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Related Concept Videos

Field Application of Global Positioning System01:28

Field Application of Global Positioning System

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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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Design Example: Alignment of a Road Line Using GIS01:17

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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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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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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Distributed Loads: Problem Solving01:21

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Related Experiment Video

Updated: Sep 10, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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EAB-BES: A Global Optimization Approach for Efficient UAV Path Planning in High-Density Urban Environments.

Yunhui Zhang1,2, Wenhong Xiao1,2, Shihong Yin3

  • 1School of Internet, Jiaxing Vocational and Technical College, Jiaxing 314036, China.

Biomimetics (Basel, Switzerland)
|August 27, 2025
PubMed
Summary

This study introduces an enhanced bald eagle search (EAB-BES) algorithm for 3D UAV path planning in urban areas. The EAB-BES algorithm significantly improves convergence speed and generates optimal, collision-free flight paths.

Keywords:
bald eagle search algorithmblock-based elite-guided differential mutationmetaheuristicmulti-strategy enhancedpath planningunmanned aerial vehicle

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Traditional bald eagle search (BES) algorithm suffers from slow convergence and local optima issues.
  • Complex urban environments pose significant challenges for Unmanned Aerial Vehicle (UAV) path planning.
  • Existing algorithms often struggle with adaptability and efficiency in dense urban settings.

Purpose of the Study:

  • To develop a multi-strategy enhanced bald eagle search (EAB-BES) algorithm for improved 3D UAV path planning.
  • To address the limitations of the traditional BES algorithm in urban environments.
  • To enhance convergence speed, path accuracy, and adaptability for UAV navigation.

Main Methods:

  • Elite opposition-based learning for enhanced solution space exploration.
  • Adaptive weight mechanism to balance global search and local exploitation.
  • Block-based elite-guided differential mutation for refined local search.

Main Results:

  • EAB-BES demonstrated the fastest convergence speed and lowest stable fitness values.
  • The algorithm generated the shortest, smoothest, and collision-free 3D paths.
  • Comparative experiments in six high-density urban environments validated superior performance against nine advanced algorithms.

Conclusions:

  • EAB-BES offers a significant improvement over the traditional BES algorithm for UAV path planning.
  • The enhanced algorithm provides an efficient, reliable, and robust solution for autonomous navigation in complex urban environments.
  • Statistical analysis confirms the superior performance and competitiveness of EAB-BES.