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Multi-Objective Design and Path Planning Optimization of Unmanned Aerial Vehicles (UAVs)

机译:无人机航空车辆(无人机)的多目标设计和路径规划优化

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This paper presents the topic of combined UAV design and path planning using bi-objective optimization. Two optimization models, integrated and decomposed, are developed and solved for path planning and design optimization of UAVs. Both models minimize a weighted sum of risk and time. The risk is based on a simulation of a UAV's crash after it loses power and is estimated by computing an expected number of on-the-ground human population fatalities which are located at or near the UAV crash site. The flight time is computed for the UAV as it travels from a start to an end point. The integrated model solves the design and path planning optimization problem all-at-once. The decomposed model has two subproblems: one for the design optimization of UAV and another for its path planning optimization. A new decomposed method for solving the path planning subproblem while considering the design optimization subproblem is presented, which solves the path planning subproblem for a discrete graph. Results for this new method are compared to an integrated all-at-once approach that does not treat the design and path planning subproblems separately, which can indicate the method proposed is less vulnerable to local minima in the path planning optimization subproblem.
机译:本文介绍了使用双目标优化组合UAV设计和路径规划的主题。为无人机的路径规划和设计优化而开发并解决了两种优化模型,集成和分解。两种型号最小化加权风险和时间的总和。风险基于在失去电力后的无人机碰撞的基础上,通过计算位于无人机碰撞部位或附近的地面的接地人口死亡人数的预期数量。当它从开始到终点开始时,为UAV计算飞行时间。集成模型解决了全能全部设计和路径规划优化问题。分解模型有两个子问题:一个用于设计优化UAV,另一个用于其路径规划优化。提出了一种新的分解方法,用于在考虑设计优化子问题的同时求解路径规划子问题,这解决了离散图的路径规划子问题。这种新方法的结果与一体化的全部方法进行了比较,该方法不分别处理设计和路径规划子问题,这可以指示所提出的方法在路径规划优化子问题中,所提出的方法更容易受到局部最小值。

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