Relative Navigation in UAV Applications

Year 2020, , 52 - 65, 30.12.2020

Tuncay Yunus Erkeç , Cengiz Hacızade

https://doi.org/10.23890/IJAST.vm01is02.0202

Abstract

This paper is committed to the relative navigation of Unmanned Aerial Vehicles (UAVs) flying in formation flight. The concept and methods of swarm UAVs technology and architecture have been explained. The relative state estimation models of unmanned aerial vehicles which are based on separate systems as Inertial Navigation Systems (INS)&Global Navigation Satellite System (GNSS), Laser&INS and Vision based techniques have been compared via various approaches. The sensors are used individually or integrated each other via sensor integration for solving relative navigation problems. The UAV relative navigation models are varied as stated in operation area, type of platform and environment. The aim of this article is to understand the correlation between relative navigation systems and potency of state estimation algorithms as well during formation flight of UAV.

Keywords

Relative Navigation, GPS, Kalman Filters, Unmanned Aerial Vehicles, Localization

References

• [1] Costa F.G., Ueyama J. Braun T. Pessin G. Osório F.S. Vargas P.A. (2012) The use of unmanned aerial vehicles and wireless sensor network in agricultural applications, in Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, 5045-5048.
• [2] Dongwoo, L., Seungkeun, K., Jinyoung, S. (2018). Formation flight of unmanned aerial vehicles using track guidance. Aerospace Science and Technology. 76. 10.1016/j.ast.2018.01.026.
• [3] Hausamann D., Zirnig W., Schreier G., Strobl P. (2005) Monitoring of gas pipelines–a civil UAV application, Aircraft Eng. and Aerospace Tech. 77 (5), 352-360.
• [4] Katrasnik J., Pernus F., Likar B. (2010) A survey of mobile robots for distribution power line inspection, IEEE Transactions on Power Delivery, 25(1), 485-493.
• [5] Palunko I., Cruz P., Fierro R. (2012) Agile load transportation: Safe and efficient load manipulation with aerial robots, Robotics & Automation Magazine, IEEE, 19(3), 69-79.
• [6] Hajase M., Noura H, Drak A., (2015) Formation Flight of Small Scale Unmanned Aerial Vehicles: A Review, In book: Control Theory: Perspectives, Applications and Developments, Chapter: Formation Flight of Small Scale Unmanned Aerial Vehicles: A Review, Nova Science Publishers.
• [7] Ma, O., Abad, A.F., Boge, T. (2012). Use of industrial robots for hardware-in-the-loop simulation of satellite rendezvous and docking, Acta Astronautica, 81, 335-347.
• [8] Rafail F.M., Budylov S.G. (2010). Short rendezvous missions for advanced Russian human spacecraft, Acta Astronautica, 67, 900-909.
• [9] Beard RW., McLain T.W., Nelson D.B., Kingston D., Johanson D. (2006) Decentralized cooperative aerial surveillance using fixed-wing miniature UAVs. Proceedings of the IEEE, 94(7), 1306-1324.
• [10] Warsi F., Hazry D., Ahmed SF. Joyo M.K., Tanveer M.H., Kamarudin H., Razlan Z.M. (2014) Yaw, Pitch and Roll controller design for fixed-wing UAV under uncertainty perturbed condition, in 2014 IEEE 10th International Colloquium on Signal Processing & its Applications (CSPA), 151-156.
• [11] Lee H., Kim S., Lim H. Kim H.J., Lee D. (2013) Control of an octa-copter from modeling to experiments, in Robotics (ISR), 2013 44th International Symposium on, 1-5.
• [12] Van de Loo J. (2007) Formation flight of two autonomous blimps, PhD diss., Technische Universiteit Eindhoven.
• [13] Anderson. B, Fidan B. Yu C., Walle D. (2008) UAV formation control: theory and application, in Recent advances in learning and control, 15-33.
• [14] Yun B., Chen B.M., Lum K.Y., Lee T.H. (2008) A leader-follower formation flight control scheme for UAV helicopters, in IEEE International Conference on Automation and Logistics, 2008. ICAL 2008. 39-44.
• [15] Zhang X.T. (2006) An output feedback nonlinear decentralized controller design for multiple unmanned vehicle coordination, in American Control Conference, 6-9.
• [16] Moafipoor S., Bock L., Fayman J. A., Honcik D. (2014). Fundamentals of autonomous relative navigation and its application to aerial refueling, Geodetics Inc. San Diego, CA 92117, USA.
• [17] Gill E. (2019). Control Approaches for Formations of Small Satellites, NATO, Delft, Netherlands.
• [18] Alonso R., Crassidis J.L., Junkins J.L. (2010). Vision-Based Relative Navigation for Formation Flying of Spacecraft, AIAA-2000-4439.
