Agricultural Vehicle Route Planning Using Heuristic ML Training Data
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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 in some cases, impossible, especially for rare or complex problems, leading to biased and inefficient route prediction.
Innovation Solution
Using heuristic algorithms to generate training data for machine-learning models, incorporating constraints such as obstacle avoidance, turn minimization, and soil compaction, and combining with real-world historical routes to optimize route prediction, thereby reducing the need for manual data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world data is collected for training machine-learning models, then the model can learn from actual operational conditions, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent applies preliminary action by using heuristic algorithms to generate synthetic training data in advance, before actual machine learning model training. This pre-generated data includes various route scenarios, constraints, and optimization cases that would otherwise require lengthy real-world data collection. The synthetic data preparation occurs beforehand, enabling the ML model to be trained without waiting for extensive field data gathering.
Solution Approach 2:
The patent employs copying by creating synthetic copies of real-world route planning scenarios through heuristic algorithms. Instead of collecting actual operational data from field operations, the system generates artificial but realistic route data that mirrors real conditions. This copying approach allows the ML model to learn from simulated examples that replicate genuine agricultural vehicle routing challenges without requiring physical data collection.
2Reliability
If real-world data is collected for training machine-learning models, then the model reflects actual operational conditions, but the cost increases significantly
Solution Approach 1:
The patent applies preliminary action by using heuristic algorithms to generate synthetic training data in advance, before actual machine learning model training. This pre-generated data includes various route scenarios, constraints, and optimization cases that would otherwise require lengthy real-world data collection. The synthetic data preparation occurs beforehand, enabling the ML model to be trained without waiting for extensive field data gathering.
Solution Approach 2:
The patent employs copying by creating synthetic copies of real-world route planning scenarios through heuristic algorithms. Instead of collecting actual operational data from field operations, the system generates artificial but realistic route data that mirrors real conditions. This copying approach allows the ML model to learn from simulated examples that replicate genuine agricultural vehicle routing challenges without requiring physical data collection.
3Quantity of substance
If manual data collection is used for training, then data can be obtained, but manual biases are introduced and data variety is limited
Solution Approach 1:
The patent applies self-service by enabling the system to automatically generate its own training data through heuristic algorithms without human intervention. The heuristic components autonomously create diverse route scenarios, apply constraints, and produce optimization cases that serve as training data. This self-generated data approach eliminates manual biases while providing abundant varied training examples for the machine learning model.
4Productivity
If heuristic algorithms are used to generate training data, then data collection time and cost are reduced, but the need for diverse training scenarios must be maintained
Solution Approach 1:
The patent applies dynamics by making the heuristic data generation process adaptive and flexible. The system dynamically adjusts route scenarios, constraints, and parameters to create diverse training cases. Rather than using fixed static data, the heuristic algorithms generate varied routes considering different agricultural conditions, vehicle types, field geometries, and operational constraints, ensuring the training data covers a wide range of possible real-world situations.
Data Source
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.