CONTACT AND VISUAL BASED ENVIRONMENT CLASSIFICATION FOR MOBILE ROBOTS
Abstract
To increase the capabilities and expand possible applications of robotic systems for special
purposes, switching from the currently being adopted remote control systems to semi-autonomous
systems that monitor operator actions and perform part of his functions is proposed. Moving to
autonomous control systems capable of functioning in the "silent" mode, in shielded areas andbeyond the range of radio communications is proposed as a next step. Such intellectualization of
on-board control systems will allow to eliminate fundamental limitations and disadvantages
caused by the communication channel and ensures the implementation of robot group control. It is
shown that the basis for robot autonomy increasing of on-board control systems, both for movement
and tool control, is onboard environment model generation and determining the coordinates
of the control object. External environment model and current coordinates makes it possible to
automate the trajectory planning and movement, which ensures the autonomous functioning of
robotic systems. The complex problem of environment segmentation according to geometric and
ground passability is considered, taking into account the characteristics of the chassis, the geometry
of the relief and the supporting properties of the ground. The existing methods are described
and the results of experimental studies are given to solve the following main tasks: – classification
of the operation zone according to geometric passability based data from the onboard technical
vision system; – ground types recognition according to the integrated technical vision system; –
using the apparatus of neural networks to improve the reliability of ground types recognition; –
determination of ground reference characteristics by measuring the reactions of the chassis during
movement. The promising directions for further research in integrating tactile and visual information
to improve the reliability of the classification of operation zone according to the complex
criterion of geometric and support passability are formulated.
References
Formirovanie modeley virtual'noy real'nosti i informatsionno-navigatsionnykh poley dlya
obespecheniya avtonomnogo funktsionirovaniya RTK spetsial'nogo naznacheniya [Formation
of virtual reality models and information and navigation fields to ensure the autonomous functioning
of special-purpose RTCs], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering
Sciences], 2017, No. 1-2, pp. 142-146.
2. 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.
3. 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 the conditions
of rough terrain ], Upravlenie dvizheniem i tekhnicheskoe zrenie avtonomnykh
transportnykh robotov: Sb. nauchn. tr. [Traffic management and technical vision of autonomous
transport robots: Collection of scientific papers]. Moscow: IFTP, 1989, pp. 61-69.
4. 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 mobile
robots], Mekhatronika, avtomatizatsiya, upravlenie [Mechatronics, Automation,
Managemen], 2005, No. 12, pp. 21-24.
5. Noskov V.P., Rubtsov I.V., Vazaev A.V. Ob effektivnosti modelirovaniya vneshney sredy po
dannym kompleksirovannoy STZ [On the effectiveness of modeling the external environment
according to the integrated STZ data], Robototekhnika i tekhnicheskaya kibernetika [Robotics
and technical cybernetics], 2015, No. 2 (7), pp. 51-55.
6. 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 integrated
system of technical vision], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering
Sciences], 2016, No. 2 (175), pp. 127-139.
7. 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], Sb. materialov 14 Vserossiyskoy nauchnoprakticheskoy
konferentsii «Perspektivnye zadachi i sistemy upravleniya» [Collection of materials
of the 14th All-Russian Scientific and Practical Conference "Perspective tasks and Management
systems"]. Rostov-on-Don: Izd-vo YuFU, 2019, pp. 29-32.
8. Vazaev A.V., Noskov V.P., Rubtsov I.V., Tsarichenko S.G. Kompleksirovannaya STZ v sisteme
upravleniya pozharnogo robota [Integrated STZ in the fire robot control system], Izvestiya
YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2017, No. 1 (186),
pp. 121-132.
9. Belyakov V.V. i dr. Vezdekhodnye transportno-tekhnologicheskie mashiny [All-terrain
transport and technological machines]. Nizhniy Novgorod: Izd-vo «Talam», 2004, 960 p.
10. Chobitok V.A. Teoriya dvizheniya tankov i BMP [Theory of movement of tanks and infantry
fighting vehicles]. Voenizdat, 1984, 264 p.
11. Sologub P.S., Veselov V.A., Ipatov O.S. i dr. Kosmicheskie robotizirovannye kompleksy.
Leningradskaya – Sankt-Peterburgskaya nauchno-konstruktorskaya shkola [Space robotic systems.