• [19] Erkec T.Y., Hajiyev, C. (2019). Traditional Methods on Relative Navigation of Small Satellites”, 2019 9th. International Rccent Advances in Space Technologies Conference (RAST), Istanbul, 869-874.
• [20] Lestarquit L. et.al. (2006). Autonomous Formation Flying RF Sensor Development for Prisma Mission. ION GNSS.
• [21] Jorgensen J.L., Benn M. (2010). VBS-The Optical Rendezvous and Docking Sensor for Prisma, Nordicspace, 16-19.
• [22] Gill E., D’Amico S (2007). Autonomous Formation Flying for the PRISMA mission, Journal of Spacecraft and Rockets 44, 3, 671-681.
• [23] Wang S., Zhan X., Zhai Y., Chi C., Shen J. (2020) Highly reliable relative navigation for multi-UAV formation flight in urban environments, Chinese Journal of Aeronautics, 1-14.
• [24] Wang J., Garratt M., Lambert A., Wang J., Han S., Sinclair D. (2008). Integration of GPS/INS/vision sensors to navigate unmanned aerial vehicles. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 37.
• [25] Ristic B., Arulampalam S., Gordon N. (2004). Beyond the Kalman Filter Particle filters for Tracking Applications, Artech House Radar Library.
• [26] Sazdovski V., Kitanov A., Petrovic I. (2015) Implicit observation model for vision aided inertial navigation of aerial vehicles using single camera vector observations, Aerospace Science and Technology 40, 33–46.
• [27] Winkler S., Schulz H.W., Buschmann M., Kordes T., Vasmann P. (2004) Improving Low-Cost GPS/MEMS-Based INS Integration for Autonomous UAV Navigation by Visual Aiding, ION GPS 2004. The Institute of Navigation, Long Beach, CA USA, pp. 1069-1075.
• [28] Tao C.V., Chapman M.A., Chaplin B.A. (2001) Automated processing of mobile mapping image sequences. ISPRS Journal of Photogrammetry & Remote Sensing, 55(5-6): 330-346.
• [29] Driessen S.P.H., Janssen N.H.J., Wang L., Palmer J.L, Nijmeijer H. (2018) Experimentally Validated Extended Kalman Filter for UAV State Estimation Using Low-Cost Sensors, IFAC PapersOnLine 51-15, 43–48.
• [30] Julian Scharnagl, Lakshminarasimhan Srinivasan, Karthik Ravandoor , Klaus Schilling. (2015), Autonomous Collision Avoidance for Rendezvaus and Docking in Space Using Photonic Mixer Devices, IFAC-PapersOnLine 48-9, 239–244.
• [31] Jun M., Motokuni I., Shirai Y. (2002) Toward VisionBased Intelligent Navigator: Its Concept and Prototype. IEEE Transactions on Intelligent Transportation Systems, 3(2), 136- 146.
• [32] Kim J., Sukkarieh S. (2004) SLAM aided GPS/INS Navigation in GPS Denied and Unknown Environments, The 2004 International Symposium on GNSS/GPS, Sydney, Australia.
• [33] Chatterji G.B., Menon P.K., Sridhar B. (1997) GPS/Machine Vision Navigation System for Aircraft. IEEE Transactions on Aerospace and Electronic System, 33(3), 1012- 1024.
• [34] Ogris G., Gal C.L., Wack, R., Paletta L. (2004) Image Based Positioning in Urban Environments Using Kalman Filtering of PCA Sensors, The 4th International Symposium on Mobile Mapping Technology (MMT2004), Kunming, China.
• [35] Smith R., Cheeseman P. (1987) On the Representation of Spatial Uncertainty. International Journal of Robotics Research, 5(4): 56-68.
• [36] Dissanayake M.W.M.G., Newman P., Durrant-Whyte H., Clark S., Csorba, M. (2001) A solution to the simultaneous localization and map building problem. IEEE Transactions on Robotics and Automation, 17(3), 229-241.
• [37] Teslic L., Skrjanc I., Klancar G. (2010) Using a LRF sensor in the Kalman filtering based localization of a mobile robot. ISA Transactions 49, 145–53.
• [38] Vázquez-Martín R., Núñez P. ,Bandera A., Sandoval F. (2009). Curvature-based environment description for robot navigation using laser range sensors. Sensors 8:5, 894–918.
• [39] Chowdhary G., Johnson E.N., Magree D., Wu A., Shein A. (2013). GPS-denied indoor and outdoor monocular vision aided navigation and control of unmanned air-craft, J. Field Robot. 30(3), 415–438.
• [40] Engel J., Sturm J., Cremers D. (2012). Camera-based navigation of a low-cost quadro-copter, in: IEEE/RJS International Conference on Intelligent Robot Systems (IROS), pp.2815–2821.
• [41] Engel J., Sturm J., Cremers D. (2012).Accurate figure flying with a quadrocopter using onboard visual and inertial sensing, in: IEEE/RJS International Conference on Intelligent Robot Systems (IROS).
• [42] Weiss S., Achtelik M., Lynen S., Chli M., Siegwart R. (2012). Real-time onboard visual-inertial state estimation and self-calibration of MAVS in unknown environ-ments, in: IEEE International Conference on Robotics and Automation (ICRA), pp.957–964.
