Autonomous UAV Tracking With Stereo Imaging and Hybrid Gimbal
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Solution Overview
Problem
Current autonomous aerial vehicle technologies face challenges in efficiently navigating and tracking objects in complex environments, particularly in capturing high-quality images for navigation and object tracking while avoiding obstacles, especially in dynamic settings like sporting events.
Innovation Solution
The implementation of a navigation system that utilizes multiple image capture devices, including stereoscopic arrays and a hybrid mechanical-digital gimbal, to autonomously maneuver and track objects, combined with machine learning models for image-based training data, allows for efficient object tracking and image capture, even in dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple image capture devices and hybrid mechanical-digital gimbal are used, then object tracking precision and image quality are improved, but device complexity increases
Solution Approach 1:
The navigation system divides the image capture function into multiple specialized devices: stereoscopic arrays for depth perception and object tracking, and hybrid mechanical-digital gimbals for stable high-quality imaging. Each device segment handles specific tracking requirements, improving overall precision while managing complexity through functional division
Solution Approach 2:
The system employs a hybrid mechanical-digital gimbal that combines mechanical stability with digital adaptability. The mechanical portion provides physical stabilization while the digital component dynamically adjusts for precise tracking, allowing the system to adapt to varying tracking demands without requiring complete system redesign
2Reliability
If autonomous navigation with obstacle avoidance is implemented, then safety and reliability are improved, but navigation speed and response time are reduced
Solution Approach 1:
The navigation system performs preliminary mapping and obstacle identification using stereoscopic image arrays before critical navigation decisions are required. By pre-processing environmental data and identifying potential hazards in advance, the system can maintain higher speeds while ensuring safety through提前 prepared navigation paths
Solution Approach 2:
The system replaces traditional mechanical obstacle avoidance with image-based perception and machine learning algorithms. Multiple image capture devices provide real-time environmental data that is processed to identify obstacles and calculate safe paths, enabling faster response times compared to mechanical sensing and reaction systems
Data Source
AI summary
Sports and fitness applications for an autonomous unmanned aerial vehicle (UAV) are described. In an example embodiment, a UAV can be configured to track a human subject using perception inputs from one or more onboard sensors. The perception inputs can be utilized to generate values for various performance metrics associated with the activity of the human subject. In some embodiments, the perception inputs can be utilized to autonomously maneuver the UAV to lead the human subject to satisfy a performance goal. The UAV can also be configured to autonomously capture images of a sporting event and/or make rule determinations while officiating a sporting event.