Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-29T08:25:41.266Z Has data issue: false hasContentIssue false
  • English
  • Français

Toward an interdisciplinary integration between multi-agents systems and multi-robots systems: a case study

Part of: Ontologies and Standards for Intelligent Systems

Published online by Cambridge University Press:  13 August 2020

Wagner Tanaka Botelho Open the ORCID record for Wagner Tanaka Botelho [Opens in a new window] ,
Maria Das Graças Bruno Marietto ,
Eduardo De Lima Mendes ,
Daniel Rodrigues De Sousa ,
Edson Pinheiro Pimentel ,
Vera Lúcia da Silva  and
Tamires dos Santos
Wagner Tanaka Botelho
Affiliation:
Federal University of ABC, Centre of Mathematics, Computation and Cognition, Av. dos Estados, 5001, Bairro Santa Terezinha, CEP 09210-580, Santo André/São Paulo, Brazil, e-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Maria Das Graças Bruno Marietto
Affiliation:
Federal University of ABC, Centre of Mathematics, Computation and Cognition, Av. dos Estados, 5001, Bairro Santa Terezinha, CEP 09210-580, Santo André/São Paulo, Brazil, e-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Eduardo De Lima Mendes
Affiliation:
Federal University of ABC, Centre of Mathematics, Computation and Cognition, Av. dos Estados, 5001, Bairro Santa Terezinha, CEP 09210-580, Santo André/São Paulo, Brazil, e-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Daniel Rodrigues De Sousa
Affiliation:
Faculty of Technology (FATEC-Itaquera), São Paulo, Brazil, e-mail: [email protected]
Edson Pinheiro Pimentel
Affiliation:
Federal University of ABC, Centre of Mathematics, Computation and Cognition, Av. dos Estados, 5001, Bairro Santa Terezinha, CEP 09210-580, Santo André/São Paulo, Brazil, e-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Vera Lúcia da Silva
Affiliation:
Federal Institute of Education, Science and Technology of São Paulo, Brazil, e-mail: [email protected]
Tamires dos Santos
Affiliation:
Federal University of ABC, Centre of Mathematics, Computation and Cognition, Av. dos Estados, 5001, Bairro Santa Terezinha, CEP 09210-580, Santo André/São Paulo, Brazil, e-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Multi-Robot System (MRS) is composed of a group of robots that work cooperatively. However, Multi-Agent System (MAS) is computational systems consisting of a group of agents that interact with each other to solve a problem. The central difference between MRS and MAS is that in the first case, the agent is a robot, and in the second, it is a software. Analyzing the scientific literature, it is possible to notice that few studies address the integration between MAS and MRS. In order to achieve the interdisciplinary integration, the theoretical background of these areas must be considered in this paper, so that the integration can be applied using a case study of decentralized MRS. The objective of this MRS is to track and surround a stationary target. Also, it has been implemented and validated in the robot simulator called Virtual Robot Experimentation Platform (V-REP). In the validation of the proposed MRS, a scenario with three robots and a stationary target were defined. In the tracking task, the robot can detect the target whose position is not known a priori. When the detection occurs, the V-REP informs the target position to the robot because the environment is discretized into a grid of rectangular cells. After that, all the robots are directed to the target, and the surround task is realized. In this task, a mathematical model with direct communication between the robots was used to keep the robots equidistant therefrom and from each other.

