Camera Swarm Angle Tracking for Long-Range Collision Avoidance
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Solution Overview
Problem
Current systems face challenges in reliably detecting and tracking small flying objects like birds and drones at long ranges due to insufficient resolution, making collision avoidance unreliable.
Innovation Solution
The implementation of area-scanning optical systems using cameras to capture images of a volume of space in front of an aircraft, processing these images to detect swarm motion and issue alerts for potential collisions, allowing for timely collision avoidance maneuvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If camera resolution is increased to detect individual small flying objects at long ranges, then detection reliability improves, but system complexity and cost increase significantly
Solution Approach 1:
The system divides the detection task into two levels: first detecting the swarm as a collective entity using standard resolution cameras, then analyzing motion patterns of the segmented swarm to predict collision risk. This avoids the need for high-resolution imaging of individual objects while maintaining detection reliability.
Solution Approach 2:
The system applies partial action by focusing computational resources only on swarms that exhibit collision-indicative motion patterns, rather than analyzing all detected objects. This reduces processing complexity while maintaining reliability for critical cases.
2Loss of time
If detection range is extended to several miles, then collision avoidance time increases, but resolution becomes insufficient for reliable tracking
Solution Approach 1:
The system transitions from tracking individual object positions (2D spatial coordinates) to tracking swarm-level motion patterns and angular changes over time. This dimensional shift allows long-range detection using lower resolution while maintaining sufficient precision for collision prediction through temporal analysis of swarm behavior.
Solution Approach 2:
The system performs preliminary detection of swarms at long ranges using standard cameras, then continuously monitors their motion patterns to predict future positions. This early detection combined with predictive analysis provides sufficient lead time for collision avoidance without requiring high-resolution tracking at extreme distances.
3Measurement precision
If individual object tracking is attempted at long ranges, then collision prediction accuracy improves, but detection reliability decreases due to insufficient resolution
Solution Approach 1:
The system merges multiple detection approaches: standard resolution camera detection of swarm presence, motion pattern analysis of the collective swarm, and predictive algorithms. This combination compensates for the inability to resolve individual objects, maintaining both detection reliability and prediction accuracy through the integration of multiple data sources and analysis methods.
Data Source
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AI summary
Systems and methods for tracking swarms of flying objects for collision avoidance using area-scanning optical systems. Images of the volume of space forward of an aircraft in flight are captured using one or more cameras and then the images are processed to determine whether the image data indicates the presence of a swarm. The camera-based collision avoidance system is configured to detect and angularly track small objects flying in a swarm at sufficiently far ranges to cue or alert the flight control system onboard an autonomous or piloted aircraft to avoid the swarm. A swarm angle tracking algorithm is used to recognize swarms of flying objects that move in unison in a certain direction by detecting a consistent characteristic of the pixel values in captured images which is indicative of swarm motion.