Authors L. A. Martynova
Month, Year 08, 2018 @en
Index UDC 007.52
DOI 10.23683/2311-3103-2018-8-69-83
Abstract The purpose of the study is to increase the efficiency of the unattended underwater vehicle (AUV)functioning by resolving the conflict in its multi-agent control system, which is related to power consumption by the AUV subsystems. In the transition from exclusive use of the battery to additional use of the battery, arose the need to resolve the contradiction between the provision of energy resources by diverse sources and its consumption. The complexity of solving the problem consisted in the unpredictability of the use of various speed regimes that affect the AUV’s energy consumption. The proposed method for resolving the conflict is based on the decomposition of energy resource consumers and forecasting the possibility of accomplishing the task assigned to the AUV related to overcoming a given distance within a given time. For this purpose, we developed a method based on the following algorithms of: forecasting the sufficiency of energy resources to overcome a given distance; determining the permissible current consumption of energy resources and the corresponding speed regime; estimating the time needed to overcome the remaining distance. The listed algorithms are characterized by a set of parameters that have been optimized depending on the prevailing conditions during the execution of the task by the device. As a criterion of optimality, the probability of overcoming a given distance within a given time is used. When optimizing the parameters the following items have been taken into account: current battery level; current level of the electrochemical generator’s reserve; time during which the device has already overcome part of the specified distance. According to these data, the following have been determined successively: remaining distance; time taken to overcome the remaining distance; reserve of energy resource expended on overcoming the remaining distance. When driving in high-speed mode, we determined the following: remaining time of consumption from the battery; energy resource consumed during this time; battery energy reserve remaining after the high-speed mode. When the vehicle is moving in its normal mode, the following parameters are calculated: consumption of energy resources from the electrochemical generator taking into account the simultaneous charge of the battery; battery charge time; time to overcome the remaining distance; speed at which you need to move to overcome the remaining distance; estimation of sufficiency of an energy resource on overcoming of the remained distance; specific power consumption corresponding to the required speed; moment of transition to the economy run mode. Based on the results of the proposed algorithm, the current allowable energy resource consumption is determined, which corresponds to: current source (battery or electrochemical generator); current speed mode (full-time or high-speed); predicted probability of overcoming a given distance. The proposed method is tested using a specially developed mathematical model for the functioning of the apparatus and its energy supply system with two heterogeneous energy sources. The software implementation of the mathematical model allows to carry out numerical experiments using the proposed method under various hydrological conditions, the results of which show the undeniable advantage of the proposed method, which significantly increases the probability of the apparatus performing the task assigned to it.

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Keywords AUV; control system; energy consumption system; multi-agent system; conflict; method; algorithm; imitation model.
References 1. Mashoshin A.I.. Skobelev P.O. Primenenie mul’tiagentnykh tekhnologiy k upravleniyu gruppoy avtonomnykh neobitayemykh podvodnykh apparatov [Application of multi-agent technologies to the management of a group of autonomous uninhabited submarines], Izvestiya YuFU. Tekhnicheskiye nauki [Izvestiya SFedU. engineering sciences], 2016, No. 1. pp. 45-59.
2. Rzhevskiy G.A.. Skobelev P.O. Kak upravlyat’ slozhnymi sistemami? Multiagentnye tekhnologii dlya sozdaniya intellektual’nykh sistem upravleniya predpriyatiyami [How to manage complex systems? Multi-agent technologies for the creation of intelligent enterprise management systems] Samara: Ofort [Etching], 2015, 290 p.
3. Gorodetskiy V.I.. Grushinskiy M.S.. Khabalov A.V. Mnogoagentnye sistemy (obzor). [Multiagent systems (review)], Novosti iskusstvennogo intellekta [News of Artificial Intelligence], 1998, No.2, p.64-116.
4. Innocenti B. A multi-agent architecture with distributed coordination for an autonomous robot. Ph.D. dissertation – Universitat de Girona, 2009, рр.147.
5. Martynova L.A., Grinenkov A.V., Pronin A.O., Kulikovskikh Yu.V. Issledovaniye funktsionirovaniya multiagentnoy sistemy upravleniya avtonomnogo neobitaemogo podvodnogo apparata s pomoshch’yu imitatsionnogo modelirovaniya.[Investigation of the functioning of a multi-agent control system of an autonomous uninhabited underwater vehicle using simulation simulation], Naukoyemkiye tekhnologii v kosmicheskikh issledovaniyakh Zemli [High technology in space exploration of the Earth], 2017, Vol. 9, No. 5, pp. 52-65.
6. Avtonomnyye podvodnyye roboty. Sistemy i tekhnologii. [Autonomous underwater robots. Systems and technologies], ed. by Ageyeva M.D. Moscow: Nauka, 2005, 306 p.
7. Martynova L.A., Mashoshin A.I. Postroeniye sistemy upravleniya avtonomnykh neobitayemykh podvodnykh apparatov na baze mul’tiagentnoy tekhnologii [Construction of a control system for autonomous uninhabited underwater vehicles based on multi-agent technology], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. engineering sciences], 2016,
No. 2 (175), pp. 38-48.
