Crop Row Field Mapping for Adaptive Autonomous Robot Navigation
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Solution Overview
Problem
Current unmanned agricultural robots face challenges in navigating agricultural fields due to variable accuracy of GPS-based geospatial data and unexpected obstacles like mis-planted rows and weeds, which existing precision planting technologies cannot fully address.
Innovation Solution
An unmanned agricultural robot system that uses aerial mapping sensors to generate a geospatial data map by combining actual and anticipated crop row positions, allowing the robot to autonomously navigate and adapt to changing field conditions in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS-based precision planting systems are used to create as-planted maps, then geospatial data can be obtained for navigation, but the accuracy is variable and insufficient for robust autonomous navigation
Solution Approach 1:
The patent combines multiple data sources including GPS-based as-planted maps, aerial mapping sensor data, and onboard sensor data to create a hybrid geospatial map. This merging of data sources compensates for the variable accuracy of individual systems, providing both high precision and reliable navigation information for autonomous operation.
Solution Approach 2:
The patent introduces aerial mapping sensors as an intermediary system that captures actual crop row positions from above. This intermediary data source bridges the gap between the low-precision GPS as-planted maps and the need for high-precision navigation, providing accurate geospatial information without requiring direct contact with the crops.
2Adaptability or versatility
If traditional as-planted maps are used, then navigation planning can be performed, but the system cannot adapt to unexpected obstacles like mis-planted rows or weeds
Solution Approach 1:
The patent implements feedback loops where aerial mapping sensors continuously capture actual crop row positions, compare them against the planned navigation path, and provide correction data. This feedback mechanism enables the system to detect mis-planted rows, weeds, and other obstacles, then adapt the navigation plan in real-time to avoid damage while maintaining productivity.
Solution Approach 2:
The patent transforms the static as-planted map into a dynamic geospatial map that updates in real-time as the autonomous vehicle navigates the field. The system continuously incorporates new aerial mapping data to reflect actual field conditions, enabling adaptive navigation that responds to changing circumstances such as mis-planted rows or unexpected obstacles.
3Measurement precision
If high-resolution actual crop row position data is collected throughout the entire field, then navigation accuracy is improved, but the time and resources required increase significantly
Solution Approach 1:
The patent performs preliminary mapping actions by capturing aerial imagery at strategic points along the navigation path rather than attempting to map the entire field beforehand. The system collects geospatial data progressively as it moves through the field, using predictive algorithms to interpolate crop row positions in areas not yet directly observed, thereby reducing total mapping time while maintaining accuracy.
Solution Approach 2:
The patent implements partial mapping by collecting high-resolution crop row position data only in the immediate vicinity of the autonomous vehicle and using predictive models to estimate positions in surrounding areas. This partial action approach provides sufficient navigation precision without the time cost of comprehensive field-wide mapping, allowing the system to operate efficiently while maintaining safety.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables robust and flexible navigation of unmanned agricultural robots, allowing them to perform in-season management tasks with improved accuracy and reduced crop damage, even in fields with variable geospatial data quality.
Implementation Method 1
One or more aerial mapping sensors may be deployed at a height above the annual crop rows, so as to enable the one or more aerial mapping sensors to capture geospatial data within an observation window of the agricultural field
Data Source
AI summary
A method of using an unmanned agricultural robot to generate an anticipatory geospatial data map of the positions of annual crop rows planted within a perimeter of an agricultural field, the method including the step of creating a geospatial data map of an agricultural field by plotting actual annual crop row positions in a portion of the geospatial data map that corresponds to a starting point observation window, and filling in a remainder of the geospatial data map with anticipated annual crop row positions corresponding to the annual crop rows outside of the starting point observation window, and refining the geospatial data map by replacing the anticipated annual crop row positions with measured actual annual crop row positions when an unexpected obstacle is encountered.