Type
Review
Information
The Knowledge Engineering Review , Volume 35 , 2020 , e35
Copyright
© The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Amato, C., Konidaris, G., Cruz, G., Maynor, C. A., How, J. P. & Kaelbling, L. P. 2015. Planning for decentralized control of multiple robots under uncertainty. In International Conference on Robotics and Automation (ICRA), 1241–1248, IEEE.CrossRefGoogle Scholar
Arai, T., Pagello, E. & Parker, L. E. 2002. Guest editorial advances in multirobot systems. IEEE Transactions on Robotics and Automation 18(5), 655–661.CrossRefGoogle Scholar
Arkin, R. C. & Balch, T. 1998. Artificial intelligence and mobile robots. In Cooperative Multiagent Robotic Systems, 277–296, MIT Press.Google Scholar
Axelrod, R. 1998. Advancing the art of simulation in the social sciences. Complexity 3(2), 16–22.3.0.CO;2-K>CrossRefGoogle Scholar
Azarm, K. & Schmidt, G. 1997. Conflict-free motion of multiple mobile robots based on decentralized motion planning and negotiation. In Proceedings of IEEE International Conference on Robotics and Automation, 4, 3526–3533.CrossRefGoogle Scholar
Balázs, B. & Vásárhelyi, G. 2018. Coordinated dense aerial traffic with self-driving drones. In IEEE International Conference on Robotics and Automation (ICRA), 6365–6372.Google Scholar
Balch, T., Boone, G., Collins, T., Forbes, H., Mackenzie, D. & Santamaria, J. C. 1995. Io, Ganymede and Callisto - a multiagent robot trash-collecting team. AI Magazine 16(2), 39–51.Google Scholar
Barrett, A., Rabideau, G., Estlin, T. & Chien, S. 2001. Coordinated continual planning methods for cooperating rovers. In IEEE Proceedings of Aerospace Conference, 1, 1/133–1/140.Google Scholar
Batista, M. R., Pinto, A. H. M. & Romero, R. A. F. 2015. Addressing escorting by behavior combining using multiple differential drive robots. In 12th Latin American Robotics Symposium and 3rd Brazilian Symposium on Robotics (LARS-SBR), 187–191, IEEE.CrossRefGoogle Scholar
Bond, A. H. & Gasser, L. 1988. Readings in Distributed Artificial Intelligence, Morgan Kaufmann Pub.Google Scholar
Bonev, I. A., Zlatanov, D. & Gosselin, C. M. 2002. Advantages of the modified Euler Angles in the design and control of PKMS. In International Conference of Parallel Kinematic Machines, 171–188, Citeseer.Google Scholar
Botelho, S. & Alami, R. 2000. Robots that cooperatively enhance their plans. In Distributed Autonomous Robotic Systems, 4, 55–65, Springer.Google Scholar
Botelho, W. T., Marietto, M. G. B., Mendes, E. L., Ferreira, J. C. M. & Silva, V. L. 2017. Multi-Robot system for tracking and surrounding a stationary target: A decentralized and cooperative approach, In IEEE International Conference on Robotics and Biomimetics (ROBIO), 793–798.Google Scholar
Bouteraa, Y., Mansour, A. B., Ghommam, J. & Poisson, G. 2011. Cooperative control and synchronization with time delays of multi-robot systems. Journal of Engineering and Computer Innovations 2(2), 28–39.Google Scholar
BubbleRob. 2017. Bubblerob - V-Rep. https://tinyurl.com/y7ls2nvx. Accessed April 01, 2019.Google Scholar
Butzke, J., Daniilidis, K., Kushleyev, A., A., Lee, D. D., Likhachev, M., Phillips, C. & Phillips, M. 2012. The University of Pennsylvania Magic 2010 multi-robot unmanned vehicle system. Journal of Field Robotics 29(5), 745–761.CrossRefGoogle Scholar
Cao, Y. U., Fukunaga, A. S., Kahng, A. B. & Meng, F. 1995. Cooperative mobile robots: antecedents and direction. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 226–234.Google Scholar
Chalupsky, H., Finin, T., Fritzson, R., McKay, D., Shapiro, S. & Wiederhold, G. 1992. An overview of KQML: a knowledge query and manipulation language.Google Scholar
