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Errors in Global Positioning System01:26

Errors in Global Positioning System

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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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The Global Positioning System (GPS) revolutionized positioning on Earth, providing precise location data through satellite ranging. The GPS system was developed in 1978 by the U.S. Department of Defense  for military use, and it became available for civilian applications in 1983, transforming fields including navigation, fleet management, and time synchronization for telecommunications systems.GPS consists of satellites in medium Earth orbit, about 20,200 kilometers above the surface,...
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Learning Autonomous Navigation in Unmapped and Unknown Environments.

Naifeng He1, Zhong Yang1, Chunguang Bu2

  • 1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

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|September 28, 2024
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Summary
This summary is machine-generated.

This study introduces PEEMEF-DARC, an autonomous navigation algorithm for mobile robots. It enables effective navigation in unknown environments using only low-precision sensors, without maps or prior knowledge.

Keywords:
maplessmobile robotnavigationreinforcement learning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Navigation Systems

Background:

  • Intelligent mobile robots require autonomous decision-making for navigation.
  • Navigating mapless or low-precision environments with limited sensors presents a significant challenge.

Purpose of the Study:

  • To develop an innovative autonomous navigation algorithm for mapless environments.
  • To enable mobile robots to navigate unknown areas without prior maps or precise localization.

Main Methods:

  • Proposed PEEMEF-DARC algorithm comprising Double Actors Regularized Critics (DARC).
  • Implemented a priority-based excellence experience data collection mechanism.
  • Utilized a multi-source experience fusion strategy mechanism.

Main Results:

  • The algorithm successfully performs autonomous navigation in unmapped and unknown environments.
  • Enhanced exploration capabilities and mitigated state-action value overestimation through regularization.
  • Significantly reduced training time via specialized data collection and fusion modules.

Conclusions:

  • PEEMEF-DARC effectively navigates unmapped and unknown environments.
  • The method achieves reliable navigation without reliance on maps or precise localization.
  • Demonstrates a viable solution for autonomous robot navigation in challenging conditions.