Roadside Mowing Guidance With LIDAR Refuse 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, 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 LIDAR and image sensors to detect and classify objects, generating three-dimensional geospatial maps, and adjusts vehicle operations automatically to avoid obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If roadside mowing operations are conducted frequently to maintain visibility and safety, then visibility and safety are improved, but operational costs and equipment damage risk increase
Solution Approach 1:
The system performs preliminary detection of obstacles using LIDAR and image sensors before mowing operations begin. By identifying telecom boxes, power boxes, and other hidden objects in advance, the system allows operators to plan routes that avoid these obstacles, preventing equipment damage and reducing the need for frequent repairs, thereby lowering operational costs while maintaining safety
Solution Approach 2:
The patent introduces LIDAR and image sensors as intermediary detection devices between the mowing equipment and hidden obstacles. These sensors create a detection layer that identifies objects obscured by vegetation, allowing the system to navigate around obstacles without direct contact, thus preventing damage while maintaining regular mowing schedules for visibility
2Productivity
If mowing operations continue without obstacle detection, then operational efficiency is maintained, but equipment damage and service disruption increase
Solution Approach 1:
The system implements real-time feedback through LIDAR and image sensors that continuously monitor the mowing environment. When obstacles are detected, the system provides immediate feedback to operators through alerts and route recommendations, allowing them to adjust operations on-the-fly. This feedback mechanism prevents equipment damage while maintaining operational efficiency by minimizing interruptions
Solution Approach 2:
The patent replaces traditional mechanical obstacle detection (physical contact with obstacles) with optical and electromagnetic sensing systems. LIDAR and image sensors detect obstacles remotely without mechanical contact, substituting the mechanical interaction that causes damage with a non-contact detection method, thereby maintaining productivity while improving equipment reliability
3Measurement precision
If manual inspection of vegetation is used to locate obstacles, then detection accuracy is improved, but time consumption and operational delays increase
Solution Approach 1:
The system replaces manual visual inspection with automated LIDAR and image sensor systems. These sensors scan the environment continuously and process data to identify obstacles hidden in vegetation. The automated detection provides accurate location information without requiring operators to stop and manually inspect areas, thereby maintaining high detection accuracy while significantly reducing time consumption
Solution Approach 2:
The LIDAR and image sensors operate continuously during mowing operations, providing uninterrupted detection of obstacles. Unlike manual inspection which requires periodic stopping, the automated system maintains continuous surveillance of the environment, ensuring accurate obstacle detection without operational delays and enabling real-time route adjustments
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, maintaining visibility, and reducing maintenance costs through automated obstacle avoidance and targeted maintenance.
Implementation Method 1
receive LIDAR data from a LIDAR camera
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 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.