[en] The usages and functionalities of Unmanned Aerial Vehicles (UAV) have grown rapidly during the last years. They are being engaged in many types of missions, ranging from military to agriculture passing by entertainment and rescue or even delivery. Nonetheless, for being able to perform such tasks, UAVs have to navigate safely in an often dynamic and partly unknown environment. This brings many challenges to overcome, some of which can lead to damages or degradations of different body parts. Thus, new tools and methods are required to allow the successful analysis and identification of the different threats that UAVs have to manage during their missions or flights. Various approaches, addressing this domain, have been proposed. However, most of them typically identify the changes in the UAVs behavior rather than the issue. This work presents an approach, which focuses not only on identifying degradations of UAVs during flights, but estimate the source of the failure as well.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Automation & Robotics Research Group
Disciplines :
Computer science
Author, co-author :
Manukyan, Anush ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Olivares Mendez, Miguel Angel ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Geist, Matthieu; Université de Lorraine > Laboratoire Interdisciplinaire des Environnements Continentaux
Voos, Holger ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
External co-authors :
yes
Language :
English
Title :
Real time degradation identification of UAV using machine learning techniques
Publication date :
13 June 2017
Event name :
International Conference on Unmanned Aircraft Systems ICUAS
Event place :
Miami, FL, United States
Event date :
From 13-06-2017 to 16-06-2017
Audience :
International
Main work title :
International Conference on Unmanned Aircraft Systems ICUAS. Miami, USA, 2017
Kataria, Aman, and M. D. Singh. "A Review of Data Classification Using K-Nearest Neighbour Algorithm. " International Journal of Emerging Technology and Advanced Engineering 3. 6 (2013): 354-360.
Lin Raz, Eliyahu Khalastchi, and Gal Kaminka. "Detecting anomalies in unmanned vehicles using the mahalanobis distance. " Robotics and Automation (ICRA), 2010 IEEE International Conference on.
Stavrou, Demetris, et al. "Fault detection for service mobile robots using model-based method. " Autonomous Robots 40. 2 (2016): 383-394.
Plagemann, Christian, Cyrill Stachniss, and Wolfram Burgard. "Efficient failure detection for mobile robots using mixed-abstraction particle filters. " European Robotics Symposium 2006. Springer Berlin Heidelberg, 2006.
Larrauri, Juan I., Gorka Sorrosal, and Mikel Gonzlez. "Automatic system for overhead power line inspection using an Unmanned Aerial VehicleRELIFO project. " Unmanned Aircraft Systems (ICUAS), 2013 International Conference on. IEEE, 2013.
Brotherton, Tom, and Ryan Mackey. "Anomaly detector fusion processing for advanced military aircraft. " Aerospace Conference, 2001, IEEE Proceedings. Vol. 6. IEEE, 2001.
Afridi, M. Jamal, Ahsan Javed Awan, and Javaid Iqbal. "AWGDetector: A machine learning tool for the accurate detection of Anomalies due to Wind Gusts (AWG) in the adaptive Altitude control unit of an Aerosonde unmanned Aerial Vehicle. " Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on. IEEE, 2010.
Khalastchi, Eliahu, et al. "Online anomaly detection in unmanned vehicles. " The 10th International Conference on Autonomous Agents and Multiagent Systems-Volume 1. International Foundation for Autonomous Agents and Multiagent Systems, 2011.
Laurikkala, Jorma, et al. "Informal identification of outliers in medical data. " Fifth International Workshop on Intelligent Data Analysis in Medicine and Pharmacology. Vol. 1. 2000.
Pokrajac, Dragoljub, Aleksandar Lazarevic, and Longin Jan Latecki. "Incremental local outlier detection for data streams. " Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on. IEEE, 2007.
Laxhammar, Rikard, and Gran Falkman. "Online learning and sequential anomaly detection in trajectories. " IEEE transactions on pattern analysis and machine intelligence 36. 6 (2014): 1158-1173.
Ahmad, Subutai, and Scott Purdy. "Real-Time Anomaly Detection for Streaming Analytics. " arXiv preprint arXiv: 1607. 02480 (2016).
Lee, Yen-Hsien, et al. "Nearest-neighbor-based approach to time-series classification. " Decision Support Systems 53. 1 (2012): 207-217.
Manukyan, Anush, et al. "UAV degradation identification for pilot notification using machine learning techniques. " Emerging Technologies and Factory Automation (ETFA), 2016 IEEE 21st International Conference on. IEEE, 2016.
Chaovalitwongse, Wanpracha Art, Ya-Ju Fan, and Rajesh C. Sachdeo. "On the time series k-nearest neighbor classification of abnormal brain activity. " IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 37. 6 (2007): 1005-1016.
Suguna, N., and K. Thanushkodi. "An improved K-nearest neighbor classification using Genetic Algorithm. " International Journal of Computer Science Issues 7. 2 (2010): 18-21.
Park, Youngser, Carey E. Priebe, and Abdou Youssef. "Anomaly detection in time series of graphs using fusion of graph invariants. " IEEE journal of selected topics in signal processing 7. 1 (2013): 67-75.
Abid, Anam, Muhammad Tahir Khan, and C. W. de Silva. "Fault detection in mobile robots using sensor fusion. "Computer Science & Education (ICCSE), 2015 10th International Conference on. IEEE, 2015.
Duan, Zhuohua, Hui Ma, and Liang Yang. "Fault detection for internal sensors of mobile robots based on support vector data description. " Control and Decision Conference (CCDC), 2015 27th Chinese. IEEE, 2015.
Arrichiello, Filippo, Alessandro Marino, and Francesco Pierri. "A decentralized fault detection and isolation strategy for networked robots. " Advanced Robotics (ICAR), 2013 16th International Conference on. IEEE, 2013.
Wu, Xindong, et al. "Top 10 algorithms in data mining. " Knowledge and Information Systems 14. 1 (2008): 1-37.
Video of the UAV Good and Worst flight https://www. dropbox. com/s/tfc6jjvrzqy5l2d/UAV- Good-Worst-Flight. mp4?dl=0
Yang, Peng, et al. "A tighter lower bound estimate for dynamic time warping. " Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013. APA
Rakthanmanon, Thanawin, et al. "Searching and mining trillions of time series subsequences under dynamic time warping. " Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012.
Correia, Vitor Monteiro. "The Aircraft Maintenance Program and its importance on Continuing Airworthiness Management. "