• [43] Ramasamy S., Sabatini R., Gardi A., Liu J. (2016). LIDAR obstacle warning and avoidance system for unmanned aerial vehicle sense and avoid, Aerospace Science and Technology 55, 344–358.
• [44] Sabatini R., Richardson M.A., Gardi A., Ramasamy S. (2015). Airborne laser sensors and integrated systems, Progress in Aerospace Sciences 79, 15–63.
• [45] Auger F., Hilairet M., Guerrero J.M., Monmasson E., Orlowska-Kowalska T., Katsura S. (2013). Industrial applications of the Kalman filter: a review, IEEE Trans. Ind. Electron. 60(12), 5458–5471.
• [46] Strelow S., Sanjiv D.(2003). Online motion estimation from image and inertial measurements. Workshop on Integration of Vision and Inertial Sensors INERVIS.
• [47] Zhang L., Yang H., Lu H., Zhang S., Cai H., Qian S. (2014). Cubature Kalman Filtering for relative Spacecraft attitude and position estimation. Acta Astronautica 105, 254-264.
• [48] Arasaratnam I., Haykin S. (2009). Cubature Kalman filters, IEEE Trans. Autom. Control. 54, 1254-1269.
• [49] Personen H., Piche R. (2010). Cubarature- based Kalman filters for positioning, In: Proceedings of the 7th Workshop on Positioning Navigation and Communication, 45-49.
• [50] Fernandez-Prades C., Vila-Valls J. (2010). Bayesian nonlinear filtering using quadrature and cubature rules applied to sensor data fusion for positioning, In: Proceedings of the IEEE International Conference on Communications, Cape Town, South Africa, 1-5.
• [51] Tang X., Liu Z., Zhang J. (2012). Square-root quaternion cubature Kalman filtering for spacecraft attitude estimation, Acta Astronautica 76, 34-94.
• [52] Melczer M., Bauer P., Bokor J., (2017). 4D Trajectory Design for Vision Only Sense and Avoid Flight Test, IFAC Papers On Line 50-1, 15203–15208.
• [53] Song J., Xu G. (2017). An Orbit Determination Method from Relative Position Increment Measurement, Aerospace Engineering SAGE, doi.org/10.1177/0954410017708803.
• [54] Felicetti L., Emami M.R. (2018). Image-based attitude maneuvers for space debris tracking, Aerospace Science and Technology 76, 58–71.
• [55] Xia Q., Rao M., Ying Y., Schen X. (1998). Adaptive Fading Kalman Filter with an Aplication, Automatica, 34, pp 1333-1338.
• [56] Levent O., Fazil A.A, (1998). Coments on “Adaptive Fading kalman Filters with an Aplication” Automatica, vol 34, pp 1663-1664.
• [57] Chen J., Wang X., Shaot X. and Duan D. (2010). An Integrated Relative Navigation System Using GPS/NISNAV for, Ultra-close Spacecraft Formation Flying, 3rd International Symposium on Systems and Control in Aeronautics and Astronautics.
• [58] Kwok N.M., Dissanayake G. (2004) An efficient multiple hypothesis filter for bearing only SLAM. In: Proceedings of IEEE / RSJ International Conference on Intelligent Robots and Systems (IROS04), Sendai, Japan, Sep / Oct 2004,pp.736–41.
• [59] Kwok N.M., Dissanayake G. (2003) Bearing-only SLAM in indoor environments. Australasian Conference on Robotics and Automation.
• [60] Kwok N.M., Dissanayake G., Ha QP. (2005) Bearing only SLAM using a SPRT based Gaussian sum filter. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA05), Barcelona, Spain, 1121–1126.
• [61] Guerra E., Munguia R., Bolea Y., Grau A. (2013) New validation algorithm for data association in SLAM, ISA Transactions 52, 662–671.
• [62] Simon D., (2006). Optimal State Estimation: Kalman, H∞ and Nonlinear Approaches, Wiley, New York.
• [63] Li W., Sun S., Jia Y., Du J. (2016). Robust unscented Kalman filter with adaptation of process and measurement noise covariances, Digital Signal Processing 48, 93–103.
• [64] Shin H., White B. (2019). Unmanned Aerial Vehicle Formations, NATO, Swindon,UK, 1-28.
• [65] Binmore K. (2007) Game Theory: A Very Short Introduction, OUP Oxford.
• [66] Valavanis K.P., Vachtsevanos G.J. (2011) Handbook of Unmanned Aerial Vehicles, Springer.
• [67] Shima T., Rasmussen S. (2009) UAV Cooperative Decision and Control: Challenges and Practical Approaches, Society for Industrial Mathematics.
• [68] Butenko S.,Murphey R., Pardalos P.M. (2010) Cooperative Control: Models, Applications and Algorithms, Springer.
• [69] Adamski W., Herman P. (2012) Comparison of two-and four-engine propulsion structures of airship, in Robot Motion and Control, 341-350.
• [70] Bestaoui Y., Hima S. (2007) Modelling and trajectory generation of lighter-than-air aerial robots-invited paper, in Robot Motion and Control, 3-28.