Clark, C. M., Rock, S. M. & Latombe, J.-C. 2003. Dynamic networks for motion planning in multi-robot space systems. In Proceedings of the 7th International Symposium on Artificial Intelligence, Robotics and Automation in Space (SAIRAS).Google Scholar
Dahale, K. P., Chaudhari, V. D. & Rane, K. 2014. Multi-robot coordination using embedded controller. International Journal of Application or Innovation in Engineering & Management (IJAIEM) 3(3), 40–46.Google Scholar
de, M.Batista, A. F., das, G. B.Marietto, M., Botelho, W. T., Kobayashi, G., dos, P.Alves, B., de Castro, S. & Ruas, T. L. 2011. Principles of agent-oriented programming. In Alkhateeb, F., Maghayreh, E. A. & Doush, I. A. (eds.). Multi-Agent Systems - Modeling, Control, Programming, Simulations and Applications, 317–342, InTech.Google Scholar
Dorigo, M., Birattari, M. & Stützle, T. 2006. Ant colony optimization. IEEE - Computational Intelligence Magazine 1(4), 28–39.CrossRefGoogle Scholar
Dorigo, M.et al. 2013. Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robotics Automation Magazine 20(4), 60–71.CrossRefGoogle Scholar
Ducatelle, F., Caro, G. A. D., Pinciroli, C., Mondada, F. & Gambardella, L. 2011. Communication assisted navigation in robotic swarms: self-organization and cooperation. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 4981–4988.Google Scholar
Fallah-Seghrouchini, A. E., Haddad, S. & Mazouzi, H. 2001. A formal study of interaction in multi-agent systems. International Journal of Computers and Their Applications 8(1).Google Scholar
Farinelli, A., Iocchi, L. & Nardi, D. 2004. Multi-robot systems: a classification focused coordination. IEEE Transactions on Systems, Man and Cybernetics 34(5), 2015–2028.CrossRefGoogle Scholar
Feddema, J. T., Lewis, C. & Schoenwald, D. A. 2002. Decentralized control of cooperative robotic vehicles: theory and application. IEEE Transactions on Robotics and Automation 18(5), 852–864.CrossRefGoogle Scholar
FIPA ACL. 2002. FIPA ACL Message Structure Specification. https://tinyurl.com/yb6yqf7e. Accessed January 19, 2019.Google Scholar
Gera, D. L. 2003. Ancient Greek Ideas on Speech, Language, and Civilization, Oxford University Press.CrossRefGoogle Scholar
Gil, S., Kumar, S., Katabi, D. & Rus, D. 2015. Adaptive Communication in Multi-Robot Systems Using Directionality of Signal Strength. The International Journal of Robotics Research 34(7), 946–968.CrossRefGoogle Scholar
Hsieh, M. A., Cowley, A., Kumar, V. & Taylor, C. J. 2008. Maintaining network connectivity and performance in robot teams. Journal of Field Robotics 25(1-2), 111–131.CrossRefGoogle Scholar
Huhns, M. N. & Stephens, L. M. 1999. Multiagent Systems and Societies of Agents, 79–120, MIT Press.Google Scholar
Iocchi, L., Nardi, D. & Salerno, M. 2001. Reactivity and deliberation: a survey on multi-robot systems. In Balancing Reactivity and Social Deliberation in Multi-Agent Systems, 9–32, Springer Berlin Heidelberg.CrossRefGoogle Scholar
IRobot. 2016. Irobot Create. https://tinyurl.com/y89vr44t. Accessed October 10, 2018.Google Scholar
Jennings, H. 1995. Controlling cooperative problem solving in industrial multiagent systems using joint intention. Artificial Intelligence 2(75), 195–240.CrossRefGoogle Scholar
Lamport, L. 1978. Time, clocks, and the ordering of events in a distributed system. Communications of the ACM 21(7), 558–565.CrossRefGoogle Scholar
Lawton, J. R. T., Beard, R. W. & Young, B. J. 2003. A decentralized approach to formation maneuvers. IEEE Transactions on Robotics and Automation 19(6), 933–941.CrossRefGoogle Scholar
Lempert, R. 2002. Agent-based modeling as organizational and public policy simulators. Proceedings of the National Academy of Sciences of the United States of America, 99, 7195.CrossRefGoogle ScholarPubMed
