and actuation characteristics
Solution Approach 1:
The system implements continuous feedback during operation to monitor the actual relationship between control signals and vehicle responses. By comparing expected behavior with actual behavior, the system detects deviations caused by model differences and automatically adjusts calibration parameters to compensate, maintaining precision without requiring extensive manual calibration for each vehicle model.
Solution Approach 2:
The system performs rapid preliminary calibration by leveraging transfer learning from previously calibrated vehicle models. Instead of starting from scratch, the system uses existing calibration data as a starting point and quickly adapts it to the new vehicle model through automated adjustment procedures, significantly reducing calibration time while maintaining precision.
Data Source
AI summary
In some implementations, the EMV uses a calibration to inform autonomous control over the EMV. To calibrate an EMV, the system first selects a calibration action comprising a control signal for actuating a control surface of the EMV. Then, using a calibration model comprising a machine learning model trained based on one or more previous calibration actions taken by the EMV, the system predicts a response of the control surface to the control signal of the calibration action. After the EMV executes the control signal to perform the calibration action, the EMV system monitors the actual response of the control signal and uses that to update the calibration model based on a comparison between the predicted and monitored states of the control surface.