Agricultural Vehicle Obstacle Mapping for Roadside Mowing 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 safety.
Innovation Solution
A guidance system for agricultural vehicles using image sensors and GNSS data to identify and classify obstacles, adjust operations automatically, and generate geospatial maps to avoid damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mowing operations are conducted along roadsides, then vegetation maintenance and visibility improvement are achieved, but risk of damage to mowing equipment and infrastructure increases
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-plans avoidance paths, allowing the mowing operation to proceed safely without unexpected equipment damage.
Solution Approach 2:
The system continuously captures real-time image data and LIDAR scans during mowing operations, processes this data to detect new obstacles, and dynamically adjusts the guidance path. This closed-loop feedback system enables the mower to respond to previously undetected objects and maintain safe operation throughout the work area.
2Reliability
If frequent mowing cycles are implemented to manage fast-growing vegetation, then roadside appearance and safety are improved, but operational costs increase
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-plans avoidance paths, allowing the mowing operation to proceed safely without unexpected equipment damage.
Solution Approach 2:
The system continuously captures real-time image data and LIDAR scans during mowing operations, processes this data to detect new obstacles, and dynamically adjusts the guidance path. This closed-loop feedback system enables the mower to respond to previously undetected objects and maintain safe operation throughout the work area.
3Reliability
If manual inspection and avoidance of obstacles is used during mowing, then equipment damage can be prevented, but operational efficiency and productivity decrease
Solution Approach 1:
The system employs autonomous obstacle detection and avoidance capabilities, allowing the mowing operation to proceed without constant manual monitoring. The image sensors, LIDAR, and guidance system work together to automatically detect obstacles and adjust the mowing path, freeing operators to focus on higher-level tasks and improving overall operational efficiency.
Solution Approach 2:
The system replaces manual visual inspection and physical avoidance maneuvers with automated optical and electromagnetic sensing systems. Image sensors and LIDAR detect obstacles, while the guidance system computationally determines avoidance paths, substituting human cognitive and physical efforts with automated technological systems.
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 avoiding obstacles and reducing maintenance costs through automated obstacle detection and mapping.
Implementation Method 1
receive image data from an image sensor, analyze the image data to identify and classify one or more pieces of refuse
Implementation Method 2
The image sensor may include at least one of a thermal camera, a light detection and ranging (LIDAR) camera
Implementation Method 3
The image sensor may include at least one of a thermal camera, 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 pieces of refuse depicted within the image data, receive GNSS location data, responsive to identifying and classifying one or more pieces of refuse, log location data indicating locations of the one or more pieces of refuse, 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 pieces of refuse on the geospatial map.