Earth Moving Vehicle Control With Online Learning Adaptation
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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, making it difficult to develop effective autonomous control systems that can operate efficiently across different environments and vehicle types.
Innovation Solution
An autonomous or semi-autonomous earth moving system that uses a combination of sensors and machine learning models to record and process data for generating digital representations of the work site, determining optimal tool paths, and controlling the movement of earth moving vehicles, allowing for online learning and adaptation to current conditions, including soil parameters and vehicle responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual operation is used for earth moving vehicles, then control precision and adaptability to changing conditions are maintained through human judgment, but operational cost increases due to the need for continuous human presence and labor expenses
Solution Approach 1:
The earth moving vehicle performs autonomous control operations without continuous human intervention. The control system automatically adjusts control surface positions based on sensor feedback and pre-programmed logic, enabling the vehicle to serve itself in terms of navigation and operation decisions
Solution Approach 2:
The patent replaces manual mechanical control with an automated control system that uses sensors, processors, and actuators. The system substitutes human operators with electronic control mechanisms that can process sensor data and adjust control surfaces in real-time
2Adaptability or versatility
If autonomous control is implemented without adaptation mechanisms, then operational cost decreases by eliminating manual operators, but control accuracy deteriorates due to inability to adapt to changing soil conditions and vehicle models
Solution Approach 1:
The control system continuously receives feedback from sensors about soil conditions, vehicle state, and control surface performance. This feedback is processed to adjust control parameters and improve accuracy over time, allowing the system to adapt to changing conditions without manual intervention
Solution Approach 2:
The system dynamically changes control parameters such as control surface positions, actuation forces, and response thresholds based on sensor input and learned patterns. This allows the autonomous vehicle to adapt its behavior to different soil conditions and operational scenarios
3Adaptability or versatility
If a unified control system is designed to work with multiple earth moving vehicle models, then versatility across different vehicle types improves, but system complexity increases due to varying control surface layouts and actuation characteristics
Solution Approach 1:
The control system is designed with universal functionality to work with multiple earth moving vehicle models. It uses a standardized interface that can accommodate different control surface layouts and actuation types through configurable parameters and adaptive calibration procedures
Solution Approach 2:
The control system dynamically adapts its parameters and control strategies based on the specific vehicle model being operated. It can adjust its behavior in real-time to match the characteristics of different vehicles, making the system versatile without requiring separate hardwired control systems for each model
4Speed
If real-time sensor data processing is implemented for autonomous control, then responsiveness to changing conditions improves, but computational load and system complexity increase
Solution Approach 1:
The control system performs preliminary processing of sensor data by pre-defining threshold values, decision logic, and control parameters. This allows real-time responsiveness without requiring complex on-the-fly computations, as the heavy processing is done in advance or through simplified real-time rules
Data Source
AI summary
An autonomous earth moving system can determine a desired state for a portion of the EMV including at least one control surface. Then the EMV selects a set of control signals for moving the portion of the EMV from the current state to the desired state using a machine learning model trained to generate control signals for moving the portion of the EMV to the desired state based on the current state. After the EMV executes the selected set of control signals, the system measures an updated state of the portion of the EMV. In some cases, this updated state of the EMV is used to iteratively update the machine learning model using an online learning process.