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Optimal trajectory planning for a UAV glider using atmospheric thermals.

机译：使用大气热学的无人机滑翔机的最佳轨迹规划。

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摘要

An Unmanned Aerial Vehicle Glider (UAV glider) uses atmospheric energy in its different forms to remain aloft for extended flight durations. This UAV glider's aim is to extract atmospheric thermal energy and use it to supplement its battery energy usage and increase the mission period.;Given an infrared camera identified atmospheric thermal of known strength and location; current wind speed and direction; current battery level; altitude and location of the UAV glider; and estimating the expected altitude gain from the thermal, is it possible to make an energy-efficient based motivation to fly to an atmospheric thermal so as to achieve UAV glider extended flight time?;For this work, an infrared thermal camera aboard the UAV glider takes continuous forward-looking ground images of "hot spots". Through image processing a candidate atmospheric thermal strength and location is estimated. An Intelligent Decision Model incorporates this information with the current UAV glider status and weather conditions to provide an energy-based recommendation to modify the flight path of the UAV glider. Research, development, and simulation of the Intelligent Decision Model is the primary focus of this work.;Three models are developed: (1) Battery Usage Model, (2) Intelligent Decision Model, and (3) Altitude Gain Model. The Battery Usage Model comes from the candidate flight trajectory, wind speed & direction and aircraft dynamic model. Intelligent Decision Model uses a fuzzy logic based approach. The Altitude Gain Model requires the strength and size of the thermal and is found a priori.
机译：无人机滑翔机（UAV滑翔机）使用各种形式的大气能量来保持高空飞行，以延长飞行时间。该无人机滑翔机的目的是提取大气热能，并利用其补充电池能量的使用并增加任务期限。当前风速和风向；当前电池电量；无人机滑翔机的高度和位置；并估算从热能获得的预期高度增益，是否有可能基于能量效率的动机飞向大气热能，从而实现无人机滑翔机延长的飞行时间？；为此，在无人机滑翔机上安装了红外热像仪连续拍摄“热点”的前瞻性地面图像。通过图像处理，估算候选大气热强度和位置。智能决策模型将此信息与当前无人机滑翔机的状态和天气状况结合在一起，以提供基于能量的建议来修改无人机滑翔机的飞行路线。智能决策模型的研究，开发和仿真是本工作的重点。开发了三个模型：（1）电池使用模型，（2）智能决策模型和（3）海拔增益模型。电池使用模型来自候选飞行轨迹，风速和方向以及飞机动力学模型。智能决策模型使用基于模糊逻辑的方法。海拔增益模型需要热量的强度和大小，并且是先验的。

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  • 作者

    Kagabo, Wilson B.;

  • 作者单位

    Rochester Institute of Technology.;

  • 授予单位 Rochester Institute of Technology.;
  • 学科 Engineering Aerospace.
  • 学位 M.S.
  • 年度 2010
  • 页码 132 p.
  • 总页数 132
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 公共建筑;
  • 关键词

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