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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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Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

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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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Types of Global Positioning System Surveys01:30

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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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Absolute Motion Analysis- General Plane Motion01:24

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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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Video Experimental Relacionado

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: Un enfoque de optimización global para la planificación eficiente de rutas de UAV en entornos urbanos de

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
Resumen

Este estudio introduce un algoritmo mejorado de búsqueda de águila calva (EAB-BES) para la planificación de rutas de UAV en 3D en áreas urbanas. El algoritmo EAB-BES mejora significativamente la velocidad de convergencia y genera trayectorias de vuelo óptimas y sin colisiones.

Palabras clave:
algoritmo de búsqueda de águila calvaLa mutación diferencial guiada por la élite basada en bloquesmetaheurísticoMejora de las estrategias múltiplesPlanificación del recorridovehículos aéreos no tripulados

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Área de la Ciencia:

  • Robótica y automatización
  • Inteligencia artificial
  • Ingeniería Aeroespacial

Sus antecedentes:

  • El algoritmo tradicional de búsqueda de águila calva (BES) sufre de convergencia lenta y problemas de óptimas locales.
  • Los entornos urbanos complejos plantean desafíos significativos para la planificación de rutas de vehículos aéreos no tripulados (UAV).
  • Los algoritmos existentes a menudo luchan con la adaptabilidad y la eficiencia en entornos urbanos densos.

Objetivo del estudio:

  • Desarrollar un algoritmo de búsqueda de águilas calvas mejorado de múltiples estrategias (EAB-BES) para mejorar la planificación de rutas de UAV en 3D.
  • Abordar las limitaciones del algoritmo BES tradicional en entornos urbanos.
  • Mejorar la velocidad de convergencia, la precisión de la trayectoria y la adaptabilidad para la navegación de UAV.

Principales métodos:

  • Aprendizaje basado en la oposición de élite para una solución mejorada de exploración espacial.
  • Mecanismo de ponderación adaptativo para equilibrar la búsqueda global y la explotación local.
  • Una mutación diferencial guiada por la élite para una búsqueda local refinada.

Principales resultados:

  • EAB-BES demostró la velocidad de convergencia más rápida y los valores de aptitud estable más bajos.
  • El algoritmo generó los caminos 3D más cortos, suaves y sin colisiones.
  • Los experimentos comparativos en seis entornos urbanos de alta densidad validaron un rendimiento superior frente a nueve algoritmos avanzados.

Conclusiones:

  • EAB-BES ofrece una mejora significativa con respecto al algoritmo BES tradicional para la planificación de rutas de UAV.
  • El algoritmo mejorado proporciona una solución eficiente, confiable y robusta para la navegación autónoma en entornos urbanos complejos.
  • El análisis estadístico confirma el rendimiento superior y la competitividad de EAB-BES.