Auto-Landing System for Fixed-Wing Unmanned Aerial Vehicle

Unmanned aerial vehicle systems, or any kind or autonomous system, are relevant to many applications today. However, they are complex and sophisticated systems that require a deep understanding of multiple technologies. In addition, the mathematical rigor, computer modelling, and programming applications involved make t¬his a challenging field of study. This thesis explores the possibility of achieving the automated landing of a fixed-wing unmanned aerial vehicle. Auto-landing systems can resolve the challenges for the novice user and make aerial vehicle platforms accessible and dependable. A wide spectrum of applications such as agriculture, aerial photography, and security, to name a few, can utilize this technology. This thesis catalogs, describes, and analyzes the research into existing solutions, attainable technologies, and the process used to develop and validate a control algorithm that can land an airplane safely

Vision-based landing of a simulated unmanned aerial vehicle with fast reinforcement learning

Landing is one of the difficult challenges for an unmanned aerial vehicle (UAV). In this paper, we propose a vision-based landing approach for an autonomous UAV using reinforcement learning (RL). The autonomous UAV learns the landing skill from scratch by interacting with the environment. The reinforcement learning algorithm explored and extended in this study is Least-Squares Policy Iteration (LSPI) to gain a fast learning process and a smooth landing trajectory. The proposed approach has been tested with a simulated quadrocopter in an extended version of the USARSim Unified System for Automation and Robot Simulation) environment. Results showed that LSPI learned the landing skill very quickly, requiring less than 142 trials

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

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

Saliency-based cooperative landing of a multirotor aerial vehicle on an autonomous surface vehicle

This paper presents a method for vision-based landing of a multirotor unmanned aerial vehicle (UAV) on an autonomous surface vehicle (ASV) equipped with a helipad. The method includes a mechanism for helipad behavioural search when outside the UAV’s field of view, a learning saliency-based mechanism for visual tracking the helipad, and a cooperative strategy for the final vision-based landing phase. Learning how to track the helipad from above occurs during takeoff and cooperation results from having the ASV tracking the UAV for assisting its landing. A set of experimental results with both simulated and physical robots show the feasibility of the presented method.info:eu-repo/semantics/acceptedVersio

Kesan penggunaan prosedur pembelajaran kawalan kendiri (self-regulated learning) terhadap pencapaian akademik, kemahiran meta kognitif dan motivasi pelajar politeknik : kajian kes

Pembelajaran Kawalan Kendiri (PKK) merupakan satu strategi pembelajaran efektif yang membantu pelajar untuk kompeten dan mempunyai autonomi dalam diri. Namun, prosedur yang betul bagi mengaplikasikan PKK masih memerlukan penambahbaikan disebabkan oleh pelajar cepat bosan belajar subjek teori. Pelajar juga didapati mengamalkan surface learning, mudah hilang fokus dalam kelas dan tidak cekap dalam mengawal kemahiran meta kognitif. Oleh itu, tujuan kajian ini dilaksanakan adalah untuk membangunkan satu prosedur PKK khusus untuk pelajar politeknik yang mengambil subjek Prinsip Pengurusan dan diuji keberkesanannya terhadap pencapaian akademik, kemahiran meta kognitif dan motivasi pelajar. Terdapat dua (2) fasa telah digunakan dalam kajian ini. Fasa pertama (1) ialah pembangunan prosedur PKK menggunakan analisis dokumen dan model Kemp. Analisis frekuensi telah digunakan dalam fasa ini. Terdapat tiga (3) hasil dapatan kajian daripada fasa pembangunan iaitu Prosedur PKK, Aktiviti Pengajaran dan Rancangan Pengajaran Harian (RPH). Fasa kedua (2) ialah pelaksanaan prosedur PKK menggunakan reka bentuk kuasi eksperimen iaitu ujian pra-pasca bagi kumpulan-kumpulan tidak seimbang. 43 orang pelajar Politeknik Sultan Haji Ahmad Shah (POLISAS) telah dipilih sebagai kumpulan rawatan manakala 38 orang pelajar Politeknik Merlimau (PMM) sebagai kumpulan kawalan. Analisis deskriptif skor min dan analisis inferensi MANCOVA telah digunakan dalam kajian ini bagi menguji perbezaan antara kumpulan kajian. Berdasarkan hasil analisis MANCOVA yang telah dijalankan, didapati wujud perbezaan yang signifikan secara statistik antara kumpulan rawatan dan kawalan bagi pencapaian akademik [F (1, 76) = 24.786, p = .000], kemahiran meta kognitif [F (1, 76) = 14.864, p = .000] dan motivasi [F (1, 76) = 65.148, p = .000]. Kesimpulannya, prosedur PKK terbukti berkesan dan boleh dijadikan panduan kepada pensyarah dalam mengaplikasikan PKK dengan lebih efektif dan berkesan