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Development of an autonomous mobile robot outdoor navigation system.

机译:自主移动机器人户外导航系统的开发。

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摘要

Navigation is a fundamental capability that mobile robots must possess. Outdoor navigation is especially challenging due to numerous uncertainties and dynamic changes in the environment such as varying lightning conditions. A robust outdoor navigation system utilizing navigational sensors such as GPS, vision, laser, and sonar improves autonomy of robots and their task handling capabilities. This thesis focuses on the development and testing of an outdoor navigation system for mobile robots that improves autonomy. This navigation system enables a mobile robot equipped with GPS, laser and sonar range finders, and vision system to navigate from a known initial position to a given target location in an outdoor environment. Waypoints that the robot will track are created in XML format by waypoints generator web interface and are given to robot prior to starting movement. The real-time tracking web interface continuously displays the position of the robot on the map by utilizing the GPS readings. An artificial neural network was designed and developed for detecting roads. Real-time computer vision algorithms were developed for online path planning and obstacle avoidance. The developed system was also tested for road detection success rate and eighty seven (87%) percent of successful road center detection rate was accomplished. The system has been successfully implemented and tested using a Pioneer 3-AT mobile robot in outdoor environments.
机译:导航是移动机器人必须具备的基本功能。由于许多不确定因素和环境动态变化(例如变化的雷电条件),户外导航尤其具有挑战性。强大的户外导航系统利用GPS,视觉,激光和声纳等导航传感器提高了机器人的自主性及其任务处理能力。本文着重于开发和测试用于移动机器人的户外导航系统,该系统可提高自主性。该导航系统使配备GPS,激光和声纳测距仪以及视觉系统的移动机器人能够在室外环境中从已知的初始位置导航到给定的目标位置。机器人将跟踪的航路点通过航路点生成器Web界面以XML格式创建,并在开始移动之前将其提供给机器人。实时跟踪Web界面利用GPS读数连续在地图上显示机器人的位置。设计并开发了用于检测道路的人工神经网络。开发了实时计算机视觉算法,用于在线路径规划和避障。还对开发的系统进行了道路检测成功率的测试,并完成了百分之八十七(87%)的成功道路中心检测率。该系统已在室外环境中使用Pioneer 3-AT移动机器人成功实施和测试。

著录项

  • 作者

    Caglar, Faruk.;

  • 作者单位

    Tennessee State University.;

  • 授予单位 Tennessee State University.;
  • 学科 Engineering Robotics.
  • 学位 M.S.
  • 年度 2008
  • 页码 142 p.
  • 总页数 142
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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