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Development of a Mars Exploration Rover for RASC-AL Exploration RoboOps Competition with an Extended Kalman Filter Based Navigation System.

机译:使用扩展的基于卡尔曼滤波的导航系统开发用于RASC-AL探险机器人竞赛的火星探测漫游车。

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摘要

The primary objective of this research is to develop a navigation system for use on a rover designed for the NASA-NIA Revolutionary Aerospace Systems Concepts Academic Linkage (RASC-AL) Exploration RoboOps challenge. This competition takes place at NASA's Johnson Space Center Rock Yard, a test facility with many obstacles including steep hills, craters, rocks and loose sandy terrain. In addition to the challenges of the terrain, the rover's operators must control it from West Virginia University's campus. It was observed during the 2012 competition that a primary challenge was a lack of situational awareness; the operators had to navigate the large test area with only delayed video from two low resolution cameras. This research seeks to provide better awareness of the rover's position in the rock yard through the development of an accurate navigation assistance system.;This research first presents the rover that was designed, developed, and demonstrated at the 2012 competition that features six-wheel-drive, four-wheel steering, and a rocker-bogie suspension system similar to NASA rovers currently operating on Mars.;This research then presents a Navigation Assistance System (NAS) capable of providing nine-state navigation data, using small, lightweight, and low-cost sensors. In order to achieve an optimal estimate of the rover's attitude, velocity, and position this system employs an Extended Kalman Filter (EKF) that fuses data from an Inertial Measurement Unit (IMU) and a Global Positioning System (GPS). The EKF utilizes complimentary data from both systems to minimize the degradation caused by inherent system error that would be experienced by using either system alone.;The primary output of the NAS, position, is evaluated against a high-quality dual-antenna, Differential GPS (DGPS) receiver, as well as a low-cost single antenna GPS receiver. The NAS was observed to provide smoother position output, however actual error from the DGPS was only slightly reduced; in the nominal case an improvement of 0.2 meters was observed. Furthermore, testing demonstrated that the overall accuracy of the GPS alone, in comparison with the DGPS unit, was observed to be 1.7 meters. For the intended purposes of the system, it was found that using GPS alone would be sufficient for navigation applications.;The integration of this system with the rover brought unexpected challenges stemming from electrical interference created by on-board electronics, therefore this work presents the results of the navigation assistance system development taken off-board the rover. Furthermore, it presents research on autonomous navigation algorithms that could implement the state estimation data provided by the navigation assistance system that was developed.
机译:这项研究的主要目的是开发一种用于漫游车的导航系统,该漫游车是为NASA-NIA革命性航空系统概念学术联系(RASC-AL)探索RoboOps挑战而设计的。这项比赛在美国国家航空航天局(NASA)的约翰逊太空中心岩石场(Johnson Space Center Rock Yard)进行,这是一个测试设施,有很多障碍,包括陡峭的山丘,火山口,岩石和松散的沙地。除了地形方面的挑战外,流动站的操作员还必须在西弗吉尼亚大学的校园内进行控制。在2012年比赛中观察到,主要挑战是缺乏态势意识。操作员只能用两个低分辨率摄像机的延迟视频在较大的测试区域中导航。这项研究旨在通过开发精确的导航辅助系统来更好地了解流动站在岩石堆场中的位置。该研究首先介绍在2012年竞赛中设计,开发和演示的流动站,其特点是六轮与目前在火星上运行的NASA漫游车类似的驱动,四轮转向和摇臂转向架悬架系统;然后,本研究提出了一种导航辅助系统(NAS),该系统可以使用小型,轻便和轻巧的系统提供九态导航数据低成本传感器。为了获得对流动站姿态,速度和位置的最佳估计,该系统采用扩展卡尔曼滤波器(EKF),该滤波器融合了来自惯性测量单元(IMU)和全球定位系统(GPS)的数据。 EKF利用来自两个系统的互补数据来最大程度地减少单独使用任何一个系统都会遇到的固有系统错误导致的性能下降; NAS的主要输出位置是根据高质量双天线差分GPS进行评估的(DGPS)接收器,以及低成本的单天线GPS接收器。观察到NAS提供了更平滑的位置输出,但是DGPS的实际误差仅得到了轻微的降低;在正常情况下,观察到了0.2米的改善。此外,测试表明,与DGPS单元相比,仅GPS的整体精度为1.7米。对于系统的预期目的,发现仅使用GPS就足以用于导航应用。该系统与流动站的集成带来了由车载电子设备产生的电干扰带来的意想不到的挑战,因此这项工作提出了漫游辅助系统的开发结果从流动站外获取。此外,它提出了关于自主导航算法的研究,该算法可以实现由开发的导航辅助系统提供的状态估计数据。

著录项

  • 作者

    Knabenshue, Benjamin J.;

  • 作者单位

    West Virginia University.;

  • 授予单位 West Virginia University.;
  • 学科 Engineering Computer.;Engineering Electronics and Electrical.
  • 学位 M.S.
  • 年度 2013
  • 页码 146 p.
  • 总页数 146
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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