Agricultural Vehicle Obstacle Detection With GNSS Logging
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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 services, and increase operational costs, while overgrown vegetation reduces visibility and poses safety hazards.
Innovation Solution
A guidance system for agricultural vehicles equipped with image sensors and GNSS technology to detect and classify objects, output alarms, and log locations, adjusting vehicle operations to avoid obstacles and maintain safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If roadside mowing operations are conducted to maintain visibility and clear vegetation, then visibility and safety 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. Objects such as telecom boxes, power boxes, and other infrastructure are identified and logged in advance, allowing the mowing operation to proceed with reduced risk of equipment damage while maintaining visibility benefits
Solution Approach 2:
The system provides real-time feedback to the operator through alarm outputs when obstacles are detected in the mowing path. The guidance system continuously monitors the environment and alerts the operator to adjust the mowing path or slow down, preventing equipment damage while allowing continuous mowing operations to maintain visibility
2Productivity
If frequent mowing cycles are performed to manage fast-growing vegetation along highways, then vegetation control is improved, but operational costs increase
Solution Approach 1:
The system logs obstacle locations and conditions during mowing operations, creating a database that can be used for future reference. This allows subsequent mowing operations to avoid known obstacles and plan more efficient paths, reducing unnecessary fuel consumption and operational costs while maintaining effective vegetation control
Solution Approach 2:
The system automatically detects, classifies, and logs obstacles without requiring manual inspection or intervention. The machine learning models continuously improve their detection capabilities using accumulated data, making the system progressively more efficient at avoiding obstacles and planning optimal mowing paths, thereby reducing operational costs
3Reliability
If manual inspection and avoidance of obstacles is used during mowing operations, then equipment safety is improved, but operational efficiency and productivity decrease
Solution Approach 1:
The system replaces manual visual inspection and obstacle avoidance with automated image sensors and machine learning algorithms. The sensors continuously scan the environment and the AI models automatically identify obstacles, eliminating the need for constant manual monitoring while maintaining high equipment safety standards and allowing the operator to focus on mowing operations
Solution Approach 2:
The system provides automated feedback through alarm outputs when obstacles are detected, allowing the operator to make quick adjustments without needing to continuously monitor the environment manually. This maintains equipment safety while enabling continuous, efficient mowing operations without productivity losses associated with manual inspection
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 preventing equipment damage, reducing repair costs, and improving visibility through automated obstacle detection and navigation.
Implementation Method 1
receive image data from an image sensor
Implementation Method 2
The image sensor may include at least one of a thermal camera
Implementation Method 3
a light detection and ranging (LIDAR) camera
Implementation Method 4
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 objects depicted within the image data, responsive to identifying and classifying one or more objects of interest, generate an alarm, cause the alarm to be output via an input/output device of the guidance system, receive GNSS location data and responsive to identifying and classifying one or more objects of interest, log location data indicating locations of the one or more objects of interest.