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NMPC and genetic algorithm based approach for trajectory tracking and collision avoidance of UAVs

机译:基于NMPC和遗传算法的无人机轨迹跟踪与避碰方法

摘要

Research on unmanned aircraft is improving constantly the autonomous flight capabilities of these vehicles in order to provide performance needed to employ them in even more complex tasks. UAV Path Planning (PP) system plans the best path to per- form the mission and then it uploads this path on the Flight Management System (FMS) providing reference to the aircraft navigation. Tracking the path is the way to link kine- matic references related to the desired aircraft positions with its dynamic behaviours, to generate the right command sequence. This paper presents a Nonlinear Model Predictive Control (NMPC) system that tracks the reference path provided by PP and exploits a spherical camera model to avoid unpredicted obstacles along the path. The control sys- tem solves on-line (i.e., at each sampling time) a finite horizon (state horizon) open loop optimal control problem with a Genetic Algorithm. This algorithm finds the command sequence that minimises the tracking error with respect to the reference path, driving the aircraft far from sensed obstacles and towards the desired trajectory.
机译:对无人飞机的研究正在不断改善这些车辆的自主飞行能力,以提供将其用于更复杂的任务所需的性能。无人机路径规划(PP)系统规划执行任务的最佳路径,然后将该路径上传到飞行管理系统(FMS)中,以提供对飞机导航的参考。跟踪路径是将与所需飞机位置有关的运动参考与其动态行为联系起来以生成正确的命令序列的方式。本文提出了一种非线性模型预测控制(NMPC)系统,该系统跟踪PP提供的参考路径,并利用球形摄像机模型来避免沿路径的意外障碍。该控制系统通过遗传算法在线(即在每个采样时间)解决有限水平(状态水平)开环最优控制问题。该算法找到的命令序列可最大程度地减少相对于参考路径的跟踪误差,从而使飞机远离感测到的障碍物并朝向所需的轨迹运动。

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