UAV Target Location via Lévy Flight and Simplex Minimization
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Solution Overview
Problem
Conventional UAVs using pre-programmed flight paths are inefficient in locating targets within a designated area, especially when the target's sensor range is smaller than the area of interest, as they do not effectively utilize onboard sensor data for autonomous navigation.
Innovation Solution
A UAV system that combines position determination and sensor data to dynamically select a flight path, transitioning from a randomized Lévy flight process to a geo-localization simplex minimization process to statistically reduce and minimize the time required to locate a target, using onboard sensors and GPS for navigation and confidence level calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If pre-programmed flight paths are used for UAV search missions, then the flight control and sensor data acquisition are decoupled and simple to implement, but the time required to locate a target increases significantly
Solution Approach 1:
The patent merges flight control and sensor data acquisition into an integrated autonomous navigation system. The flight path determination unit combines GPS position data with sensor measurements (accelerometer, gyroscope, magnetometer) to dynamically calculate optimal flight paths, eliminating the decoupling between control and sensing while reducing target location time through coordinated autonomous operation
Solution Approach 2:
The system implements continuous feedback by constantly monitoring sensor data and position information, then using this feedback to dynamically adjust the flight path in real-time. The flight path determination unit processes ongoing sensor readings and GPS updates to optimize the search trajectory, enabling the UAV to adapt its navigation based on actual environmental conditions and sensor detections
2Productivity
If conventional pre-programmed search flight paths are used, then the UAV can cover a specified area, but the sensor range being smaller than the area of interest reduces search efficiency
Solution Approach 1:
The patent transitions from static pre-programmed flight paths to dynamic autonomous navigation. The flight path determination unit continuously calculates optimal trajectories based on real-time sensor data and position information, allowing the UAV to adapt its search pattern dynamically to match its actual sensor range and efficiently cover the designated area without wasting time on redundant paths
Solution Approach 2:
The system changes navigation parameters dynamically by adjusting flight path parameters based on sensor range limitations. The flight path determination unit modifies velocity vectors, turn rates, and search patterns according to the relationship between sensor range and area of interest, optimizing search efficiency for the specific operational constraints
3Extent of automation
If onboard sensor data is not utilized for navigation, then the system is simpler to operate, but the UAV cannot autonomously optimize its flight path to minimize search time
Solution Approach 1:
The UAV performs self-service through autonomous navigation where the flight path determination unit independently processes sensor data and position information to generate optimized flight paths without human intervention. The system serves itself by automatically integrating accelerometer, gyroscope, and magnetometer data with GPS position to navigate toward the target, reducing the need for human operators while maintaining operational effectiveness
Data Source
AI summary
Described herein is an unmanned aerial vehicle (UAV) system that incorporates sensor data to statistically minimize the time to autonomously locate a target on the ground. The system uses a two-stage approach to finding the RF target: 1) randomized flight, such as Lévy fight, to search the ground space and, 2) a geo-localization process, such as a simplex minimization process, to home in on the target.