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System wyznaczania ścieżek przemieszczeń dla grupy autonomicznych mobilnych robotów-agentów rozpoznawczo-inspekcyjnych

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EN
A system of path determination for a group of autonomous, mobile inspection robots-agents
Języki publikacji
PL
Abstrakty
PL
W artykule opisano oryginalny system wieloagentowy, którego podstawowym celem jest rozpoznanie, a następnie przeprowadzanie inspekcji nieznanego obszaru przy wykorzystaniu grupy autonomicznych mobilnych robotów-agentów. Wnioski z przeprowadzonych symulacji systemu pozwalają na rozpoznanie obszaru wiedzy, dotyczącego szczególnych aspektów systemów wieloagentowych takich jak min: metody ustalania ścieżek przemieszczeń agentów mobilnych czy wpływ liczby agentów na globalną efektywność systemu.
EN
The paper describes an original multi-agent system [1, 2, 10, 11] whose primary purpose is to recognize and, subsequently, to inspect continuously a limited area. The agents are autonomous mobile robots, equipped with a set of sensors that are capable of scanning the examined area and collecting information on significant features. The main decision problem necessary to be solved in the system is to determine the paths for the robotic agents in such a way that the area is recognized in the shortest time, and the inspection process is the most effective from the point of view of the state of knowledge about the significant features of the examined area. The agents should act collectively in such a way as to balance their load and, hence, to optimize the system performance. In the paper there are described in detail the consecutive steps of the algorithm (Fig. 1), and, in particular, the methodology for determining the direction of motion of robotic agents (Subsections 5.3 and 5.4). The presented multi-agent system was implemented in the Webots environment [4]. Educational robots of the e-puck class were used as robots-agents (Fig. 2), [3]. Implementation of the system made it possible to perform a series of experiments which allowed drawing interesting conclusions (Section 6) on the effectiveness of the system in achieving its primary objectives. At the end of the paper the experiment is illustrated (Fig. 7), and also the growth chart of recognition of the area for each robotic agent is presented (Fig. 8).
Wydawca
Rocznik
Strony
1155--1159
Opis fizyczny
Bibliogr. 13 poz., rys., wykr., wzory
Twórcy
  • Politechnika Koszalińska, Wydział Mechaniczny, Katedra Mechaniki Precyzyjnej, Racławicka 15-17, 75-620 Koszalin
Bibliografia
  • [1] Dasgupta P.: Multi-agent coordination techniques for multi-robot task allocation and multi-robot area coverage. Collaboration Technologies and Systems (CTS), International Conference on Digital Object Identifier 2012.
  • [2] Dasgupta P.: Multi-Robot Task Allocation for Performing Cooperative Foraging Tasks in an Initially Unknown Environment. Innovations in Defense Support Systems - 2, Springer, Studies in Computational Intelligence, vol. 338, s. 5-20, 2011.
  • [3] Mondada F., Bonani M., Raemy X., Pugh J., Cianci C., Klaptocz A., Magnenat S., Zufferey J. C., Floreano D., Martinoli, A.: The e-puck, a Robot Designed for Education in Engineering. Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions, s. 59-65.
  • [4] Michel O.: Webots: Professional Mobile Robot Simulation, International Journal of Advanced Robotic Systems, vol. 1, Num. 1, s. 39-42.
  • [5] Ellips M., Amin-Naseri M. R.: A Voronoi Diagram-Visibility Graph-Potential Field Compound Algorithm for Robot Path Planning. Journal o Robotic Systems, vol. 21, s. 275-300, 2004.
  • [6] Barracuand J., Langlois B., Latombe J.: Numerical potential field techniques for robot path planning. IEEE Transactions on systems man and cybernetics, vol. 22, s. 224-241, 1992.
  • [7] Ge S. S ., Cui Y. J.: Dynamic Motion Planning for Mobile Robots Using Potential Field Method. Autonomous Robots vol. 13, Issue: 3, s. 207–222, 2002.
  • [8] Kim Dong H., Shin Seiichi.: Self-organization of Decentralized Swarm Agents Based on Modified Particle Swarm Algorithm. Journal of Intelligent and Robotic Systems, vol. 46, Issue 2, s. 129–149, 2006.
  • [9] Aydin Mehmet Emin.: Coordinating metaheuristic agents with swarm intelligence. Journal of Intelligent Manufacturing, vol. 23, Issue: 4, s. 991–999, 2012.
  • [10] Ephrati E., Jeffrey S.: A heuristic technique for multi‐agent planning. Annals of Mathematics and Artificial Intelligence, vol. 20, Issue: 1-4, s. 13–67, 1997.
  • [11] Tehrani Nik, Nejad Hossein, Sugimura Nobuhiro, Iwamura Koji, Tanimizu Yoshitaka.: Multi agent architecture for dynamic incremental process planning in the flexible manufacturing system. Journal of Intelligent Manufacturing, vol. 21, Issue: 4, s. 487–499, 2010.
  • [12] Neto A., Macharet A., Douglas G., Mario F. M.: On the Generation of Trajectories for Multiple UAVs in Environments with Obstacles. Journal of Intelligent and Robotic Systems, vol. 57, Issue: 1-4, s. 123–141, 2010.
  • [13] Ioannidis K., Sirakoulis G. Ch., Andreadis I.: Cellular ants: A method to create collision free trajectories for a cooperative robot team. Robotics and Autonomous Systems, vol. 59, Issue: 2, s. 113-127, 2011.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3475d00d-79be-4fcc-a560-6a6ab2479004
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