Drone Positioning Control With Geofences and Marker Tracking
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Solution Overview
Problem
Existing drone systems lack effective methods for precise positioning and obstacle avoidance, especially when tracking moving subjects and navigating complex geographical areas, which limits their ability to capture dynamic scenes and maintain safety.
Innovation Solution
A system comprising a drone equipped with motors, sensors, and a processor that uses marker-based tracking, geofences, and real-time data computation to adjust flight paths dynamically, allowing for subject tracking, obstacle avoidance, and safe navigation within defined geographical areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the drone uses marker-based tracking to follow moving subjects, then the positioning precision is improved, but the device complexity increases due to multiple sensors and processors required
Solution Approach 1:
The system divides the complex positioning task into multiple independent modules: marker detection module, geofence monitoring module, obstacle avoidance module, and path planning module. Each module processes specific aspects of positioning independently, reducing the complexity burden on any single component while achieving high overall positioning precision through coordinated operation of these segmented functional units
Solution Approach 2:
The patent introduces virtual geofences as intermediary elements between the drone and physical obstacles, and between the tracking system and the subject. These digital boundaries act as mediators that simplify the control logic by providing clear decision boundaries, allowing the drone to maintain precise positioning within defined safe zones without requiring complex real-time collision calculation with every potential obstacle
2Reliability
If the drone dynamically adjusts flight paths to avoid obstacles, then the operational safety is improved, but the response time increases due to real-time data computation
Solution Approach 1:
The system pre-computes multiple potential flight paths and stores them in advance within the geofenced operational area. When obstacles are detected, the drone can immediately switch to a pre-calculated alternative path without requiring time-consuming real-time computation, thus maintaining both high operational safety through obstacle avoidance and fast response time through pre-prepared flight options
Solution Approach 2:
The flight path adjustment mechanism dynamically balances safety and response time by adapting the level of real-time computation based on situational context. In low-risk environments, the system uses pre-computed paths for rapid response, while in high-risk situations with multiple moving obstacles, it activates more intensive real-time calculation to ensure safety, creating a dynamic response strategy that optimizes both parameters
3Measurement precision
If the drone maintains precise formation and tracks subject movements, then the capture quality is improved, but the energy consumption increases due to continuous motor adjustments
Solution Approach 1:
The drone employs periodic adjustment cycles rather than continuous motor corrections. The control system monitors subject position and formation accuracy, then executes motor adjustments only when deviations exceed predetermined thresholds or at scheduled intervals. This periodic control strategy maintains the required formation precision while significantly reducing energy consumption by eliminating unnecessary continuous adjustments
Solution Approach 2:
The system implements a feedback mechanism that monitors both subject movement and drone position, adjusting motor power only when correction is actually needed to maintain formation precision. The feedback loop compares desired versus actual positions and activates energy-consuming motor adjustments solely when errors exceed acceptable margins, thereby maintaining high formation accuracy while minimizing wasted energy on redundant corrections
Data Source
AI summary
A system, method, and apparatus for remotely controlled or even autonomous drone positioning control includes a drone, positioning control subsystem, subcontroller, positional or inertial sensor, processor, image sensor, and ground control device, and is configured to i) ascertain a geographical area having a geofence, ii) track at least one subject and an associated physical or digital marker within the geographical area, iii) recognize and process at least one marker geofence, iv) execute at least one positioning plan data set having at least one positioning path, and v) fly the drone per the data sets and paths without crossing a geofence or colliding with any obstacle. The present invention may also be configured to execute one or more commands that cause the drone to switch its position or path relative to priority or sequence-oriented commands, or to move the drone within a certain distance from the marker.