Agricultural Vehicle Sensor Fusion for Obstacle-Aware Navigation
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Solution Overview
Problem
Agricultural vehicles face challenges in navigating through unpredictable environments with obstacles and animals, as operators may not see these hazards in time to avoid them, and existing technologies lack effective automation for obstacle identification and navigation.
Innovation Solution
The agricultural vehicle is equipped with a propulsion system, steering system, vehicle controller, camera sensor, LiDAR sensor, and an imaging controller that processes data from these sensors to fuse LiDAR data and image data, creating a fused instance with depth information to guide navigational commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors (camera and LiDAR) are integrated for obstacle detection, then measurement precision and detection capability are improved, but device complexity increases
Solution Approach 1:
The patent combines camera sensor and LiDAR sensor into a unified imaging system with a single controller that processes both image data and LiDAR data together. The controller fuses data from both sensors to create comprehensive depth information and fused instances, allowing the system to achieve high measurement precision while managing complexity through integrated processing rather than separate systems
Solution Approach 2:
The imaging controller serves multiple functions: it processes image data from the camera sensor, processes LiDAR data from the LiDAR sensor, fuses both data types, creates depth information, generates fused instances, and provides navigational commands. This multi-functional approach consolidates what would otherwise require multiple separate systems into a single universal controller
2Measurement precision
If real-time data fusion from multiple sensors is performed, then obstacle identification accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The controller performs preliminary segmentation of image data and preliminary processing of LiDAR data before fusing them. By segmenting image data in advance and processing LiDAR data to extract key features before fusion, the system prepares data structures that facilitate faster and more efficient fusion operations, reducing overall processing time while maintaining high accuracy
3Reliability
If automated navigation control is implemented based on sensor data, then operational safety is improved, but system complexity increases
Solution Approach 1:
The imaging controller autonomously processes sensor data, creates fused instances with depth information, determines obstacle locations, and generates navigational commands without requiring constant operator intervention. The system serves itself by automatically completing the full control loop from sensing to navigation decision-making, improving safety through consistent automated monitoring while managing complexity through self-contained control logic
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 solution enhances the safety and efficiency of agricultural operations by providing real-time obstacle detection and navigation assistance, allowing the vehicle to autonomously avoid hazards and improve operational precision.
Implementation Method 1
a light detection and ranging (LiDAR) sensor having a LiDAR field of view (LFOV), the LiDAR sensor operably coupled to the agricultural vehicle
Data Source
AI summary
An agricultural vehicle may include a propulsion system, a steering system, and a vehicle controller. An agricultural vehicle may include a camera sensor having a camera field of view (CFOV), a light detection and ranging (LiDAR) sensor having a LiDAR field of view (LFOV), and an imaging controller in operable communication with the camera sensor and the LiDAR sensor. The imaging controller may include a processor and at least one non-transitory computer-readable storage medium having instructions store thereon that, when executed by the at least one processor cause the imaging controller to, at least, receive image data, segment the image data, fuse LiDAR data and the image data, create a fused instance including depth information; and provide at least the fused instance to the vehicle controller, wherein the vehicle controller is configured to provide at least one navigational command based at least partially on the fused instance.