UAV Infrared Navigation Switching for Low-Light Obstacle Avoidance
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Solution Overview
Problem
Conventional vision-based navigation systems for UAVs are not optimized for low-light or no-light conditions, leading to impaired autonomous navigation, potential damage, or injury, as they rely on infrared filtering which can inhibit obstacle avoidance and image quality.
Innovation Solution
A UAV system configured for day and night modes, using onboard cameras to collect image data with infrared data, and employing a learning model trained for depth estimation in the infrared domain for night mode navigation, along with a light blocking mechanism to prevent glare, enabling continuous autonomous operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If infrared filtering is used in vision-based navigation systems, then image quality is improved in day mode, but autonomous navigation capability deteriorates in low-light and no-light conditions
Solution Approach 1:
The patent implements dynamic switching between day mode and night mode configurations based on environmental lighting conditions. The system automatically adjusts camera settings, infrared filtering status, and light source activation states to optimize performance for the current operating conditions, thereby maintaining both image quality and navigation reliability across varying light levels
Solution Approach 2:
The patent applies different processing and filtering strategies to different spectral bands based on operating conditions. In day mode, infrared data is filtered to improve visible image quality. In night mode, the system selectively utilizes infrared data for navigation while managing its impact on visible image quality, applying local quality adjustments to different parts of the spectral data
2Reliability
If infrared data is utilized for night mode navigation, then obstacle avoidance capability is improved, but image quality deteriorates due to infrared glare
Solution Approach 1:
The patent segments the navigation system into distinct functional components: visible light imaging subsystem, infrared sensing subsystem, and fusion processing subsystem. This allows independent optimization of each subsystem for its specific function while maintaining overall system performance, enabling infrared data to be used for obstacle detection without compromising visible image quality
Solution Approach 2:
The patent introduces computational image processing and sensor fusion algorithms as intermediaries between the infrared data capture and the navigation decision-making process. These intermediaries process infrared data separately, extract navigation-relevant information, and fuse it with visible light data, preventing infrared glare from directly degrading the visible image quality while still enabling obstacle avoidance
3Reliability
If light sources are activated in low-light conditions, then navigation capability is improved, but harmful glare is generated that interferes with image collection
Solution Approach 1:
The patent implements periodic or pulsed activation of onboard light sources rather than continuous illumination. This allows the system to provide necessary illumination for navigation while creating temporal separation between light emission and image capture, reducing the impact of glare on image quality while maintaining navigation capability
Solution Approach 2:
The patent utilizes the temporal dimension by coordinating light source activation with camera shutter timing. The system activates lights just before or during image capture and uses synchronized shutter control to minimize glare exposure, effectively adding a time-based dimension to the spatial illumination problem
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
Enables reliable autonomous navigation in low-light conditions by filtering infrared data in day mode and utilizing infrared data for obstacle avoidance in night mode, enhancing safety and operational efficiency.
Implementation Method 1
A camera sensor of a UAV collects image data including infrared data
Data Source
AI summary
Autonomous aerial navigation in low-light and no-light conditions includes using night mode obstacle avoidance intelligence, training, and mechanisms for vision-based unmanned aerial vehicle (UAV) navigation to enable autonomous flight operations of a UAV in low-light and no-light environments using infrared data.