Lemvigh, D. & Moller, A. 2008. Advanced robot navigation for multiagent system using LEGO NXT, Bachelor thesis, Technical University of Denmark.Google Scholar
Mas, I., Li, S., Acain, J. & Kitts, C. 2009. Entrapment/escorting and patrolling missions in multi-robot cluster space control. In IEEE/RJS International Conference on Intelligent Robots and Systems (IROS), 5855–5861.Google Scholar
Matsumoto, A., Asama, H., Ishida, Y., Ozaki, K. & Endo, I. 1990. Communication in the autonomous and decentralized robot system actress. In Proceedings of International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications, 835–840.Google Scholar
McCarthy, J., Minsky, M. L., Rochester, N. & Shannon, C. 1955. Dartmouth conference. In A Proposal for the DartMounth Summer Research Project on Artifical Intelligence, 1.Google Scholar
Mendes, E. L., Botelho, W. T. & Marietto, M. G. B. 2018a. Decentralized MRS: simulation results with four robot (V-Rep). http://youtu.be/qj0Q8oV8j3w. Accessed February 05, 2019.Google Scholar
Mendes, E. L., Botelho, W. T. & Marietto, M. G. B. 2018b. Decentralized MRS: simulation results with three robot (V-Rep). http://youtu.be/7_gShy608VQ. Accessed February 05, 2019.Google Scholar
Murphy, R. R. 2000a. Introduction to AI Robotics, Cambridge, Massachusetts: MIT Press.Google Scholar
Murphy, R. R. 2000b. Marsupial and shape-shifting robots for urban search and rescue. Intelligent Systems and their Applications 15(2), 14–19.Google Scholar
Obst, O. & Boedecker, J. 2006. Flexible coordination of multiagent team behavior using HTN planning. In Bredenfeld, A., Jacoff, A., Noda, I. & Takahashi, Y. (eds). RoboCup 2005: Robot Soccer World Cup IX, 521–528, Springer Berlin Heidelberg.CrossRefGoogle Scholar
Oldham, C. 2015. Futuristic Talos delivering immediate benefits. https://tinyurl.com/y986pst7. Accessed February 10, 2019.Google Scholar
Ota, J. 2006. Multi-agent robot systems as distributed autonomous systems. Advanced Engineering Informatics 20(1), 59–70.CrossRefGoogle Scholar
Pereira, G. A. S., Campos, M. F. M. & Kumar, V. 2004. Decentralized algorithms for multi-robot manipulation via caging. The International Journal of Robotics Research 23(7-8), 783–795.CrossRefGoogle Scholar
Pessin, G., Osório, F., Musse, S., Nonnenmacher, V. & Ferreira, S. S. 2007. Evoluindo Estratégias de Posicionamento em um Sistema Multi-Robótico Aplicado ao Combate de Incêndios Florestais (Positioning strategies in a multi-robot system applied to combat forest fires, in Portuguese). Revista Hífen 32(64), 78–84.Google Scholar
Pirjanian, P. 1997. An overview of system architectures for action selection in mobile robotics. Laboratory of Image Analysis, Aalborg University, Aalborg, Denmark, 1.Google Scholar
Prestes, E., Carbonera, J. L., Fiorini, S. R., Jorge, V. A. M., Abel, M., Madhavan, R., Locoro, A., Goncalves, P., Barreto, M. E., Habib, M., Chibani, A., Gérard, S., Amirat, Y. & Schlenoff, C. 2013. Towards a core ontology for robotics and automation. Robotics and Autonomous Systems 61(11), 1193–1204. Ubiquitous Robotics.CrossRefGoogle Scholar
Reis, J. C. G., Lima, P. U. & Garcia, J. 2014. Efficient distributed communications for multi-robot systems. In Behnke, S., Veloso, M., Visser, A. & Xiong, R. (eds). RoboCup 2013: Robot World Cup XVII, 8371, Springer Berlin Heidelberg, 280–291.CrossRefGoogle Scholar
Rezende, S. O. 2005. Sistemas Inteligentes (Intelligent Systems, in Portuguese), Editora Manole Ltda.Google Scholar
Riisgaard, S. & Blas, M. R. 2003. Slam for dummies. A Tutorial Approach to Simultaneous Localization and Mapping 22(1–127), 126.Google Scholar
Rockel, S. 2011. A multi-robot platform for mobile robots with multi-agent technology, Master’s thesis, University of Hamburg.Google Scholar