8. Martynova L.A.. Organizatsiya raspredelennykh vychisleniy v imitatsionnoy sisteme modelirovaniya raboty avtonomnogo neobitayemogo podvodnogo apparata [The organization of distributed computing in the simulation system of modeling the operation of an autonomous uninhabited underwater vehicle], Izvestiya YuFU. Tekhnicheskie nauki, [Izvestiya SFedU. engineering sciences], 2017, No. 2 (187), pp. 100-112.
9. Martynova L.A. Metod koordinatsii funktsionirovaniya agentov v mul’tiagentnoy sisteme upravleniya ANPA [The method of coordinating the functioning of agents in the ANPA multi-agent control system], International Conference on Naval Architecture and Ocean Engineering. Saint Petersburg: SPbGMTU, 2016, pp. 470-480.
10. Kostenko V.V., Mikhaylov D.N. Opredeleniye parametrov energosilovoy ustanovki avtonomnogo neobitayemogo podvodnogo apparata po zadannoy dalnosti khoda [Determination of the parameters of the power-plant of an autonomous uninhabited underwater vehicle for a given range of travel], Izvestiya YuFU. Tekhnicheskiye nauki [Izvestiya sfedu. engineering sciences], 2013, No. 3 (140), pp. 70-73.
11. Gerasimov V.A.. Filozhenko A.Yu.. Chepurin P.I. Struktura sistemy elektrosnabzheniya avtonomnogo neobitayemogo podvodnogo apparata. [The structure of the power supply system of an autonomous uninhabited underwater vehicle], Izvestiya YuFU. Tekhnicheskiye nauki [Izvestiya sfedu. engineering sciences], 2013, No. 3 (140), pp. 47-55.
12. Martynova L. A., Konyukhov G.V.. Rukhlov N.N.. Pronin A.O. Imitatsionnaya sistema funktsionirovaniya gruppy avtonomnykh neobitayemykh podvodnykh apparatov na mul’tiagentnoy osnove [Simulation system of functioning of a group of autonomous unmanned underwater vehicles on a multi-agent basis], Materialy Mezhdunarodnoy konferentsii po morskoy robototekhnike v osvoyenii okeana «Morskaya robototekhnika 2017 [Materials of the International Conference on Marine Robotics in Ocean Development "Marine Robotics 2017], Sankt-Peterburg. Russia.
13. Martynova L.A,. Grinenkov A.V., Pronin A.O., Kulikovskikh Yu.V. Imitatsionnoye modelirovaniye funktsionirovaniya mul’tiagentnoy sistemy upravleniya avtonomnogo neobitayemogo podvodnogo apparata [Simulation modeling of functioning of a multi-agent control system of an autonomous uninhabited underwater vehicle], Trudy Vos’moy Vserossiyskoy nauchno-prakticheskoy konferentsii «Imitatsionnoye modelirovaniye. Teoriya i praktika» (IMMOD-2017) [Proceedings of the eighth All-Russian scientific-practical conference "Imitation modeling. Theory and practice "(IMMOD-2017)]. St. Petersburg: Izd-vo VVM, 2017, pp.474-479.
14. Chandy K.M., Lamport L. Distributed snapshots: determining global states of distributed systems, ACM Transactions on Computer Systems, 1985, No. 3 (1), pp. 63-75.
15. Chandy K.M., Misra J. The Drinking Philosophers Problem ACM TOPLAS, 6:4, October 1984, pp. 632-646.
16. Charron-Bost B. Concerning the size of logical clocks in distributed systems, Information Processing Letters, 1991, No. 39, pp. 11-16.
17. Charron-Bost В., Tel G., Mattem F. Synchronous, asynchronous, and causally ordered communication, Distributed Computing, 1996, No. 9 (4); pp. 173-191.
18. Fidge C. Logical time in distributed computing systems, IEEE Computer, August 1991, pp. 28-33.
19. Fowler J., Zwaenepoel W. Causal distributed breakpoints, Proceeding: of the 10th International Conference on Distributed Computing System, 1990, pp. 134-141.
20. Mattern F. Virtual time and global states of distributed systems, Proceedings of die Parallel and Distributed Algorithms Conference (Cosnard, Quinton, Raynal, Robert Eds.). North-Holland, 1988, pp. 215-226.
21. Raymond K. Tree-based algorithm for distributed mutual exclusion, ACM Transactions on Computer Systems, 1989, No. 7, pp. 61-77.
22. Raynal M. A simple taxonomy of distributed mutual exclusion algorithms, Operating Systems Review, 1991, No. 25 (2), pp. 47-50.
23. Ricart G., Agrawala A.K. An optimal algorithm for mutual exclusion in computer networks, Communications of die ACM, 1981, No. 24 (1), pp. 9-17.
24. Kosyakov M. S. Vvedeniye v raspredelennyye vychisleniya [Introduction to distributed computing]. Saint Petersburg: NIU ITMO, 2014, 155 p.

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