Autonomous drone race: A computationally efficient vision-based navigation and control strategy
Autonomous drone race: A computationally efficient vision-based navigation and control strategy
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DOI：
10.1016/j.robot.2020.103621
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发表时间：
2020-11-01
影响因子：
4.3
通讯作者：
de Croon, Guido C. H. E.
中科院分区：
文献类型：
--
作者：
Li, Shuo;Ozo, Michael M. O., I;de Croon, Guido C. H. E.
无人机竞赛正在成为一项流行的运动，人类飞行员必须控制他们的无人机在复杂的环境中高速飞行，并按照预先定义的顺序通过多个登机口。在本文中，我们开发了一个自主系统，使无人机仅使用机载资源就可以完全自主地比赛。针对微型飞行器(MAV)常用的同时定位测绘和视觉惯性里程计等视觉导航方法计算量大的缺点，我们提出了一种高效的蛇门视觉导航算法，该算法可以在20 HZ处探测到鹦鹉Bebop无人机上的蛇门。然后，结合门检测的结果，我们提出了一种稳健的位姿估计算法，该算法比目前最先进的透视n点法具有更好的抗检测噪声能力。在比赛中，有时大门不在无人机的视野内。针对这种情况，提出了一种基于状态预测的前馈控制策略来引导无人机飞向下一个登机口。实验表明，该无人机可以在2 S范围内飞行半径为1.5m的半圆，圆端误差仅为30 cm，不需要任何位置反馈。最后，整个系统在一个复杂的环境中进行了测试(德尔夫特大学航天工程学院的一个展厅)。结果显示，该无人机可以以1.5m/S的速度完成15个门的轨迹，这一速度快于2016年和2017年IROS自主无人机比赛的速度。(C)2020爱思唯尔B.V.保留所有权利。
Drone racing is becoming a popular sport where human pilots have to control their drones to fly at high speed through complex environments and pass a number of gates in a pre-defined sequence. In this paper, we develop an autonomous system for drones to race fully autonomously using only onboard resources. Instead of commonly used visual navigation methods, such as simultaneous localization and mapping and visual inertial odometry, which are computationally expensive for micro aerial vehicles (MAVs), we developed the highly efficient snake gate detection algorithm for visual navigation, which can detect the gate at 20 HZ on a Parrot Bebop drone. Then, with the gate detection result, we developed a robust pose estimation algorithm which has better tolerance to detection noise than a state-of-the-art perspective-n-point method. During the race, sometimes the gates are not in the drone's field of view. For this case, a state prediction-based feed-forward control strategy is developed to steer the drone to fly to the next gate. Experiments show that the drone can fly a half-circle with 1.5 m radius within 2 s with only 30 cm error at the end of the circle without any position feedback. Finally, the whole system is tested in a complex environment (a showroom in the faculty of Aerospace Engineering, TU Delft). The result shows that the drone can complete the track of 15 gates with a speed of 1.5 m/s which is faster than the speeds exhibited at the 2016 and 2017 IROS autonomous drone races. (C) 2020 Elsevier B.V. All rights reserved.