Depth Imaging Obstacle Mapping for GPS-Denied UAV Avoidance
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Solution Overview
Problem
Current unmanned aerial vehicle (UAV) collision avoidance systems face challenges in GPS-denied environments, where depth sensors provide detailed views but require additional sensors, and traditional methods like SLAM fail in low light or dynamic conditions, leading to unreliable obstacle detection and avoidance.
Innovation Solution
Implementing a collision avoidance system that uses depth imaging sensors to generate a spherical map from depth images, determining obstacles within distinct distance ranges, and calculating a virtual force vector to control the UAV's flight path, allowing obstacle avoidance without relying on GPS or absolute positional information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional SLAM methods are used for obstacle detection, then the system can operate in GPS-denied environments, but the system fails in low light or dynamic conditions leading to unreliable obstacle detection
Solution Approach 1:
The patent replaces traditional SLAM algorithms with a depth imaging sensor-based detection system. The depth sensor directly measures distance to obstacles using time-of-flight or similar methods, bypassing the need for visual feature tracking that fails in low light. This substitution of the detection mechanism fundamentally resolves the reliability issue in challenging environmental conditions.
Solution Approach 2:
The system changes the measurement parameter from visual features (2D images) to depth information (3D distance data). By measuring the time of flight of light or phase shift directly, the system obtains reliable distance measurements independent of lighting conditions, thereby improving detection reliability in low light and dynamic environments.
2Measurement precision
If depth sensors are used to provide detailed views for collision avoidance, then obstacle detection accuracy improves, but additional sensors are required increasing system complexity and cost
Solution Approach 1:
The patent makes the depth imaging sensor multi-functional by using it for both navigation and obstacle detection tasks. The same sensor that provides detailed depth information for collision avoidance also enables the UAV to operate in GPS-denied environments, eliminating the need for separate additional sensors and reducing overall system complexity.
Solution Approach 2:
The system merges the obstacle detection function with the existing depth imaging capability. Instead of adding separate obstacle detection sensors, the patent combines multiple functions (depth mapping, obstacle detection, and navigation) into a single integrated system using the depth sensor, thereby reducing hardware complexity while maintaining high measurement precision.
Data Source
AI summary
According to various aspects, a collision avoidance method may include: receiving depth information of one or more depth imaging sensors of an unmanned aerial vehicle; determining from the depth information a first obstacle located within a first distance range and movement information associated with the first obstacle; determining from the depth information a second obstacle located within a second distance range and movement information associated with the second obstacle, the second distance range is distinct from the first distance range, determining a virtual force vector based on the determined movement information, and controlling flight of the unmanned aerial vehicle based on the virtual force vector to avoid a collision of the unmanned aerial vehicle with the first obstacle and the second obstacle.