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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement.

Ziyong Zhang1, Xiaoling Xu1, Jinqiang Cui2

  • 1The Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for multiple unmanned aerial vehicles (UAVs) to achieve optimal area coverage. The approach enables UAVs to self-localize and coordinate coverage efficiently, even with limited initial global positioning.

Keywords:
coverage controlrelative localizationultra-wide band (UWB) sensorsunmanned aerial vehicles (UAVs)

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Area of Science:

  • Robotics
  • Control Systems
  • Geospatial Analysis

Background:

  • Cooperative area coverage with multiple unmanned aerial vehicles (UAVs) is complex.
  • Limited global positioning for UAVs necessitates advanced relative localization techniques.
  • Simultaneous relative localization and cooperative control present significant challenges.

Purpose of the Study:

  • To develop a method for optimal area coverage placement using multiple UAVs with limited global positioning.
  • To enable simultaneous relative localization and cooperative coverage control among UAVs.
  • To formulate and solve the optimal multi-UAV placement problem.

Main Methods:

  • A single-landmark-based relative localization algorithm was proposed.
  • A distributed coverage control law was integrated with the localization algorithm.
  • The optimal multi-UAV placement was formulated as a quadratic programming problem.
  • Sequential Quadratic Programming (SQP) algorithms were employed for optimization.

Main Results:

  • The proposed method allows UAVs to efficiently self-localize in a cooperative manner.
  • The system successfully completes cooperative area coverage tasks.
  • The method balances optimal relative localization with optimal coverage control.

Conclusions:

  • The developed approach effectively addresses the challenge of multi-UAV cooperative area coverage with limited global positioning.
  • The integration of relative localization and coverage control is feasible and efficient.
  • The quadratic programming formulation provides an optimal solution for UAV placement.