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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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Updated: Sep 28, 2025

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Path Planning in Localization Uncertaining Environment Based on Dijkstra Method.

Can Wang1, Chensheng Cheng1, Dianyu Yang1

  • 1School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China.

Frontiers in Neurorobotics
|April 1, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Dijkstra-based path planning algorithm to combat path drift caused by localization uncertainty in GPS-denied environments. The method effectively reduces cumulative error in real-world scenarios with measurement noise.

Keywords:
Dijkstracumulative error estimationglobal planninggreedy searchpath planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Navigation Systems

Background:

  • Path planning algorithms generate robot trajectories using kinematics and environmental data.
  • Localization uncertainty from odometry sensors causes path drift, especially in GPS-denied environments.

Purpose of the Study:

  • To propose a novel path planning algorithm addressing path drift due to localization uncertainty.
  • To reduce cumulative error in robot navigation within GPS-denied environments.

Main Methods:

  • A Dijkstra-based path planning algorithm was developed.
  • The algorithm incorporates statistical characteristics of dead-reckoning localization error.
  • Replanned paths minimize cumulative error by utilizing uniform distribution in the search space.

Main Results:

  • Simulations confirmed the effectiveness of the proposed algorithm.
  • Real-world tests with measurement noise demonstrated reduced cumulative error compared to conventional methods.

Conclusions:

  • The novel algorithm successfully mitigates path drift caused by localization uncertainty.
  • This approach offers improved path accuracy for robots operating in challenging navigation conditions.