Aerial Vehicle Obstacle Detection With Redundant Camera Analysis
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Solution Overview
Problem
Current methods for controlling aerial vehicle flight movements are inefficient and unreliable, particularly in detecting and avoiding obstacles, as they rely solely on sensor signals without robust redundancy for accurate identification and collision avoidance.
Innovation Solution
The method employs multiple camera devices for acquiring image data, which undergo redundant analysis using both artificial intelligence and conventional image analysis to determine object parameters for flight obstacles, ensuring accurate identification and transmission of these parameters to a control device for collision avoidance, incorporating pre-processing and visual analysis to maintain field of view integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single sensor device is used for obstacle detection, then the device complexity is low, but the reliability of obstacle detection is insufficient
Solution Approach 1:
The patent assigns different detection capabilities to different camera devices - a first camera device for wide-area monitoring and a second camera device for detailed analysis of specific regions. This local differentiation of detection quality enables reliable obstacle detection without requiring all sensors to have maximum complexity
Solution Approach 2:
The obstacle detection function is segmented into multiple independent camera devices, each handling specific detection tasks. The evaluation device then processes data from these segmented sources, allowing the system to achieve high reliability through distributed detection rather than a single complex sensor
2Measurement precision
If redundant image analysis with multiple analysis methods is performed, then the measurement precision of obstacle identification is improved, but the processing time increases
Solution Approach 1:
The evaluation device performs preliminary filtering and preprocessing of image data before applying complex analysis methods. By preparing and organizing data in advance, the system reduces the time required for subsequent redundant analysis while maintaining identification accuracy
Solution Approach 2:
The system implements a multi-stage analysis process where obvious obstacles are identified quickly through simpler methods, allowing the system to skip detailed redundant analysis for clearly detected objects while applying thorough analysis only to ambiguous cases, thus reducing overall processing time
Data Source
AI summary
The preferred embodiments pertain to a method for controlling a flight movement of an aerial vehicle that includes acquiring first image data by means of a first camera device that is arranged on an aerial vehicle and configured for monitoring an environment of the aerial vehicle while flying, wherein the first image data are indicative of a first sequence of first camera images. The method also includes acquiring second image data by means of a second camera device that is arranged on an aerial vehicle and configured for monitoring the environment of the aerial vehicle while flying, wherein the second image data are indicative of a second sequence of second camera images. The processing includes determining object parameters for a position of a flight obstacle in the environment of the aerial vehicle if the first image analysis predicts the flight obstacle in the at least one camera measurement image and the second image analysis likewise identifies the flight obstacle in the at least one camera measurement image. An aerial vehicle is furthermore disclosed.