Leningrad – St. Petersburg Scientific and Design school], under the general ed. of
V.A. Veselova. Saint Petersburg, 2016, 200 p.
12. Krizhevsky A., Sutskever I., Hinton G.E. ImageNet Classification with Deep Convolutional
Neural Networks, Advances in Neural Information Processing Systems 25, ed. by Pereira F.,
Burges C.J.C., Bottou L., Weinberger K.Q. Curran Associates, Inc., 2012, pp. 1097-1105.
13. Govorukhin A.M., Kuprin A.M., Kovalenko A.N., Gamezo M.V. Spravochnik po voennoy
topografii [Handbook of Military Topography]. 2nd ed. Moscow: Voenizdat, 1980, 344 p.
14. Bekker M.G. Vvedenie v teoriyu sistem mestnost'–mashina [Introduction to the theory of terrain-
machine systems]: trans. from the engl. by V.V. Gus'kova. Moscow: Mashinostroenie,
1973, 519 p.
15. Teoriya i konstruktsiya tanka. T. 8. Parametry vneshney sredy, ispol'zuemykh v raschetakh
tankov [Theory and design of the tank. Vol. 8. Parameters of the external environment used in
the calculations of tanks]. Moscow: Mashinostroenie, 1987, 196 p.
16. Polotno puti transportno-tekhnologicheskikh mashin: uchebnik [The roadbed of transport and
technological machines: textbook], ed. by V.V. Belyakova, A.A. Kurkina. Nizhny Novgorod:
NGTU im R.E. Alekseeva, 2014.
17. Krasnen'kov V.I., Kharitonov S.A., Shumilin A.V. Matematicheskaya model' krivolineynogo
dvizheniya transportnoy gusenichnoy mashiny po deformiruemomu osnovaniyu [Mathematical
model of the curved movement of a transport crawler on a deformable base], Izvestiya vuzov.
Mashinostroenie [Izvestiya vuzov. Mechanical engineering], 1989, No. 11, pp. 94-99.
18. Zhileykin M.M., Zakharov A.Yu., Pan'shin M.V. Proverka adekvatnosti i tochnosti
matematicheskoy modeli vzaimodeystviya elastichnogo kolesa s deformiruemym opornym
osnovaniem [Checking the adequacy and accuracy of the mathematical model of the interaction
of an elastic wheel with a deformable support base], Tr. NGTU im. R.E. Alekseeva [Proceedings
of the NSTU named after R. E. Alekseev]. – Nizhny Novgorod, 2018, No. 4, pp. 206-214.
19. Beketov A.S. Teoriya upravlyaemogo dvizheniya gusenichnykh mashin [Theory of controlled
movement of tracked vehicles]. Moscow: Izd-vo MGTU im. N.E. Baumana, 2017, 125 p.
20. Naumov V.N., Mashkov K.Yu., Kotiev G.O., Chizhov D.A., Gorelov V.A. Metod
matematicheskogo modelirovaniya pryamolineynogo dvizheniya robotizirovannykh
transportnykh sredstv po deformiruemomu gruntu [Method of mathematical modeling of rectilinear
motion of robotic vehicles on deformable ground], Inzhenernyy zhurnal: nauka i
innovatsii [Engineering Journal: Science and Innovation], 2012, No. 11 (11), pp. 34.
21. Mashkov K.Yu., Rubtsov V.I., Shtifanov N.V. Avtomaticheskaya sistema obespecheniya
opornoy prokhodimosti mobil'nogo robota [Automatic support system for mobile robot patency],
Vestnik MGTU im. N.E. Baumana. Ser. «Mashinostroenie». Spets. vypusk «Spetsial'naya
robototekhnika i mekhatronika» [Bulletin of the Bauman Moscow State Technical University.
Series "Mechanical Engineering". Special issue "Special robotics and Mechatronics"], 2012,
pp. 95-106.
22. Mashkov K.Y. Pozdnyakov T.D. Using strain gages to assess the bearing properties of underlying
surface with a robot crawler IOP, Conference Series: Materials Science and Engineering,
2020, Vol. 820.
