Vehicle Path Filtering Using Yield and Cost Thresholds
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Solution Overview
Problem
Current vehicle guidance systems fail to effectively plan paths that consider non-geographic features of a field or work area, leading to inefficiencies in vehicle operation and increased maintenance costs, as they do not account for varying yield, crop destruction, and operational costs across different areas.
Innovation Solution
A system that uses non-geographic data to filter and modify vehicle paths by deleting or modifying guidance lines that do not meet operator-set thresholds for yield, crop destruction, and operational costs, allowing the vehicle to prioritize high-yield areas and minimize damage and operational expenses, with dynamic updates based on changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional vehicle guidance systems use standard path planning algorithms, then the vehicle can cover the field area efficiently, but the system cannot account for non-geographic features such as yield variations, crop destruction, and operational costs
Solution Approach 1:
The path planning system is segmented into multiple independent modules: a path planning module that generates initial guidance lines, a filtering module that applies non-geographic feature thresholds, and a guidance module that executes the filtered path. This segmentation allows each module to handle specific functions, improving adaptability to non-geographic features while keeping individual module complexity manageable.
Solution Approach 2:
A filtering module acts as an intermediary between the path planning module and the guidance module. This intermediary receives the planned path, applies threshold-based filtering based on non-geographic features (yield, crop destruction, operational costs), and outputs the optimized path. The intermediary enables the system to incorporate non-geographic considerations without requiring complete system redesign.
2Productivity
If the system filters paths based on multiple non-geographic features with threshold values, then the vehicle operation efficiency improves, but the computational processing time and complexity increase
Solution Approach 1:
Non-geographic feature data (yield maps, crop destruction zones, operational cost parameters) are collected and processed in advance before path execution. Threshold values for these features are pre-determined based on operational requirements. This preliminary preparation enables rapid path filtering during actual vehicle operation, improving efficiency without excessive computational burden during execution.
Solution Approach 2:
The system uses threshold-based parameter filtering where non-geographic feature values are compared against predetermined thresholds. This parameter change approach transforms complex multi-criteria optimization into simpler threshold comparison operations, significantly reducing computational processing time while maintaining the ability to optimize for multiple non-geographic features simultaneously.
3Adaptability or versatility
If the system dynamically updates paths based on changing non-geographic conditions, then the adaptability to field conditions improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The system performs periodic path filtering and updates at predetermined intervals or at key decision points during field operations, rather than continuously processing data. This periodic action allows the system to adapt to changing non-geographic conditions while avoiding the excessive computational burden of continuous real-time processing, balancing adaptability with manageable system complexity.
4Productivity
If the system deletes guidance lines that do not meet threshold values, then the vehicle prioritizes high-yield areas and minimizes damage, but the total work area coverage may be reduced
Solution Approach 1:
The system applies different quality standards to different segments of the path based on local non-geographic features. High-yield areas with favorable conditions receive priority treatment and are included in the optimized path, while low-yield areas or zones with high operational costs are filtered out. This local quality approach ensures that path optimization is tailored to specific field conditions rather than applying uniform criteria across the entire field.
Data Source
AI summary
Systems and methods for planning and filtering a path of a vehicle in a work area or field are described. An example system includes a user interface that supports a selection of a threshold value used to filter the path of the vehicle. The system also includes a data processor that receives the path that defines guidance lines and transitions between guidance lines and filters the path by deleting portions of the guidance lines that do not satisfy the threshold value. The guidance lines define movement of the vehicle though a work area or field. The system includes a guidance module that controls at least one motive function of the vehicle based on the filtered path.