Top

Scientific and Technical Information Processing

Published in:

01-12-2022

Planning the Behavior of an Autonomous Flying Robot in a Space of Subtasks. Knowledge Representation Model

Authors: V. B. Melekhin, M. V. Khachumov

Published in: Scientific and Technical Information Processing | Issue 5/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This article shows that usually the automatic control system of autonomous flying robots based on unmanned aerial vehicles has limited computing resources, which makes it impossible to use known labor-intensive logical models of knowledge representation and processing to plan goal-directed behavior. Thus, there is a need to develop a model of knowledge representation and processing that makes it possible to plan goal-oriented behavior under conditions of a priori uncertainty of the problem environment with polynomial complexity. To solve this problem, a model of knowledge representation is constructed in the form of a set of typical basic, intermediate, and terminal growth elements used to automatically plan goal-seeking behavior in the space of subtasks in the form of a growing reduction network model for solving complex problems under uncertainty. Automatic goal-setting procedures are developed that allow an autonomous flying robot to secure its activities under a priori uncertainty in an unstable problem environment.

previous article System-Object Determinant Analysis: Constructing a Domain Taxonomy

next article Planning the Behavior of an Autonomous Flying Robot in a Subtask Space. Inference Procedures

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Kalyaev, A.V., Chernukhin, Yu.V., and Kalyaev, I.A., Odnorodnye upravlyayushchie struktury adaptivnykh robotov (Homogeneous Control Structures of Adaptive Robots), Moscow: Nauka, 1990.

Afanas’ev, V.N., Dinamicheskie sistemy upravleniya s nepolnoi informatsiei. Algoritmicheskoe konstruirovanie (Dynamic Control Systems with Incomplete Information: Algorithmic Design), Moscow: Editorial URSS, 2018.

Stuart, R. and Norvig, P., Artificial Intelligence: A Modern Approach, Pearson, 2020, 4th ed.MATH

Shumskij, S.A., Mashinnyj intellekt. ocherki po teorii mashinnogo obucheniya i iskusstvennogo intellekta (Machine Intelligence. Essays on the Theory of Machine Learning and Artificial Intelligence), Moscow: RIOR, 2020.

Ford, M., Architects of Intelligence: The Truth about AI from the People Building It, Birmingham: Packt Publishing, 2018.

Jones, T.M., AI Application Programming, Charles River Media, 2005.

Kober, J. and Peters, J., Learning Motor Skills: From Algorithms to Robot Experiments, Springer Tracts in Advanced Robotics, vol. 97, Cham: Springer, 2014. https://doi.org/10.1007/978-3-319-03194-1

Melekhin, V.B., Model of representation and acquisition of new knowledge by an autonomous intelligent robot based on the logic of conditionally dependent predicates, J. Comput. Syst. Sci. Int., 2019, vol. 58, no. 5, pp. 747–765. https://doi.org/10.1134/S1064230719050101MathSciNetCrossRefMATH

Kelly, A., Mobile Robotics: Mathematics, Models, and Methods, New York: Cambridge Univ. Press, 2013.CrossRef

10.

Kilani, Y., Bsoul, M., Alsarhan, A., and Al-Khasawneh, A.A., Survey of the satisfiability-problems solving algorithms, Int. J. Adv. Intell. Paradigms, 2013, vol. 5, no. 3, pp. 233–256. https://doi.org/10.1504/IJAIP.2013.056447CrossRef

11.

Varlamov, O.O. and Aladdin, D.V., On the use of mivar networks for intelligent planning of the behavior of robots in the state space, Izv. Kabardino-Balkarskogo Nauchn. Tsentra Ross. Akad. Nauk, 2018, no. 6-2, pp. 75–82.

12.

Karpov, V.E., Karpova, I.P., and Kulinich, A.A., Sotsial’nye soobshchestva robotov (Social Communities of Robots), Moscow: Lenand, 2019.

13.

Simon, D., Algorithms of Evolutionary Optimization: Biologically Inspired and Population-Based Approaches to Computer Intelligence, Hoboken, N.J.: John Wiley & Sons, 2013.MATH

14.

Callan, R., Essence of Neural Networks, Prentice Hall, 1998.

15.

Melekhin, V.B. and Khachumov, M.V., Partitioning complex tasks into subtasks in the process of planning goal-seeking behavior of an integral unmanned aerial vehicle in the state space, Morskie Intell. Tekhnol., 2020, no. 3-1, pp. 237–244. https://doi.org/10.37220/MIT.2020.49.3.032

16.

Melekhin, V.B. and Khachumov, M.V., Planning the behavior of an integrated unmanned aerial vehicle based on the division of complex tasks into subtasks in the state space, Morskie Intell. Tekhnol., 2020, no. 3-1, pp. 245–252. https://doi.org/10.37220/MIT.2020.49.3.033

17.

Melekhin, V.B. and Khachumov, M.V., Planning the collective activities of autonomous mobile intelligent agents under uncertainty, Sci. Tech. Inf. Process., 2021, vol. 48, no. 6, pp. 493–501. https://doi.org/10.3103/S0147688221060101CrossRef

18.

Melekhin, V.B. and Khachumov, M.V., Fuzzy semantic networks as a knowledge representation model of autonomous intelligent systems, Sci. Tech. Inf. Process., 2021, vol. 48, no. 5, pp. 333–341. https://doi.org/10.3103/S0147688221050051CrossRef

19.

Melekhin, V.B. and Khachumov, M.V., Tools for self-organizing a group of autonomous unmanned aerial vehicles under uncertainty, Aviakosm. Priborostr., 2019, no. 2, pp. 44–51.

20.

Bersnhtein, L.S. and Melekhin, V.B., Planning polyphasic behavior of self-organizing intelligent systems, J. Comput. Syst. Sci. Int., 2000, vol. 39, no. 5, pp. 808–811.

Title: Planning the Behavior of an Autonomous Flying Robot in a Space of Subtasks. Knowledge Representation Model
Authors: V. B. Melekhin
M. V. Khachumov
Publication date: 01-12-2022
Publisher: Pleiades Publishing
Published in: Scientific and Technical Information Processing / Issue 5/2022
Print ISSN: 0147-6882
Electronic ISSN: 1934-8118
DOI: https://doi.org/10.3103/S0147688222050070

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 5/2022

The Importance of Forecasting in Industrial Enterprise Management Using Machine Learning

System-Object Determinant Analysis: Constructing a Domain Taxonomy

Type-2 Fuzzy Sets and Their Application in Decision-Making: Implementations

Mathematical Aspects of the Concept of Influence in Cognitive Simulations

Lattice Theory for Machine Learning

Application of Random Forest in Choosing a Method of Recovering the Age of Social Network Users

Premium Partner