Roadside Mowing Guidance With 3D Obstacle Mapping
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Solution Overview
Problem
Current roadside mowing operations face challenges such as damage to mowing equipment and infrastructure from hidden obstacles like telecom and power boxes, reduced visibility due to overgrown vegetation, and high operational costs from frequent mowing cycles, posing safety hazards and maintenance issues.
Innovation Solution
A guidance system for agricultural vehicles that utilizes LIDAR and image sensors to detect and classify objects of interest, generating three-dimensional geospatial maps, and automatically adjusts vehicle operations to avoid obstacles, log locations, and provide geospatial data for targeted maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mowing operations are conducted along roadsides to maintain clear visibility, then safety is 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 such as telecom boxes, power boxes, and other infrastructure elements before the mowing operation begins. By using LIDAR and image sensors to scan the area ahead and create a map of hidden objects, the system allows the operator to plan the mowing path in advance to avoid these obstacles, thereby preventing equipment damage while maintaining safety benefits of roadside mowing
Solution Approach 2:
The guidance system acts as an intermediary between the mowing operation and hidden obstacles. It uses sensor data (LIDAR, image sensors) to detect obstacles that are not visible to the human operator, processes this information to identify safe mowing paths, and provides guidance recommendations to the operator. This intermediary system bridges the gap between the need to mow roadside vegetation and the risk of damaging equipment on hidden infrastructure
2Illumination intensity
If frequent mowing cycles are performed to manage fast-growing vegetation, then visibility is maintained, but operational costs increase
Solution Approach 1:
The system creates a preliminary map of the roadside area including vegetation distribution, terrain features, and obstacle locations before mowing begins. This advance knowledge allows for optimized path planning that maximizes mowing efficiency by identifying the most effective cutting patterns and areas that require attention, thereby maintaining visibility while reducing the frequency and cost of mowing operations
Solution Approach 2:
The guidance system provides real-time feedback to the operator during mowing operations, showing the current position relative to the planned path, detected obstacles, and areas that have been mowed. This feedback loop allows the operator to adjust the mowing pattern dynamically, ensuring complete coverage for visibility maintenance while avoiding redundant passes that would increase operational costs
3Reliability
If the agricultural vehicle automatically adjusts operation to avoid detected objects, then equipment damage is prevented, but the complexity of the guidance system increases
Solution Approach 1:
The guidance system performs self-service by automatically detecting obstacles using LIDAR and image sensors, processing the sensor data to identify safe paths, and providing real-time guidance recommendations without requiring external assistance. The system autonomously monitors the environment ahead, processes the information through onboard computers, and generates avoidance recommendations, thereby protecting equipment while managing complexity through integrated self-contained functionality
Solution Approach 2:
The system merges multiple functions into a single integrated guidance system: obstacle detection using LIDAR, visual detection using image sensors, path planning algorithms, real-time processing, and operator interface all combine in one system. This consolidation of detection, processing, decision-making, and guidance functions into a unified platform achieves equipment protection while controlling overall system complexity through functional integration rather than separate independent 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 by avoiding damage to equipment and infrastructure, improves visibility, and reduces operational costs by optimizing mowing operations and maintaining clear roadways.
Implementation Method 1
receive LIDAR data from a 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.