Agricultural Vehicle Guidance for Hidden Obstacle Detection and Avoidance
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Solution Overview
Problem
Challenges exist in roadside mowing operations due to hidden obstacles like telecom and power boxes, which can damage equipment, disrupt service, and increase operational costs, while overgrown vegetation reduces visibility and poses safety hazards.
Innovation Solution
A guidance system for agricultural vehicles that uses image sensors and GNSS data to identify and classify objects, generate geospatial maps, and adjust vehicle operation to avoid obstacles, including water wells and other objects, using machine learning models to detect and classify objects of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If roadside mowing operations are conducted to maintain visibility and clear vegetation, then safety and visibility are improved, but the risk of damage to mowing equipment and infrastructure increases due to hidden obstacles
Solution Approach 1:
The system performs preliminary detection of obstacles using image sensors and machine learning models before the mowing operation begins. By identifying telecom boxes, power boxes, and other hidden objects in advance, the system allows operators to adjust the mowing path or lift the implement to avoid damage, thus maintaining visibility improvement while preventing equipment damage
Solution Approach 2:
The guidance system acts as an intermediary between the vegetation management objective and the physical mowing operation. It processes image data, identifies obstacles, and provides guidance recommendations that mediate between the need to clear vegetation for visibility and the need to avoid hidden obstacles, resolving the contradiction through intelligent decision support
2Productivity
If mowing operations are performed frequently to manage fast-growing vegetation, then vegetation control is improved, but operational costs increase due to equipment damage and infrastructure repairs
Solution Approach 1:
The system performs preliminary obstacle detection and mapping before mowing operations, creating a digital record of hidden objects such as telecom and power boxes. This preliminary action prevents repeated equipment damage and costly repairs during subsequent mowing cycles, reducing operational costs while maintaining productivity
Solution Approach 2:
The system provides feedback to operators about detected obstacles and recommended path adjustments in real-time during mowing operations. This feedback loop allows operators to avoid obstacles without stopping the mowing operation, maintaining productivity while preventing equipment damage and reducing operational costs
3Ease of operation
If manual inspection and mowing operations are used to detect and avoid obstacles, then operational flexibility is maintained, but detection precision and response time are insufficient
Solution Approach 1:
The system replaces manual visual inspection with automated image sensors and machine learning models that continuously scan the environment. This substitution provides superior detection precision and response time while maintaining operational flexibility through automated guidance recommendations that operators can implement in real-time
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
Enhances safety and efficiency by automatically avoiding obstacles, reducing equipment damage, and optimizing maintenance operations, while providing geospatial data for targeted maintenance.
Implementation Method 1
receive image data from an image sensor, analyze the image data to identify and classify one or more water wells depicted within the image data
Implementation Method 2
receive GNSS location data
Data Source
AI summary
A guidance system for controlling operation of an agricultural vehicle. The guidance system includes at least one processor and at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the guidance system, during an agricultural operation, to: receive image data from an image sensor, analyze the image data to identify and classify one or more water wells depicted within the image data, receive GNSS location data, responsive to identifying and classifying one or more water wells, log location data indicating locations of the one or more water wells, and based at least partially on the image data and the logged location data, generate a geospatial map indicating locations of the one or more water wells on the geospatial map.