An Autonomous Soaring for Small Drones Using the Extended Kalman Filter Thermal Updraft Center Prediction Method Based on Ordinary Least Squares

IF 4.4 2区 地球科学 Q1 REMOTE SENSING
Drones Pub Date : 2023-09-26 DOI:10.3390/drones7100603
Weigang An, Tianyu Lin, Peng Zhang
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Abstract

Many birds in the natural world are capable of engaging in sustained soaring within thermal updrafts for extended periods without flapping their wings. Autonomous soaring has the potential to greatly improve both the range and endurance of small drones. In this paper, the extended Kalman filter (EKF) thermal updraft center prediction method based on ordinary least squares (OLS) is proposed to develop the autonomous soaring system for small drones, and an adaptive step size update strategy is incorporated into the EKF. The proposed method is compared with EKF thermal updraft prediction methods through simulated experiments. The results indicate that the proposed prediction method has low computational complexity and fast convergence speed and performs more stably in weak thermal updrafts. The above advantages stem from the OLS providing an approximate distribution of the thermal updraft around the drone for the EKF. This empowers the EKF algorithm with ample information to dynamically update the thermal updraft center in real time. The adaptive step size update strategy further accelerates the convergence speed of this process. In addition, flight experiments were conducted on the Talon fixed-wing drone platform to test the autonomous soaring system. During the flight experiment, the drone successfully engaged in static soaring within thermal updrafts, effectively hovering and gaining energy. Throughout the approximately 40 min flight duration, the drone only utilized its propulsion for about 8 min. This demonstrated the effectiveness of the autonomous soaring system using the EKF thermal updraft center prediction method based on OLS. Finally, by analyzing and discussing the differences between the simulation experiment results and the flight experiment results, some improvement strategies for the current work are proposed.
基于普通最小二乘的扩展卡尔曼滤波热上升气流中心预测方法在小型无人机自主飞行中的应用
自然界中的许多鸟类能够在不拍打翅膀的情况下,在热上升气流中长时间持续翱翔。自主飞行有可能大大提高小型无人机的航程和续航能力。提出了基于普通最小二乘(OLS)的扩展卡尔曼滤波(EKF)热上升气流中心预测方法,并在EKF中引入了自适应步长更新策略。通过模拟实验,将该方法与EKF热上升气流预测方法进行了比较。结果表明,该方法计算复杂度低,收敛速度快,在弱热上升气流中表现稳定。上述优点源于OLS为EKF提供了无人机周围热上升气流的近似分布。这使EKF算法具有充足的信息,可以实时动态更新热上升气流中心。自适应步长更新策略进一步加快了该过程的收敛速度。此外,在“魔爪”固定翼无人机平台上进行了飞行实验,对自主翱翔系统进行了测试。在飞行实验中,无人机成功地在热上升气流中进行静态翱翔,有效地悬停并获得能量。在大约40分钟的飞行时间内,无人机只利用了大约8分钟的推进力。这证明了基于OLS的EKF热上升气流中心预测方法的自主翱翔系统的有效性。最后,通过分析和讨论仿真实验结果与飞行实验结果的差异,提出了当前工作的改进策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Drones
Drones Engineering-Aerospace Engineering
CiteScore
5.60
自引率
18.80%
发文量
331
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