AUTOMATED LANDING OF AN UNMANNED HELICOPTER TO AN UNEQUPPED SITE
Abstract
Unmanned helicopters perform many tasks in difficult operating conditions and are subject to various destabilizing factors that significantly affect flight safety. The main problems encoun-tered in the operation of unmanned helicopters are considered. It is shown that the insufficient level of flight safety is caused, in particular, by the high frequency of crashes during forced land-ings. The necessity of creating onboard means of automatic landing of an unmanned helicopter is proved. Taking into account the requirements of the Federal aviation regulations for landing plac-es, the parameters-restrictions that allow formalizing the choice of terrain areas suitable for land-ing according to the onboard technical vision system are formulated. On the basis of comparative analysis, it is shown that at present, when forming the initial video data for solving this problem, it is advisable to use a complex system of technical vision based on mutually adjusted and having a common viewing area of a 3D laser sensor, color video camera and thermal imager. The proposedrecognition algorithms of the pick-up location in the video data on-Board complex system of tech-nical vision with the use of geometric criteria and the reference permeability. It is proposed to perform the recognition of landing places based on the criterion of geometric cross-country capa-bility in two stages: at the first stage, a map of terrain heights is formed based on 3D laser sensor data, and at the second stage, areas suitable for helicopter landing are selected. Recognition of suitable and unsuitable areas is performed by comparing the elevation differences of this terrain with the reference elevation differences defined for this unmanned helicopter. It is proposed to perform the recognition of suitable landing sites based on the criterion of reference passability by calculating the Euclidean distance between the obtained data and pre-known standards corre-sponding to different types of soil in the six-dimensional feature space (height variance, reflected signal intensity, three colors, and temperature). The final selection of suitable places for planting is proposed to be made from sites that meet both criteria. The results of the work of the corre-sponding software and hardware in real conditions are presented, confirming the correctness and effectiveness of the proposed algorithms.
References
2. Yan X., Wu X., Kakadiaris I.A., Shah S.K. To Track or To Detect? An Ensemble Framework for Optimal Selection. In: Fitzgibbon A., Lazebnik S., Perona P., Sato Y., Schmid C. (eds) Computer Vision – ECCV 2012. LNCS. Vol. 7576. Springer, Berlin, Heidelberg, 2012.
3. Türmer S., Leitloff J., Reinartz, P., et al. Evaluation of selected features for car detection in aerial images, ISPRS Hannover Workshop, 2011, pp. 14-17.
4. Obermeyer K. Path Planning for a UAV Performing Reconnaissance of Static Ground Targets in Terrain. In: AIAA Guidance, Navigation, and Control Conference, Guidance, Navigation, and Control and Co-located Conferences, 11 p. Chicago, USA, 2009.
5. Kim N., Chervonenkis M. Situational control unmanned aerial vehicles for traffic monitoring. Modern Applied Science 9 (5), Special Issue, Canadian Center of Science and Education. ISSN (printed): 1913-1844. ISSN (electronic): 1913-1852, 2015.
6. Lin, F., Lum, KY., Chen, B.M. et al. Development of a vision-based ground target detection and tracking system for a small unmanned helicopter. Science in Chine Series F: Information Sciences, Springer, 2009. 52:2201.
7. Kogan I.M. Prikladnaya teoriya informatsii [Applied information theory]. Moscow: Radio i svyaz'. 1981, 216 p.
8. Gorelik A.L., Skripkie V.A. Metody raspoznavaniya [Methods of recognition]. Moscow: Nauka, 2004, 207 p.
9. David A. Forssyth, Jean Ponce. Computer Vision: a Modern Approach, Prentice Hall, Ptr., Coperight, 2003 by Pearson Education, Inc.
10. Kim N., Bodunkov N. «Computer Vision in Control Systems - 3: Aerial and Satellite Image Processing». Vol. 3, Editors M. Favorskaya, Lakhmi C. Jain, Springer 2018, 343 p.
