Ground Vision Acquisition for Uneven Road Navigation in Logistics Vehicles
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Solution Overview
Problem
Existing unmanned vehicles struggle to effectively navigate uneven road surfaces, as they cannot accurately judge the presence or severity of such surfaces during operation, which can lead to instability and potential accidents.
Innovation Solution
A vision acquisition system for smart logistics vehicles, comprising an image information acquisition unit, angle information acquisition unit, vehicle information acquisition unit, image information analysis unit, and calculation unit, which includes modules for ground image acquisition, feature point identification, camera angle control, vehicle speed and steering control, and distance measurement, allowing for real-time analysis and control to safely navigate uneven road surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing unmanned vehicle technology is used, then the vehicle can make effective judgment on pedestrians or obstacles, but the vehicle cannot effectively judge uneven road surfaces
Solution Approach 1:
The vision acquisition system divides the road surface analysis into multiple components: ground image acquisition, feature point identification, contour information extraction, and pit detection. This segmentation enables precise judgment of uneven road surfaces by analyzing specific visual features rather than relying on a single detection mechanism.
Solution Approach 2:
The patent introduces a camera as an intermediary device to capture ground images, which serve as the basis for judging uneven road surfaces. The vision acquisition system acts as a mediator between the vehicle's sensors and the road surface conditions, translating visual information into actionable driving decisions.
2Reliability
If the vehicle slows down to pass through pits safely, then the vehicle can maintain stability, but the driving efficiency decreases
Solution Approach 1:
The vision acquisition system performs preliminary detection of pits and uneven surfaces before the vehicle reaches them. By identifying road conditions in advance and calculating appropriate speeds, the system enables the vehicle to maintain optimal velocity through pits without sudden braking, thereby preserving both stability and driving efficiency.
Solution Approach 2:
The system dynamically adjusts the vehicle's speed parameter based on real-time road condition analysis. By changing the speed parameter adaptively rather than maintaining a constant low speed, the vehicle can pass through pits safely while minimizing the impact on overall driving efficiency.
3Measurement precision
If the camera inclination angle is adjusted to acquire satisfactory images, then the image quality improves, but the system complexity increases
Solution Approach 1:
The camera inclination angle is made dynamic rather than fixed, allowing real-time adjustment to optimize ground image acquisition. The angle control module dynamically adapts the camera's viewing angle based on vehicle speed and road conditions, ensuring high-quality images without requiring complex mechanical structures.
4Measurement precision
If real-time vision analysis is performed to detect pits, then the road surface judgment accuracy improves, but the calculation complexity increases
Solution Approach 1:
The image processing system extracts only the essential features from ground images for pit detection, such as contour information and specific visual patterns. By focusing on key features rather than analyzing the entire image in detail, the system achieves high detection accuracy while minimizing calculation complexity.
Data Source
AI summary
A vision acquisition system equipped in an intelligent terminal of a smart logistics vehicle is disclosed. The vision acquisition system includes an image information acquisition unit, an angle information acquisition unit, a vehicle information acquisition unit, an image information analysis unit and a calculation unit. The image information acquisition unit includes a ground image acquisition module and a feature point identification module; the ground image acquisition module acquires a ground image in front of a vehicle through a camera; and the feature point identification module is used for identifying contour information of the image and mark a near point and a far point of the image. In implementation of the present invention, a road surface in front of the driving vehicle can be acquired.