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Improving particle swarm optimization path planning through inclusion of flight mechanics.

机译:通过纳入飞行机制来改进粒子群优化路径规划。

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摘要

Military engagements are continuing the movement toward automated and unmanned vehicles for a variety of simple and complex tasks. This allows humans to stay away from dangerous situations and use their skills for more difficult tasks. One important piece of this strategy is the use of automated path planners for unmanned aerial vehicles (UAVs). Current UAV operation requires multiple individuals to control a single plane, tying up important human resources. Often paths are planned by creating waypoints for a vehicle to fly through, with the intention of doing reconnaissance while avoiding as much danger to the plane as possible. Path planners often plan routes without taking into consideration the UAV's ability to perform the maneuvers required to fly the specified waypoints, instead relying upon them to fly as close as possible.;This thesis presents a path planner solution incorporating vehicle mechanics to insure feasible flight paths. This path planner uses Particle Swarm Optimization (PSO) and digital pheromones to generate multiple three-dimensional flight paths for the operator to choose from. B-spline curves are generated using universal interpolation with each path waypoint representing a control point. The b-spline curve represents the flight path of the UAV. Each point along the curve is evaluated for fuel efficiency, threat avoidance, reconnaissance, terrain avoidance, and vehicle mechanics.;Optimization of the flight path occurs based on operator defined performance characteristics, such as maximum threat avoidance or minimum vehicle dynamics cost. These performance characteristics can be defined for each unique aircraft, allowing the same formulation to be used for any aircraft. The vehicle mechanics conditions considered are pull-out, glide, climb, and steady, level, co-ordinate turns. Calculating the flight mechanics requires knowing the velocity and angle of the plane, calculated using the derivative of the point on the curve. The flight mechanics of the path allows the path planner to determine whether the path exceeds the maximum load factor (G-force), minimum velocity (stall velocity), or the minimum turning radius. Comparing the results between PSO Path Planner with flight mechanics and PSO Path Planner without flight mechanics over five scenarios indicates an increase in the feasibility of the returned paths.;Visualizing the flight paths was improved by changing the original waypoint based visualization to a b-spline curve representation. Using b-spline curves allows for an accurate representation of the actual UAV flight path especially when considering turns. Operators no longer must create a mental representation of the flight path to match the waypoints.
机译:军事交战正在继续朝着自动化和无人驾驶汽车的方向发展,以完成各种简单和复杂的任务。这使人类可以远离危险情况,并利用他们的技能来完成更困难的任务。该策略的重要部分是将自动路径规划器用于无人飞行器(UAV)。当前的无人机操作需要多个人来控制一架飞机,从而占用重要的人力资源。通常通过创建飞行器的航路点来规划路径,以进行侦察,同时尽可能避免对飞机造成危险。路径规划师通常在规划路线时并未考虑无人机执行飞行指定航路点所需的机动能力,而是依靠它们尽可能近地飞行。本论文提出了一种结合了车辆力学以确保可行的飞行路径的路径规划师解决方案。 。该路径规划器使用粒子群优化(PSO)和数字信息素来生成多个三维飞行路径,供操作员选择。 B样条曲线是使用通用插值生成的,每个路径航路点代表一个控制点。 b样条曲线表示无人机的飞行路径。评估曲线上的每个点的燃油效率,避免威胁,侦察,避免地形和车辆力学。根据驾驶员定义的性能特征(例如最大程度地避免威胁或最小的车辆动力学成本)对飞行路线进行优化。可以为每架独特的飞机定义这些性能特征,从而将相同的公式应用于任何飞机。所考虑的车辆机械条件为拔出,滑行,爬升和稳定,水平,坐标转弯。计算飞行力学需要知道飞机的速度和角度,这是使用曲线上的点的导数计算的。路径的飞行机制允许路径规划者确定路径是否超过最大负载系数(G力),最小速度(失速)或最小转弯半径。比较具有五个飞行场景的具有飞行力学的PSO路径规划器和不具有飞行力学的PSO路径规划器之间的结果,表明返回路径的可行性有所提高。通过将基于原始航路点的可视化更改为b样条,可以改善飞行路径的可视化曲线表示。使用b样条曲线可以准确表示实际的无人机飞行路径,尤其是在考虑转弯时。运营商不再必须为飞行路线创建思维表征以匹配航路点。

著录项

  • 作者

    Holub, Joseph Scott.;

  • 作者单位

    Iowa State University.;

  • 授予单位 Iowa State University.;
  • 学科 Engineering Mechanical.
  • 学位 M.S.
  • 年度 2010
  • 页码 111 p.
  • 总页数 111
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:37:07

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