11. Kim N.V., Hyun Y.M. and Yang H.K. Performance analysis of aircraft automatic landing sys-tem based on surface image processing, Proceedings WCSE/UKC 2002, Seoul, 2002.
12. Kim N.V., Kuznetsov A.G. Avtomaticheskaya posadka malogabaritnogo letatel'nogo apparata v osobykh situatsiyakh [Automatic landing of a small-sized aircraft in special situations], Tr. mezhdunarodnoy konferentsii [Proceedings of the international conference]. Saint Petersburg, 2010.
13. Zagoruyko S.N., Kaz'min V.N., Noskov V.P. Navigatsiya BPLA i 3D-rekonstruktsiya vneshney sredy po dannym bortovoy STZ [UAV Navigation and 3D reconstruction of the external envi-ronment according to the onboard STZ], Mekhatronika, avtomatizatsiya, upravlenie [Mecha-tronics, automation, control], 2014, No. 8, pp. 62-68.
14. Buyvolov G.A., Noskov V.P., Rurenko A.A., Raspopin A.N. Apparatno-algoritmicheskie sredstva formirovaniya modeli problemnoy sredy v usloviyakh peresechennoy mestnosti [Hardware-algorithmic means of forming a model of the problem environment in a rough ter-rain], Sb. nauchnykh trudov «Upravlenie dvizheniem i tekhnicheskoe zrenie avtonomnykh transportnykh robotov» [Collection of scientific papers “Traffic control and technical vision of Autonomous transport robots”]. Moscow: IFTP, 1989, pp. 61-69.
15. Kalyaev A.V., Noskov V.P., Chernukhin Yu.V., Kalyaev I.A. Odnorodnye upravlyayushchie struktury adaptivnykh robotov [Homogeneous control structures of adaptive robots]. Moscow: Nauka, 1990, 147 p.
16. Noskov V.P., Rubtsov I.V. Opyt resheniya zadachi avtonomnogo upravleniya dvizheniem mobil'nykh robotov [Experience in solving the problem of Autonomous motion control of mo-bile robots], Mekhatronika, avtomatizatsiya, upravlenie [Mechatronics, automation, control], 2005, No. 12, pp. 21-24.5.
17. Noskov A.V., Rubtsov I.V., Romanov A.Yu. Formirovanie ob"edinennoy modeli vneshney sredy na osnove informatsii videokamery i dal'nomera [Formation of a unified model of the external environment based on information from a video camera and a rangefinder] Mekhatronika, avtomatizatsiya, upravlenie [Mechatronics, automation, control], 2007, No. 8, pp. 2-5.
18. Vazaev A.V., Noskov V.P., Rubtsov I.V., TSarichenko S.G. Raspoznavanie ob"ektov i tipov opornoy poverkhnosti po dannym kompleksirovannoy sistemy tekhnicheskogo zreniya [Recognition of objects and types of the reference surface according to the data of the integrat-ed system of technical vision], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engi-neering Sciences], 2016, No. 2 (175), pp. 127-139.
19. Vazaev A.V., Noskov V.P., Rubtsov I.V., Tsarichenko S.G. Kompleksirovannaya STZ v sisteme upravleniya pozharnogo robota [Complexional STZ in the fire control system of the robot], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2017, No. 1 (186), pp. 121-132.
20. Vazaev A.V., Noskov V.P., Rubtsov I.V. Neyrosetevoy modul' vybora etalonov dlya raspoznavaniya tipov opornoy poverkhnosti [Neural network module for selecting standards for recognizing types of reference surfaces], Perspektivnye sistemy i zadachi upravleniya: Ma-ter.XIV Vserossiyskoy nauchno-prakticheskoy konferentsii i X molodezhnoy shkoly-seminara «Upravlenie i obrabotka informatsii v tekhnicheskikh sistemakh» [Perspective systems and management tasks: Materials of the XIV all-Russian scientific and practical conference and the X youth school-seminar "Management and processing of information in technical systems"]. Rostov-on-Don; Taganrog: Izd-vo YuFU, 2019, pp. 29-33.