Observer-based Controller for VTOL-UAVs Tracking using Direct Vision-Aided Inertial Navigation Measurements

This paper proposes a novel observer-based controller for Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) designed to directly receive measurements from a Vision-Aided Inertial Navigation System (VA-INS) and produce the required thrust and rotational torque inputs. The VA-INS is composed of a vision unit (monocular or stereo camera) and a typical low-cost 6-axis Inertial Measurement Unit (IMU) equipped with an accelerometer and a gyroscope. A major benefit of this approach is its applicability for environments where the Global Positioning System (GPS) is inaccessible. The proposed VTOL-UAV observer utilizes IMU and feature measurements to accurately estimate attitude (orientation), gyroscope bias, position, and linear velocity. Ability to use VA-INS measurements directly makes the proposed observer design more computationally efficient as it obviates the need for attitude and position reconstruction. Once the motion components are estimated, the observer-based controller is used to control the VTOL-UAV attitude, angular velocity, position, and linear velocity guiding the vehicle along the desired trajectory in six degrees of freedom (6 DoF). The closed-loop estimation and the control errors of the observer-based controller are proven to be exponentially stable starting from almost any initial condition. To achieve global and unique VTOL-UAV representation in 6 DoF, the proposed approach is posed on the Lie Group and the design in unit-quaternion is presented. Although the proposed approach is described in a continuous form, the discrete version is provided and tested. Keywords: Vision-aided inertial navigation system, unmanned aerial vehicle, vertical take-off and landing, stochastic, noise, Robotics, control systems, air mobility, observer-based controller algorithm, landmark measurement, exponential stability

Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition

[EN] Over the last few years, several researchers have been developing protocols and applications in order to autonomously land unmanned aerial vehicles (UAVs). However, most of the proposed protocols rely on expensive equipment or do not satisfy the high precision needs of some UAV applications such as package retrieval and delivery or the compact landing of UAV swarms. Therefore, in this work, a solution for high precision landing based on the use of ArUco markers is presented. In the proposed solution, a UAV equipped with a low-cost camera is able to detect ArUco markers sized 56×56 cm from an altitude of up to 30 m. Once the marker is detected, the UAV changes its flight behavior in order to land on the exact position where the marker is located. The proposal was evaluated and validated using both the ArduSim simulation platform and real UAV flights. The results show an average offset of only 11 cm from the target position, which vastly improves the landing accuracy compared to the traditional GPS-based landing, which typically deviates from the intended target by 1 to 3 m.This work was funded by the Ministerio de Ciencia, Innovación y Universidades, Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018 , Spain, under Grant RTI2018-096384-B-I00.Wubben, J.; Fabra Collado, FJ.; Tavares De Araujo Cesariny Calafate, CM.; Krzeszowski, T.; Márquez Barja, JM.; Cano, J.; Manzoni, P. (2019). Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition. Electronics. 8(12):1-16. https://doi.org/10.3390/electronics8121532S116812Pan, X., Ma, D., Jin, L., & Jiang, Z. (2008). Vision-Based Approach Angle and Height Estimation for UAV Landing. 2008 Congress on Image and Signal Processing. doi:10.1109/cisp.2008.78Tang, D., Li, F., Shen, N., & Guo, S. (2011). UAV attitude and position estimation for vision-based landing. Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology. doi:10.1109/emeit.2011.6023131Gautam, A., Sujit, P. B., & Saripalli, S. (2014). A survey of autonomous landing techniques for UAVs. 2014 International Conference on Unmanned Aircraft Systems (ICUAS). doi:10.1109/icuas.2014.6842377Holybro Pixhawk 4 · PX4 v1.9.0 User Guidehttps://docs.px4.io/v1.9.0/en/flight_controller/pixhawk4.htmlGarrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F. J., & Medina-Carnicer, R. (2016). Generation of fiducial marker dictionaries using Mixed Integer Linear Programming. Pattern Recognition, 51, 481-491. doi:10.1016/j.patcog.2015.09.023Romero-Ramirez, F. J., Muñoz-Salinas, R., & Medina-Carnicer, R. (2018). Speeded up detection of squared fiducial markers. Image and Vision Computing, 76, 38-47. doi:10.1016/j.imavis.2018.05.004ArUco: Augmented reality library based on OpenCVhttps://sourceforge.net/projects/aruco/Jin, S., Zhang, J., Shen, L., & Li, T. (2016). On-board vision autonomous landing techniques for quadrotor: A survey. 2016 35th Chinese Control Conference (CCC). doi:10.1109/chicc.2016.7554984Chen, X., Phang, S. K., Shan, M., & Chen, B. M. (2016). System integration of a vision-guided UAV for autonomous landing on moving platform. 2016 12th IEEE International Conference on Control and Automation (ICCA). doi:10.1109/icca.2016.7505370Nowak, E., Gupta, K., & Najjaran, H. (2017). Development of a Plug-and-Play Infrared Landing System for Multirotor Unmanned Aerial Vehicles. 2017 14th Conference on Computer and Robot Vision (CRV). doi:10.1109/crv.2017.23Shaker, M., Smith, M. N. R., Yue, S., & Duckett, T. (2010). Vision-Based Landing of a Simulated Unmanned Aerial Vehicle with Fast Reinforcement Learning. 2010 International Conference on Emerging Security Technologies. doi:10.1109/est.2010.14Araar, O., Aouf, N., & Vitanov, I. (2016). Vision Based Autonomous Landing of Multirotor UAV on Moving Platform. Journal of Intelligent & Robotic Systems, 85(2), 369-384. doi:10.1007/s10846-016-0399-zPatruno, C., Nitti, M., Petitti, A., Stella, E., & D’Orazio, T. (2018). A Vision-Based Approach for Unmanned Aerial Vehicle Landing. Journal of Intelligent & Robotic Systems, 95(2), 645-664. doi:10.1007/s10846-018-0933-2Baca, T., Stepan, P., Spurny, V., Hert, D., Penicka, R., Saska, M., … Kumar, V. (2019). Autonomous landing on a moving vehicle with an unmanned aerial vehicle. Journal of Field Robotics, 36(5), 874-891. doi:10.1002/rob.21858De Souza, J. P. C., Marcato, A. L. M., de Aguiar, E. P., Jucá, M. A., & Teixeira, A. M. (2019). Autonomous Landing of UAV Based on Artificial Neural Network Supervised by Fuzzy Logic. Journal of Control, Automation and Electrical Systems, 30(4), 522-531. doi:10.1007/s40313-019-00465-ySITL Simulator (Software in the Loop)http://ardupilot.org/dev/docs/sitl-simulator-software-in-the-loop.htmlFabra, F., Calafate, C. T., Cano, J.-C., & Manzoni, P. (2017). On the impact of inter-UAV communications interference in the 2.4 GHz band. 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC). doi:10.1109/iwcmc.2017.7986413MAVLink Micro Air Vehicle Communication Protocolhttp://qgroundcontrol.org/mavlink/startFabra, F., Calafate, C. T., Cano, J. C., & Manzoni, P. (2018). ArduSim: Accurate and real-time multicopter simulation. Simulation Modelling Practice and Theory, 87, 170-190. doi:10.1016/j.simpat.2018.06.009Careem, M. A. A., Gomez, J., Saha, D., & Dutta, A. (2019). HiPER-V: A High Precision Radio Frequency Vehicle for Aerial Measurements. 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). doi:10.1109/sahcn.2019.882490

