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N-learning: An Approach for Learning and Teaching Skills in Multirobot Teams

Published online by Cambridge University Press:  16 April 2019

Luís Feliphe S. Costa Open the ORCID record for Luís Feliphe S. Costa [Opens in a new window] ,
Tiago P. Nascimento Open the ORCID record for Tiago P. Nascimento [Opens in a new window] ,
Rosiery da S. Maia  and
Luiz Marcos G. Gonçalves
Luís Feliphe S. Costa
Affiliation:
Department of Computer Engineering and Automation, Universidade Federal do Rio Grande do Norte, Natal, Brazil. E-mails: [email protected], [email protected]
Tiago P. Nascimento*
Affiliation:
Department of Computer Systems, Universidade Federal da Paraiba, Paraiba, Brazil
Rosiery da S. Maia
Affiliation:
Department of Informatics, Universidade do Estado do Rio Grande do Norte Mossoró, Brazil. E-mail: [email protected]
Luiz Marcos G. Gonçalves
Affiliation:
Department of Computer Engineering and Automation, Universidade Federal do Rio Grande do Norte, Natal, Brazil. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

We propose the N-learning practical approach for teaching and learning behaviors in a multirobot system, which is performed through mandatory behavior acquisition based on interactions between the robots at execution time. The proposed methodology can be used to self-program the robots of a team by programming only a single robot with a set of codes that contain behaviors to be transferred and used by other robots as necessary. These codes are implemented in a modular fashion. An advantage of our approach is that when a team of robots is required to perform a specific mission, the set of behaviors required to accomplish that mission can be implemented only once in a single robot or in a distributed fashion. Then, these distributed behaviors are transferred to each of the other robots in the team according to their demand, without the need to reprogram them by hand since the robots in the team can share them autonomously. As an application example, a human critic can teach (or program) only one or a few robots, and these robots are thus able to exchange knowledge with the other team members since they have been preinstalled to run the N-learning system basics. Simulated and real robot experiments are performed to demonstrate the feasibility and validation of our approach.

Keywords

Multirobot systemRobotic leaningAutonomous behaviorKnowledge acquisition
Type
Articles
Information
Robotica , Volume 38 , Issue 1 , January 2020 , pp. 48 - 68
Copyright
© Cambridge University Press 2019 

