Earthmoving Vehicle Control With Online Learning for Changing Soil
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Solution Overview
Problem
The operation of earth moving vehicles is costly and challenging due to the need for manual operators and the complexity of adapting to changing soil conditions and diverse vehicle models, which complicates the development of effective autonomous control systems.
Innovation Solution
An autonomous or semi-autonomous earth moving system that uses a combination of sensors and machine learning models to control and navigate earth moving vehicles, selecting optimal tool paths and control signals based on real-time data from the vehicle and environment, allowing for adaptive operation across various soil conditions and vehicle types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual operators control earth moving vehicles, then operational flexibility and adaptability to changing conditions are maintained, but operational costs increase and productivity decreases
Solution Approach 1:
The earth moving vehicle autonomously performs earth moving operations using onboard sensors, processors, and control systems. The vehicle independently detects soil conditions, determines optimal tool paths, and executes excavation actions without continuous human intervention, enabling self-service operation that maintains adaptability while improving productivity
Solution Approach 2:
The patent replaces manual mechanical control with an automated control system comprising sensors, processors, and actuators. The system substitutes human operators with electronic and computational components that detect environmental conditions and automatically adjust vehicle operations, maintaining adaptability through sensor feedback while eliminating the productivity limitations of manual control
2Productivity
If autonomous control systems are implemented, then productivity and operational efficiency improve, but system complexity and difficulty of detecting and measuring changing conditions increase
Solution Approach 1:
The patent combines multiple sensor types (force sensors, position sensors, imaging sensors) into an integrated detection system. These sensors are merged with processing components that analyze sensor data and determine soil conditions, creating a unified system that detects changing conditions effectively while managing complexity through integration
Solution Approach 2:
The control system is designed to perform multiple functions: detecting soil conditions, determining tool paths, controlling vehicle movement, and adjusting tool operations. This multi-functional system handles various detection and control tasks using a unified architecture, improving productivity while managing complexity through versatile component design
3Productivity
If autonomous control systems are implemented, then operational costs decrease and productivity improves, but device complexity and difficulty of controlling diverse vehicle models increase
Solution Approach 1:
The control system is designed with universal components that can adapt to different earth moving vehicle models. The processor executes standardized algorithms that work across various vehicle types, and the control interfaces are designed to accommodate different actuation systems (hydraulic, electric, pneumatic), enabling the system to control diverse vehicle models without requiring model-specific complex architecture
Solution Approach 2:
The system adapts to different vehicle models by adjusting control parameters such as actuation force characteristics, response times, and sensor calibration values. Rather than requiring different control algorithms for each vehicle type, the system maintains a unified control architecture and modifies operational parameters to accommodate variations in vehicle specifications, reducing device complexity while maintaining versatility
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.