Agronomy Vehicle Obstruction Detection With 2D-3D Sensor Fusion
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Solution Overview
Problem
Agronomy vehicles, such as tractors and their implements, often encounter obstacles while operating, which can cause damage to the vehicle or implement. Existing technologies lack effective methods to automatically identify and respond to obstacles in real-time, leading to potential damage and inefficiencies.
Innovation Solution
The proposed obstruction avoidance system uses a combination of 2D image processing and 3D point cloud analysis to identify potential obstructions. By correlating data from sensors, such as cameras and LIDAR, the system determines the location and dimensions of obstacles and automatically adjusts the state of the agronomy vehicle or its implement to avoid or minimize damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated obstruction detection systems are implemented, then damage to agronomy vehicles is reduced, but device complexity increases
Solution Approach 1:
The system combines multiple sensor types (cameras, LIDAR, radar) into a single multi-functional detection platform that performs both 2D image capture and 3D point cloud generation, reducing the need for separate dedicated systems while maintaining comprehensive obstruction detection capabilities
Solution Approach 2:
The patent implements a hierarchical processing architecture where 2D image data and 3D point cloud data are processed at different levels of detail, with the 3D processing nested within the overall detection framework to handle only critical obstructions, thereby managing computational complexity
2Measurement precision
If real-time 3D point cloud analysis is performed, then obstruction identification accuracy is improved, but processing time increases
Solution Approach 1:
The detection process is segmented into two stages: initial 2D image-based obstruction candidate identification followed by selective 3D point cloud analysis only for identified candidates, avoiding the need to process all 3D data points and significantly reducing processing time while maintaining accuracy
Solution Approach 2:
The system performs preliminary 2D image processing to identify potential obstructions before committing to more computationally intensive 3D analysis, allowing the system to filter out non-obstruction elements early and focus processing resources only on relevant targets
3Productivity
If automated state adjustment of implements is implemented, then operational efficiency is improved, but device complexity increases
Solution Approach 1:
The system establishes a closed-loop feedback mechanism where sensor data continuously monitors obstruction presence, the controller automatically adjusts implement state in response, and the system continues monitoring to verify the effectiveness of the adjustment, enabling adaptive automated operation without requiring complex manual intervention 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
This system effectively identifies and responds to obstacles, reducing the risk of damage to agronomy vehicles and their implements. It enables automatic and adaptive responses to various obstructions, improving operational efficiency and safety.
Implementation Method 1
obtain signals serving as a basis for a three-dimensional (3D) point cloud
Data Source
AI summary
An obstruction avoidance system may include a agronomy vehicle, at least one sensor configured to output signals serving as a basis for a three-dimensional (3D) point cloud and to output signals corresponding to a two-dimensional (2D) image, and instructions to direct a processor to capture a particular 2D image and to obtain signals serving as a basis for a particular 3D point cloud; identify an obstruction candidate in the particular 2d image; correlate the obstruction candidate to a portion of the particular 3D point cloud to determine a value for a parameter of the obstruction candidate; classify the obstruction candidate as an obstruction based upon the parameter; and output control signals to alter a state of the agronomy vehicle in response to the obstruction candidate being classified as an obstruction.