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References

Mansfield, M., Collins, J. J., Eaton, M. and Collins, T., “N-learning: A Reinforcement Learning Paradigm for Multiagent Systems,” In: AI 2005: Advances in Artificial Intelligence (Zhang, S. and Jarvis, R., eds.) (Springer, Berlin, Heidelberg, 2005) pp. 684–694.10.1007/11589990_71CrossRefGoogle Scholar
Brooks, R., “A robust layered control system for a mobile robot,” IEEE J. Rob. Autom. 2(1), 14–23 (1986).10.1109/JRA.1986.1087032CrossRefGoogle Scholar
Wahde, M., Introduction to Autonomous Robots (Copendium, Chalmers University of Technology, Goteborg, Sweden, 2016).Google Scholar
Stone, P. and Veloso, M., “Task Decomposition and Dynamic Role Assignment for Real - Time Strategic Teamwork,” International Workshop on Agent Theories, Architectures, and Languages (Springer, Berlin, Heidelberg, 1999) pp. 293–308. http://dx.doi.org/10.1007/3-540-49057-4_19Google Scholar
Brooks, R. A., “Intelligence without representation,” Artif. Intell. 47(1–3), 139–159 (1991).10.1016/0004-3702(91)90053-MCrossRefGoogle Scholar
Raza, S. A., Kanwal, A., Rehan, M., Khan, K. A., Aslam, M. and Asif, H. M. S., “Asia: Attention Driven Pre-conscious Perception for Socially Interactive Agents.” 2015 International Conference on Information and Communication Technologies (ICICT), Rennes, France (2015) pp. 1–8.Google Scholar
Simes, A. D. S., Colombini, E. L. and Ribeiro, C. H. C., “Conaim: A conscious attention-based integrated model for human-like robots,” IEEE Syst J. 11(3), 1296–1307 (2016).10.1109/JSYST.2015.2498542CrossRefGoogle Scholar
Corrente, G., Cunha, J., Sequeira, R. and Lau, N., “Cooperative Robotics: Passes in Robotic Soccer,” 2013 13th International Conference on Autonomous Robot Systems, Lisbon, Portugal (2013) pp. 1–6.Google Scholar
Gan, Y., Dai, X. and Da, Q., “Emulating Manual Welding Process by Two Cooperative Robots,” Proceedings of the 33rd Chinese Control Conference, Nanjing, China (2014), pp. 8414–8420.Google Scholar
Singh, P., Tiwari, R. and Bhattacharya, M., “Navigation in Multi robot System Using Cooperative Learning: A Survey,” 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), New Delhi, India (2016) pp. 145–150.Google Scholar
Daş, M. T., Dülger, L. C. and Daş, G. S., “Robotic Applications with Particle Swarm Optimization (PSO),” 2013 International Conference on Control, Decision and Information Technologies (CoDIT), Hammamet, Tunisia (2013) pp. 160–165.Google Scholar
Khan, M. T., Nasir, F., Qadir, M. U. and Iqbal, J., “Artificial Immune System Based Framework for Multi-robot Cooperation,” 2014 9th International Conference on Computer Science Education (ICCSE), Vancouver, Canada (2014) pp. 50–55.Google Scholar
Lin, H. I. and Lee, C. S. G., “Neuro-fuzzy-based skill learning for robots,” Robotica 30(6), 1013–1027 (2012).10.1017/S026357471100124XCrossRefGoogle Scholar
Maia, R. S. and Gonçalves, L. M. G., “Intellectual development model for multi-robot systems,” J. Intell. Rob. Syst. 80(1), 165–187 (2015). http://dx.doi.org/10.1007/s10846-015-0224-0.CrossRefGoogle Scholar
Vygotsky, L., “Play and its role in the mental development of the child,” Sov. Psychol. 5(3), 6–18 (1967).10.2753/RPO1061-040505036CrossRefGoogle Scholar
Piaget, J., The Equilibration of Cognitive Structures: The Central Problem of Intellectual Development (University of Chicago Press, Chicago, 1985).Google Scholar
Yan, Z., Jouandeau, N. and Cherif, A. A., “A survey and analysis of multi-robot coordination,” Int J. Adv. Rob. Syst. 10(12), 399 (2013). http://dx.doi.org/10.5772/57313.CrossRefGoogle Scholar
Rockel, S., Klimentjew, D. and Zhang, J., “A Multi-robot Platform for Mobile Robots; A Novel Evaluation and Development Approach with Multi-agent Technology,” 2012 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Hamburg, Germany (2012) pp. 470–477.Google Scholar
Muller, J. P. and Pischel, M., “Integrating Agent Interaction into a Planner-Reactor Architecture,” AAAI Workshop on Distributed AI, Seattle, USA (1994) pp. 229–243.Google Scholar
Lyons, D. and Hendriks, A., “Planning as incremental adaptation of a reactive system,” Rob. Auton. Syst. 14(4), 255–288 (1995). http://www.sciencedirect.com/science/article/pii/092188909400033X.10.1016/0921-8890(94)00033-XCrossRefGoogle Scholar
Groth, C. and Henrich, D., “Single-Shot Learning and Scheduled Execution of Behaviors for a Robotic Manipulator,” ISR/Robotik 2014; 41st International Symposium on Robotics, Munich, Germany (2014) pp. 1–6.Google Scholar
Huang, S., Aertbelien, E., Brussel, H. V. and Bruyninckx, H., “A Behavior-Based Approach for Task Learning on Mobile Manipulators,” ISR 2010 (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics), Munich, Germany (2010) pp. 1–6.Google Scholar
Di Mario, E. and Martinoli, A., “Distributed particle swarm optimization for limited-time adaptation with real robots,” Robotica 32(2), 193–208 (2014).10.1017/S026357471300101XCrossRefGoogle Scholar
Dorigo, M. and Schnepf, U., “Genetics-based machine learning and behavior-based robotics: a new synthesis,” EEE Trans. Syst. Man Cybern. 23(1), 141–154 (1993).10.1109/21.214773CrossRefGoogle Scholar
Mendiburu, F. J., Morais, M. R. A. and Lima, A. M. N., “Behavior Coordination in Multi-robot Systems,” 2016 IEEE International Conference on Automatica (ICA-ACCA), Curico, Chile (2016) pp. 1–7.Google Scholar
Ray, D. N., Mandal, A., Majumder, S. and Mukhopadhyay, S., “Human-Like Gradual Multi-agent q-Learning Using the Concept of Behavior-Based Robotics for Autonomous Exploration,” 2011 IEEE International Conference on Robotics and Biomimetics, Karon Beach, Phuket, Thailand (2011) pp. 2725–2732.Google Scholar
Parker, L. E., “Alliance: An architecture for fault tolerant multirobot cooperation,” IEEE Trans. Rob. Autom. 14(2), 220–240 (1998).10.1109/70.681242CrossRefGoogle Scholar
Li, M., Cai, Z., Yi, X., Wang, Z., Wang, Y., Zhang, Y. and Yang, X., “ALLIANCE-ROS: A Software Architecture on ROS for Fault-Tolerant Cooperative Multi-robot Systems” Pacific Rim International Conference on Artificial Intelligence (Springer International Publishing, Cham, 2016) pp. 233–242. http://dx.doi.org/10.1007/978-3-319-42911-3_19.CrossRefGoogle Scholar
Forrest, S., “Emergent computation: Self-organizing, collective, and cooperative phenomena in natural and artificial computing networks,” Physica D: Nonlinear Phenom. 42(1), 1–11 (1990).10.1016/0167-2789(90)90063-UCrossRefGoogle Scholar
ROS.org, Ros (2015). http://wiki.ros.org/pt.Google Scholar
Gerkey, B. P., Vaughan, R. T. and Howard, A., “The Player/Stage Project: Tools for Multi-robot and Distributed Sensor Systems,” Proceedings of the 11th International Conference on Advanced Robotics, Coimbra, Portugal (2003) pp. 317–323.Google Scholar
Costa, L. F., Online resource - first simulated experiment (2017). https://www.dropbox.com/s/i4vk4san1bmlxs2/OnlineResource1.mp4?dl=0.Google Scholar
Costa, L. F., Online resource - second experiment in simulation (2017). https://www.dropbox.com/s/l2hlmfk17rqraxu/OnlineResource2.mp4?dl=0.Google Scholar