Machine Learning Route Planning for Multi-Factor Field Navigation
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Solution Overview
Problem
Existing route planning systems for mobile machines, such as agricultural, construction, and forestry machines, do not adequately consider complex interactions between terrain, ground conditions, soil type, weather, and machinery capabilities, leading to suboptimal paths and increased operational costs.
Innovation Solution
The use of machine learning, specifically deep learning models based on Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, or Transformer-based models, to generate routing plans that take into account various environmental and machine-specific factors, thereby optimizing routes for fuel efficiency, operation time, and soil compaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual planning or simple heuristic algorithms are used for route planning, then the system complexity is low and ease of operation is maintained, but the routing efficiency is suboptimal and fuel consumption increases
Solution Approach 1:
The patent replaces manual planning and simple heuristic algorithms with a machine learning-based deep learning model that processes terrain, ground conditions, soil type, weather, and machinery capabilities data. This substitution transitions from mechanical rule-based systems to intelligent adaptive systems, resolving the contradiction by accepting increased computational complexity to achieve significantly improved routing efficiency and fuel optimization
Solution Approach 2:
The system dynamically adjusts routing parameters based on multiple environmental and operational factors including terrain characteristics, ground conditions, soil type, weather conditions, and machinery capabilities. By continuously optimizing route parameters rather than using fixed heuristic rules, the system achieves superior routing efficiency while the complexity is managed through automated parameter adjustment rather than manual intervention
2Productivity
If complex interactions between terrain, ground conditions, soil type, weather, and machinery capabilities are considered, then routing optimization improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary data collection and processing of terrain maps, ground conditions, soil type information, weather forecasts, and machinery capability specifications before route planning begins. By pre-processing and storing this complex multi-factor data, the system reduces real-time computational complexity while maintaining comprehensive consideration of all interacting factors for optimized routing decisions
Solution Approach 2:
The deep learning model creates simplified representations or copies of the complex environmental and operational data structures, transforming detailed terrain, soil, and weather information into processed features that can be efficiently analyzed. This copying approach maintains the essential interactions between factors while reducing computational complexity for real-time route optimization
3Loss of energy
If traditional route planning methods are used, then implementation is simple and quick, but fuel consumption increases and operational costs rise
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor actual fuel consumption, terrain conditions, and operational parameters during machine operation. This feedback is used to refine and update the deep learning model's routing recommendations, creating a closed-loop system that progressively reduces fuel consumption. The automated feedback-driven optimization compensates for the increased implementation complexity by providing continuous improvement without requiring constant manual intervention
Data Source
AI summary
Embodiments include technologies that use machine learning to plan a route. Some embodiments include a method that includes using machine learning to generate or update a route plan. In some examples, the method includes receiving, by a computing system, initial routing information, the initial routing information defining routes followed by or to be followed by one or more mobile machines in one or more fields. In such examples, the method also includes training, by the computing system, a deep learning model using the initial routing information. Also, in such examples, the method includes using, by the computing system, the trained model to generate new routing information for a given field.