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Trajectory optimization of an unmanned aerial–aquatic rotorcraft navigating between air and water
International Journal of Advanced Robotic Systems ( IF 2.1 ) Pub Date : 2021-03-15 , DOI: 10.1177/1729881421992258
Xichao Su 1 , Yu Wu 2, 3 , Fang Guo 1 , Jiapeng Cui 2 , Ge Yang 4
Affiliation  

Unmanned aerial–aquatic vehicles are a new type of aircraft that can navigate in air and underwater. An unmanned aerial–aquatic rotorcraft (UAAR) is introduced to complete the task of navigating between air and underwater, and the trajectory optimization problem for this task is focused on in this study. The dynamics of a four-axle rotorcraft with eight rotors operating in air and underwater is described. On this basis, the trajectory optimization model is established, wherein the constraints on control variables and states in different media are included. The optimization index is denoted as the weighted sum of the terminal states. In view of the weakness of the teaching- and learning-based optimization (TLBO) algorithm, the formula for updating the individual grade in the teaching process is modified. Thus, this ensures that the algorithm avoids converging at the local optimum and improves the solution quality. Finally, an improved TLBO (ITLBO)-based trajectory optimization method for UAAR navigating between air and water is developed. The control variables are discretized with respect to height at a set of Chebyshev collocation points to reduce the terminal error of states, and the values of control variables at other heights are obtained via interpolation. In the simulation studies, the ITLBO-based method exhibits better performance in terms of optimizing the index when compared to the other two algorithms. Furthermore, the effects of the distribution and number of collocation points on the results are analyzed.



中文翻译:

空中和水上航行的无人机水上旋翼机的轨迹优化

无人机是一种可以在空中和水下航行的新型飞机。引入无人空中水上旋翼机(UAAR)来完成空中和水下之间的导航任务,并且本研究着重于此任务的轨迹优化问题。描述了具有八个在空中和水下运行的旋翼的四轴旋翼飞行器的动力学。在此基础上,建立了轨迹优化模型,其中包括了对不同介质中控制变量和状态的约束。最优化指数表示为终端状态的加权和。鉴于基于教与学的优化(TLBO)算法的缺点,修改了在教学过程中更新个人成绩的公式。因此,这确保了算法避免收敛于局部最优值并提高了求解质量。最后,开发了一种改进的基于TLBO(ITLBO)的UAAR在水和空气之间导航的轨迹优化方法。控制变量相对于一组切比雪夫搭配点的高度离散化,以减少状态的最终误差,而其他高度的控制变量的值则通过插值获得。在仿真研究中,与其他两种算法相比,基于ITLBO的方法在优化索引方面表现出更好的性能。此外,分析了分布点和并置点数对结果的影响。提出了一种改进的基于TLBO(ITLBO)的UAAR在水和空气之间导航的轨迹优化方法。控制变量相对于一组切比雪夫搭配点的高度离散化,以减少状态的最终误差,而其他高度的控制变量的值则通过插值获得。在仿真研究中,与其他两种算法相比,基于ITLBO的方法在优化索引方面表现出更好的性能。此外,分析了分布点和并置点数对结果的影响。提出了一种改进的基于TLBO(ITLBO)的UAAR在水和空气之间导航的轨迹优化方法。控制变量相对于一组切比雪夫搭配点的高度离散化,以减少状态的最终误差,而其他高度的控制变量的值则通过插值获得。在仿真研究中,与其他两种算法相比,基于ITLBO的方法在优化索引方面表现出更好的性能。此外,分析了分布点和并置点数对结果的影响。通过插值获得其他高度的控制变量的值。在仿真研究中,与其他两种算法相比,基于ITLBO的方法在优化索引方面表现出更好的性能。此外,分析了分布点和并置点数对结果的影响。通过插值获得其他高度的控制变量的值。在仿真研究中,与其他两种算法相比,基于ITLBO的方法在优化索引方面表现出更好的性能。此外,分析了分布点和并置点数对结果的影响。

更新日期:2021-03-16
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