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首页> 外文期刊>Aerospace science and technology >A mixed probabilistic-geometric strategy for UAV optimum flight path identification based on bit-coded basic manoeuvres
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A mixed probabilistic-geometric strategy for UAV optimum flight path identification based on bit-coded basic manoeuvres

机译：基于位编码基本机动的无人机概率最优混合航迹识别混合策略

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This paper presents a novel algorithm identifying optimal flight trajectories for Unmanned Aerial Vehicles compliant with environmental constraints. Such constraints are defined in terms of obstacles, fixed way points and selected destination points. Optimality is evaluated taking the minimum path length as the specific objective function. The proposed path planning strategy is based on an original trajectory modelling coupled with a Particle Swarm optimizer (PSO). Flight paths starting from a specified point and ending at a selected destination point are divided into a finite number of segments made up of circular arcs and straight lines. In the proposed approach such a geometrical sequence is replaced with a finite sequence of binary-coded basic manoeuvres. This novel formulation allows to easily handle the manoeuvres sequence with a fixed number of integer variables taking advantage of PSO capability in handling discrete variables; moreover the use of mixed-type variables provides the optimization procedure a useful flexibility in the "decision making" modelling and operational scenarios definition as well. Specific geometric-based linear obstacle avoidance models have been implemented in addition to suitable penalty functions. The use of these models forces each path to be consistent with the environmental constraints favouring the identification of feasible trajectories with a reduced number of iterations and particles. The path planning model has been developed with particular care devoted to reduce computational effort as well as to improve algorithm capability in handling general-shaped obstacles both in 2-D and 3-D environments. Various applications have been performed in order to test the effectiveness of the proposed flight path generator. Applicability of the proposed optimization model also to vehicles with VTOL and hovering capabilities has been preliminarily assessed. (C) 2017 Elsevier Masson SAS. All rights reserved.
机译：本文提出了一种新颖的算法，可识别符合环境约束的无人机的最佳飞行轨迹。根据障碍物，固定路线点和选定的目的地点定义此类约束。以最小路径长度作为特定目标函数来评估最佳性。所提出的路径规划策略基于原始轨迹模型并结合了粒子群优化器（PSO）。从指定点开始并在选定的目的地点结束的飞行路径分为有限数量的由圆弧和直线组成的线段。在所提出的方法中，这样的几何序列被二进制编码的基本动作的有限序列代替。这种新颖的公式允许利用PSO处理离散变量的能力，利用固定数量的整数变量轻松处理操纵序列。此外，混合类型变量的使用为优化过程提供了“决策”建模和操作场景定义方面的有用灵活性。除了合适的惩罚函数外，还实施了基于特定几何的线性避障模型。这些模型的使用迫使每条路径与环境约束条件保持一致，从而有利于以减少的迭代次数和粒子数来确定可行的轨迹。路径规划模型的开发特别谨慎，旨在减少计算工作量以及提高2D和3D环境中处理通用障碍物的算法能力。为了测试所提出的飞行路径发生器的有效性，已经进行了各种应用。初步评估了所提出的优化模型对具有VTOL和悬停功能的车辆的适用性。 （C）2017 Elsevier Masson SAS。版权所有。

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