Agricultural Vehicle Route Planning Using Heuristic-Trained ML
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Solution Overview
Problem
Collecting real-world data for training machine-learning models for agricultural vehicle route planning is time-consuming and expensive, or impossible for rare/complex problems, leading to biased and inefficient route prediction.
Innovation Solution
Generate training data using heuristic algorithms to process boundary data of agricultural regions, incorporating constraints and real-world historical routes, and combine with machine-learning models to predict efficient routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world data is collected for training machine-learning models, then the model can learn from actual operational patterns, but the data collection process is time-consuming and expensive
Solution Approach 1:
The patent creates synthetic training data by copying and simulating real-world route planning scenarios through a digital twin of the agricultural vehicle and field environment. This allows the machine learning model to be trained on numerous simulated examples without the time and cost of collecting equivalent real-world data, while still capturing the essential patterns and constraints of actual operations.
2Measurement precision
If real-world data is collected for training machine-learning models, then the model can learn from actual operational patterns, but the process is expensive
Solution Approach 1:
The patent creates synthetic training data by copying and simulating real-world route planning scenarios through a digital twin of the agricultural vehicle and field environment. This allows the machine learning model to be trained on numerous simulated examples without the time and cost of collecting equivalent real-world data, while still capturing the essential patterns and constraints of actual operations.
3Ease of manufacture
If training data is limited to available real-world data, then collection is more feasible, but the model cannot handle rare or complex route planning problems
Solution Approach 1:
The patent prepares the system in advance by creating a digital twin that encapsulates the vehicle's characteristics, field boundaries, and operational constraints. This preliminary setup enables the generation of diverse synthetic training data covering rare and complex scenarios that would be difficult to capture in real-world operations, thereby improving model adaptability without requiring extensive real-world data collection for each scenario.
Solution Approach 2:
The patent creates synthetic training data by copying and simulating real-world route planning scenarios through a digital twin of the agricultural vehicle and field environment. This allows the machine learning model to be trained on numerous simulated examples without the time and cost of collecting equivalent real-world data, while still capturing the essential patterns and constraints of actual operations.
4Measurement precision
If manual data collection is used, then real operational patterns can be captured, but manual biases are introduced into the training data
Solution Approach 1:
The patent creates synthetic training data by copying and simulating real-world route planning scenarios through a digital twin of the agricultural vehicle and field environment. This allows the machine learning model to be trained on numerous simulated examples without the time and cost of collecting equivalent real-world data, while still capturing the essential patterns and constraints of actual operations.
Solution Approach 2:
The system uses automated algorithms to generate training data through the digital twin simulation, eliminating the need for manual data collection and annotation. This self-service approach ensures that the training data is generated consistently according to predefined rules and constraints, removing human bias from the data collection process while maintaining operational realism.
Data Source
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AI summary
A computing system for training a machine-learning model for predicting a recommended route for an agricultural vehicle to perform an agricultural process. The computing system further tracks adherence of the agricultural vehicle to the recommended route and/or controlling the vehicle to follow the recommended route. The machine-learning model is trained on recommended routes generated using one or more heuristic algorithms.