Abstract
The work is devoted to the design of bio-inspired methods and algorithms for controlling the collective behavior of robots based on adaptive behavior models of multi-agent biological systems of the region: detection of harmful substances; search and rescue operations in areas of natural and technological disasters, as well as combat zones, patrolling the borders of a certain area. The fundamental idea of swarm control is “swarm intelligence”. The control of a homogeneous swarm of robots is based on the principle of force relaxation. The search for a solution is carried out in an affine space whose elements are n-dimensional points (positions). Each robot ri calculates the objective function f (Xi(t)) - the value of the desired substance at the point Xi(t). The fundamental problem that was solved in this paper is related to the development of the structure of an affine position space that allows one to display and search for interpretations of solutions with integer parameter values. Coordinate values must be discrete and independent of each other. In contrast to the canonical particle swarm method, in order to reduce the weight of affine bonds, by moving the pi particle to a new position in the affine solution space, a directed mutation operator has been developed, the essence of which is to change the integer values of the genes in the chromosome. New chromosome structures have been developed for representing solutions. The temporal complexity of the algorithm lies in the range O (n2)–O (n3), where n is the number of robots.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Karpenko, A.P.: Robotics and computer-aided design systems: Textbook. Publishing House MSTU them N.E. Bauman, Moscow, 71 p. (2014)
Syryamkina, V.I.: Teams of intelligent robots. Scopes of application. In: Syryamkina, V.I. (ed.) (Series: Intelligent Technical Systems) (sub-series: Cognitive Robotics). STT, Tomsk, 140 p. (2018)
Bondarchuk, A.S., Borovik, V.S., Gutsul, V.I.: Intelligent Robotic Systems: Textbook. Syryamkina, V.I. (ed.) (Series: Intelligent Technical Systems). Tomsk, 256 p. (2017)
Kalyaev, I.A., Gaiduk, A.R., Kapustyan, S.G. Models and Algorithms of Collective Control in Groups of Robots. Fizmatlit, Moscow, 280 p. (2009)
Karpov, V.E.: The collective behavior of robots. Desired and actual, modern mechatronics. In: Sat scientific Proceedings of the All-Russian Scientific School,132 p. (2011)
Ivanov, D.Ya.: Swarm intelligence methods for controlling groups of small unmanned aerial vehicles. In: Izvestiya SFU. Technical Science, Taganrog, vol. 3, no. 116, pp. 221–229 (2011)
Kalyaev, I.A., Gaiduk, A.R.: Flocking management principles in a group of objects. Mechatron. Autom. Manage. 12, 27–38 (2004)
Karpenko, A.P.: Modern Search Engine Optimization Algorithms. In: Algorithms inspired by nature: a tutorial. Publishing House MSTU them N.E. Bauman, Moscow, 448 p. (2014)
Lebedev, B.K., Lebedev, O.B., Lebedeva, E.O.: Swarm algorithm for planning the operation of multiprocessor computing systems. Electron. Sci. J. Eng. Bull. Don 3 (2017)
Lebedev, B.K., Lebedev, O.B., Lebedeva, E.M.: Resource allocation based on hybrid swarm intelligence models. Sci. Tech. J. Inf. Tech. Mech. Opt. 17(6), 1063–1073 (2017)
Clerc, M.: Particle Swarm Optimization. ISTE, London, UK, 187 p. (2006)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Cong, J., Romesis, M., Xie, M.: Optimality, scalability and stability study of partitioning and placement algorithms. In: Proceedings of the International Symposium on Physical Design, Monterey, CA, pp. 88–94 (2003)
Lebedev, B.K., Lebedev, O.B.: Hybrid bioinspired algorithm based on the integration of the branch and bound method and the ant colony method. Bull. Rostov State Transp. Univ. 2(70), 77–88 (2018)
Vorobeva, E.Yu., Karpenko, A.P., Seliverstov, E.Yu.: Co-hybridization of particle swarm algorithms. Sci. Educ. Electr. J. 4 (2012)
Agasiev, T.A., Karpenko, A.P.: Modern technology of global optimization. Inf. Tech. 6, 370–386 (2018)
Wang, X.: Hybrid nature-inspired computation method for optimization. Doctoral dissertation. Helsinki University of Technology, TKK Dissertations, Espoo, 161 p. (2009)
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35, 268–308 (2003)
Lebedev, V.B.: Construction of the shortest connecting networks on the basis of swarm intelligence. Izvestiya SFU. Publishing house TTI SFedU, vol. 7, pp. 37–44 (2011)
Sha, D.Y., Hsu, C.-Y.: A hybrid particle swarm optimization for job shop scheduling problem. Comput. Ind. Eng. 51, 791–808 (2006)
OR-Library is collection of test data for a variety of OR problem. http://mscmga.ms.ic.ac.uk
Acknowledgements
This research is supported by grants of the Russian Foundation for Basic Research of the Russian Federation, the project № 19-07-00645.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Veselov, G.E., Lebedev, B.K., Lebedev, O.B. (2020). Management of Behavior of a Swarm of Robots Applicable to the Tasks of Monitoring a Some Territory. In: Silhavy, R. (eds) Artificial Intelligence and Bioinspired Computational Methods. CSOC 2020. Advances in Intelligent Systems and Computing, vol 1225. Springer, Cham. https://doi.org/10.1007/978-3-030-51971-1_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-51971-1_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51970-4
Online ISBN: 978-3-030-51971-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)