Detection and estimation of moving obstacles for a UAV

In recent years, research interest in Unmanned Aerial Vehicles (UAVs) has been grown rapidly because of their potential use for a wide range of applications. In this paper, we proposed a vision-based detection and position/velocity estimation of moving obstacle for a UAV. The knowledge of a moving obstacle's state, i.e., position, velocity, is essential to achieve better performance for an intelligent UAV system specially in autonomous navigation and landing tasks. The novelties are: (1) the design and implementation of a localization method using sensor fusion methodology which fuses Inertial Measurement Unit (IMU) signals and Pozyx signals; (2) The development of detection and estimation of moving obstacles method based on on-board vision system. Experimental results validate the effectiveness of the proposed approach. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Tightly Coupled GNSS and Vision Navigation for Unmanned Air Vehicle Applications

This paper explores the unique benefits that can be obtained from a tight integration of a GNSS sensor and a forward-looking vision sensor. The motivation of this research is the belief that both GNSS and vision will be integral features of future UAV avionics architectures, GNSS for basic aircraft navigation and vision for obstacle-aircraft collision avoidance. The paper will show that utilising basic single-antenna GNSS measurements and observables, along with aircraft information derived from optical flow techniques creates unique synergies. Results of the accuracy of attitude estimates will be presented, based a comprehensive Matlab® Simulink® model which re-creates an optical flow stream based on the flight of an aircraft. This paper establishes the viability of this novel integrated GNSS/Vision approach for use as the complete UAV sensor package, or as a backup sensor for an inertial navigation system

A pan-tilt camera Fuzzy vision controller on an unmanned aerial vehicle

is paper presents an implementation of two Fuzzy Logic controllers working in parallel for a pan-tilt camera platform on an UAV. This implementation uses a basic Lucas-Kanade tracker algorithm, which sends information about the error between the center of the object to track and the center of the image, to the Fuzzy controller. This information is enough for the controller, to follow the object moving a two axis servo-platform, besides the UAV vibrations and movements. The two Fuzzy controllers of each axis, work with a rules-base of 49 rules, two inputs and one output with a more significant sector defined to improve the behavior of those

Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data

In this work, we propose a robust network-in-the-loop control system for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV). To estimate the UAV’s absolute pose, we develop a deep neural network (DNN) architecture for visual-inertial odometry, which provides a robust alternative to traditional methods. We first evaluate the accuracy of the estimation by comparing the prediction of our model to traditional visual-inertial approaches on the publicly available EuRoC MAV dataset. The results indicate a clear improvement in the accuracy of the pose estimation up to 25% over the baseline. Finally, we integrate the data-driven estimator in the closed-loop flight control system of Airsim, a simulator available as a plugin for Unreal Engine, and we provide simulation results for autonomous navigation and landing

Visual servoing of an autonomous helicopter in urban areas using feature tracking

We present the design and implementation of a vision-based feature tracking system for an autonomous helicopter. Visual sensing is used for estimating the position and velocity of features in the image plane (urban features like windows) in order to generate velocity references for the flight control. These visual-based references are then combined with GPS-positioning references to navigate towards these features and then track them. We present results from experimental flight trials, performed in two UAV systems and under different conditions that show the feasibility and robustness of our approach