Agricultural Vehicle Guidance Using Image and GNSS Obstacle Mapping
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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, pose safety hazards, and increase operational costs.
Innovation Solution
An agricultural vehicle guidance system utilizing image sensors and GNSS data to identify and classify objects, generate geospatial maps, and adjust vehicle operations to avoid obstacles automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual roadside mowing operations are conducted, then vegetation maintenance and clear visibility are achieved, but equipment damage risk and operational costs increase due to hidden obstacles
Solution Approach 1:
The system performs preliminary detection of obstacles (telecom boxes, power boxes, rocks, debris) using image sensors and LiDAR before the mowing operation begins. The guidance system creates a map of hazardous objects and pre-planes avoidance paths, allowing the mowing operation to proceed without unexpected equipment damage while maintaining productivity.
Solution Approach 2:
The patent introduces an intermediary guidance system between the mowing equipment and the obstacles. This system includes image sensors, LiDAR, processors for analyzing sensor data, and a guidance system that processes information about hidden objects and communicates avoidance instructions to the operator or automatically controls the vehicle, preventing direct contact between the mower and obstacles.
2Reliability
If frequent mowing cycles are implemented to manage fast-growing vegetation, then clear visibility and roadside safety are maintained, but operational costs increase
Solution Approach 1:
The guidance system enables the mowing operation to be more self-directed by automatically detecting obstacles and generating avoidance paths without requiring constant human intervention. The system logs obstacle locations and learns from the environment, allowing for more efficient routing and reduced operational costs while maintaining the same safety standards for roadside visibility.
Solution Approach 2:
The system changes operational parameters by using multiple sensor types (image sensors, LiDAR, thermal cameras) and processing their data to create a comprehensive understanding of the environment. This allows for optimized mowing paths that avoid obstacles while maintaining efficient vegetation management, reducing the number of passes needed and lowering operational costs.
3Reliability
If automated obstacle detection and avoidance systems are implemented, then equipment damage is prevented and operational efficiency is improved, but system complexity increases
Solution Approach 1:
The guidance system is segmented into distinct functional modules: image sensors for visual detection, LiDAR for depth mapping, thermal cameras for heat signature detection, processors for data analysis, and a guidance system for path planning. Each module performs a specific function, making the overall complex system manageable through modular design and allowing for targeted improvements in each component.
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 and reducing operational costs 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
The image sensor may include at least one of a light detection and ranging (LIDAR) camera
Data Source
Figure 1
Figure 2A~2B
Figure 3A~3B
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.