Rockel, S., Klimentjew, D. & Zhang, J. 2012. A multi-robot platform for mobile robots - a novel evaluation and development approach with multi-agent technology. In Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 470–477.Google Scholar
Roldán, J., Garcia-Aunon, P., Garzón, M., de León, J., del Cerro, J. & Barrientos, A. 2016. Heterogeneous multi-robot system for mapping environmental variables of greenhouses. Sensors 16(7), 1018.CrossRefGoogle ScholarPubMed
Romero, R., Preste, E., Osório, F. & Wolf, D. F. 2014. Robótica Móvel (Mobile Robots, in Portuguese), LTC.Google Scholar
Rooker, M. N. & Birk, A. 2007. Multi-robot exploration under the constraints of wireless networking. Control Engineering Practice 15(4), 435–445.CrossRefGoogle Scholar
Ruas, T. L., das, G. B.Marietto, M., de Moraes Batista, A. F., dos, S.França, R., Heideker, A., Noronha, E. A. & da Silva, F. A. 2011. Modeling artificial life through multi-agent based simulation. In Alkhateeb, F., Maghayreh, E. A. & Doush, I. A. (eds). Multi-Agent Systems - Modeling, Control, Programming, Simulations and Applications, InTech, 41–62.Google Scholar
Schmuck, P. & Chli, M. 2017. Multi-UAV collaborative monocular SLAM. In IEEE International Conference on Robotics and Automation (ICRA), 3863–3870.Google Scholar
Searle, J. R. 1969. Speech Acts: An Essay in the Philosophy of Language, Cambridge University Press.CrossRefGoogle Scholar
Sensor, U. 2017a. Ultrasonic Sensor - V-Rep. https://tinyurl.com/yc7jh45g. Accessed April 27, 2019.Google Scholar
Sensor, V. 2017b. Vision Sensor - V-Rep. https://tinyurl.com/yagtj3nc. Accessed March 07, 2019.Google Scholar
Simmons, R., Apfelbaum, D., Burgard, W., Fox, D., Moors, M., Thrun, S. & Younes, H. 2000. Coordination for multi-robot exploration and mapping. In Proceedings of Conference on Association for the Advancement of Artificial Intelligence (AAAI), 852–858.Google Scholar
Spurny, V., Baca, T. & Saska, M. 2016. Complex manoeuvres of heterogeneous Mav-Ugv formations using a model predictive control. In 21st International Conference on Methods and Models in Automation and Robotics (MMAR), 998–1003.Google Scholar
Stanek, O. 2012. Centralized multirobot system, Bachelor Thesis, The Department of Software Engineering, Charles University in Prague.Google Scholar
Steele, F. & Thomas, G. 2007. Directed stigmergy-based control for multi-robot systems. In 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI), 223–230.Google Scholar
Turpin, M., Michael, N. & Kumar, V. 2014. Capt: Concurrent assignment and planning of trajectories for multiple robots. The International Journal of Robotics Research 33(1), 98–112.CrossRefGoogle Scholar
V-REP. 2018. Coppelia robotics. http://www.coppeliarobotics.com. Accessed April 17, 2019.Google Scholar
Vásárhelyi, G., Virágh, C., Somorjai, G., Nepusz, T., Eiben, A. E. & Vicsek, T. 2018. Optimized flocking of autonomous drones in confined environments. Science Robotics 3(20).CrossRefGoogle Scholar
Virágh, C., Nagy, M., Gershenson, C. & Vásárhelyi, G. 2016. Self-organized UAV traffic in realistic environments. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1645–1652.Google Scholar
Wagdy, A. & Khamis, A. 2013. Adaptive group formation in multirobot systems. In Advances in Artificial Intelligence.CrossRefGoogle Scholar
Yan, Z., Jouandeau, N. & Cherif, A. A. 2013. A survey and analysis of multi-robot coordination. International Journal Advanced Robot System 10(399), 376–386.Google Scholar

Botelho et al. supplementary material

Botelho et al. supplementary material 1

Download Botelho et al. supplementary material(Video)
Video 2.2 MB

Botelho et al. supplementary material

Botelho et al. supplementary material 2

Download Botelho et al. supplementary material(Video)
Video 4 MB