本文为澳大利亚悉尼大学（作者：Nicholas R.J. Lawrance）的博士论文，共233页。
无人机（UAV）在一系列工业、科学和国防应用中提供了独特的能力。小型无人机可以通过滑翔来延长飞行时间，而不需要额外的推进力。本文研究了高空飞行的气动机理，提出了无人机自主感知和利用风环境来延长飞行时间的规划和控制算法。
为了利用滑翔飞行，需要对飞机和周围大气之间的能量相互作用有一个透彻的了解。本文提出了一种滑行飞行器的数学模型，并研究了风是如何影响飞行能量变化的。确定了直线风切变条件下滑翔和腾空飞行的最佳能量效率条件。
所提出的路径规划器利用滑翔飞机的能量方程，在已知的风场上规划节能路径。以前的飞行规划者都把重点放在单一类型的能量增益上，比如静态的翱翔。通过直接使用能量方程，规划器可以利用所有的能量增益条件，而不是依赖于专门的控制器。
规划者需要对风场进行充分估计，以规划可靠的能量增益路径。小型无人机通常只能在飞行过程中直接观测风场的状态。提出了一种基于高斯过程（GP）回归的直接风场生成方法。这种无模型方法可以考虑静态和动态风场，并且不限于特定类型的风场结构。
保持一张准确的地图需要规划者确保有效的地图采样，并保持足够的能量继续飞行。路径规划算法利用GP图的方差估计来识别地图中需要改进的区域。规划者评估飞机的能量状态和当前地图，以确定风场的目标区域，以便进一步勘探或开发能源。
结果表明，该结构能够在静态和动态风场中产生能量增益路径。映射算法记录风场的直接样本，生成风场图，规划算法使用该地图同时探索和开发风场，以延长飞行时间而无需推进功率。
Unmanned Aerial Vehicles (UAVs) provide unique capabilities in a range of industrial,scientific and defence applications. A small UAV could extend flight duration without requiring additional propulsive power through the use of soaring. This thesis examines the aerodynamic mechanisms of soaring flight and proposes planning and control algorithms for a UAV to autonomously sense and utilise the wind environment to extend flight duration.
In order to utilise soaring a thorough understanding of the energy interaction between an aircraft and the surrounding atmosphere is required. This thesis presents a mathematical model for a gliding aircraft and examines how wind contributes to the energy change of an aircraft. Conditions for optimal energy efficiency are identified for gliding and soaring flight in linear wind shear.
The proposed path planner takes advantage of the energy equations for a gliding aircraft to plan energy efficient paths over a known wind field. Previous soaring planners have focused on a single type of energy gain such as static soaring. By using the energy equations directly the planner can exploit all energy gain conditions rather than relying on specialised controllers.
The planner requires an adequate estimate of the wind field to plan reliable energy gain paths. A small UAV would typically only have access to direct wind observations taken during flight. Gaussian Process (GP) regression is proposed to generate a wind map from direct wind observations. This model-free approach can account for static and dynamic wind fields and does not restrict the planner to particular types of wind structure.
Maintaining an accurate map requires the planner to ensure efficient map sampling and maintain sufficient energy to continue flight. The path planning algorithm exploits the variance estimate from the GP map to identify regions of the map which require improvement. The planner assesses the aircraft’s energy state and current map to determine target regions of the wind field for further exploration or energy exploitation.
Results demonstrate that this architecture is capable of generating energy-gain paths in both static and dynamic wind fields. The mapping algorithm records direct samples of the wind to generate a wind map that is used by the planning algorithm to simultaneously explore and exploit the wind field to extend flight duration without propulsive power.
引言
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