Coordination, Synchronization and Localization Investigations in a Parallel Intelligent Robot Cellular Automata Model that Performs Foraging Task

Danielli A. Lima, Claudiney R. Tinoco, Juan M. N. Viedman, Gina M. B. Oliveira

2017

Abstract

Multiple agent systems can be applied to foraging tasks, thus solving this problem in a cooperative intelligent approach using cellular automata modeling. The objective is to construct an algorithm that performs foraging task correctly in Webots EDU simulation platform using robot architecture and also improves the individual controller model of each intelligent agent, using e-Puck devices properly. The proposed communication model has taken into account some cellular automata specifications, such as, the need for parallel synchronization, localization and accuracy of information dependency. After several simulations in Webots EDU, evaluating different approaches, the proposed communication model presented promising results on the parallel multi-robot foraging performance being pertinent in intelligent swarm robotics context.

References

  1. Alizadeh, R. (2011). A dynamic cellular automaton model for evacuation process with obstacles. Safety Science, 49(2):315-323.
  2. Beckers, R., Holland, O., and Deneubourg, J.-L. (1994). From local actions to global tasks: Stigmergy and collective robotics. In Artificial life IV , volume 181, page 189.
  3. Behring, C., Bracho, M., Castro, M., and Moreno, J. (2001). An algorithm for robot path planning with cellular automata. In Theory and practical issues on cellular automata, pages 11-19. Springer.
  4. Byun, H. and Yu, J. (2014). Cellular-automaton-based node scheduling control for wireless sensor networks. Vehicular Technology, IEEE Transactions on, 63(8):3892-3899.
  5. Calvo, R., de Oliveira, J. R., Figueiredo, M., and Romero, R. A. (2014). Parametric investigation of a distributed strategy for multiple agents systems applied to cooperative tasks. In Proceedings of the 29th Annual ACM Symposium on Applied Computing, pages 207-212. ACM.
  6. Calvo, R., de Oliveira, J. R., Figueiredo, M., and Romero, R. A. F. (2011). Bio-inspired coordination of multiple robots systems and stigmergy mechanims to cooperative exploration and surveillance tasks. In Cybernetics and Intelligent Systems (CIS), pages 223-228. IEEE.
  7. Couceiro, M. S., Rocha, R. P., and Ferreira, N. M. (2011). A novel multi-robot exploration approach based on particle swarm optimization algorithms. In Safety, Security, and Rescue Robotics (SSRR), 2011 IEEE International Symposium on, pages 327-332. IEEE.
  8. Falleiros, E. L. S., Calvo, R., and Ishii, R. P. (2015). Pheroslam: A collaborative and bioinspired multiagent system based on monocular vision. In Computational Science and Its Applications-ICCSA 2015 , pages 71-85. Springer.
  9. Ferreira, G. B., Vargas, P. A., and Oliveira, G. M. (2014). An improved cellular automata-based model for robot path-planning. In Advances in Autonomous Robotics Systems, pages 25-36. Springer.
  10. Fortunati, L. (2016). Moving robots from industrial sectors to domestic spheres: A foreword. In Toward Robotic Socially Believable Behaving SystemsVolume II, pages 1-3. Springer.
  11. Glover, F. (1989). Tabu search part i. ORSA Journal on computing, 1(3):190-206.
  12. Glover, F. (1990). Tabu search part ii. ORSA Journal on computing, 2(1):4-32.
  13. Gordon, D. M. (2014). The ecology of collective behavior. PLoS Biol, 12(3):e1001805.
  14. Kantor, G., Singh, S., Peterson, R., Rus, D., Das, A., Kumar, V., Pereira, G., and Spletzer, J. (2006). Distributed search and rescue with robot and sensor teams. In Field and Service Robotics, pages 529-538. Springer.
  15. Lima, D. A. and Oliveira, G. M. B. (2016a). A cellular automata ant memory model of foraging in a swarm of robots (submitted to). Applied Mathematical Modelling.
  16. Lima, D. A. and Oliveira, G. M. B. (2016b). New bioinspired coordination strategies for multi-agent systems applied to foraging tasks. IEEE International Conference on Tools with Artificial Intelligence (ICTAI), IEEE Artificial Intelligence Society, San Jose, CA, United States, 6-8th November 2016.
  17. Lima, D. A. and Oliveira, G. M. B. (2016c). A probabilistic cellular automata ant memory model for a swarm of foraging robots. 14th International Conference on Control, Automation, Robotics and Vision (ICARCV 2016), IEEE Control Society, Phuket, Thailand, 13- 15th November 2016.
  18. Lima, D. A., Tinoco, C. R., and Oliveira, G. M. B. (2016). A Cellular Automata Model with Repulsive Pheromone for Swarm Robotics in Surveillance, pages 312-322. Cellular Automata: 12th International Conference on Cellular Automata for Research and Industry, ACRI 2016, Fez, Morocco, September 5-8, 2016. Proceedings, Springer International Publishing, Cham.
  19. Marchese, F. (2011). Mrs motion planning: the spatiotemporal multilayered cellular automata approach. Introduction to Modern Robotics. iConcept Press.
  20. Martinelli, A. (2002). The odometry error of a mobile robot with a synchronous drive system. Robotics and Automation, IEEE Transactions on, 18(3):399-405.
  21. Oliveira, G. M., Vargas, P. A., and Ferreira, G. B. (2015). A local decision making cellular automata-based pathplanning. The European Conference on Artificial Life (ECAL 2015), Procedings, York, United Kingdom, Monday, 20-24th July, 2015, Artifical Life Society .
  22. Santoso, J., Riyanto, B., and Adiprawita, W. (2016). Dynamic path planning for mobile robots with cellular learning automata. Journal of ICT Research and Applications, 10(1):1-14.
  23. Silva, E. C., Soares, J. A. J. P., and Lima, D. A. (2016). One-dimensional chaotic cellular automata with fixed border applied to a cryptosystem modeling for digital images. Revista de Informatica Teorica e Aplicada, 23:250-276.
  24. Varas, A., Cornejo, M., Mainemer, D., Toledo, B., Rogan, J., Munoz, V., and Valdivia, J. (2007). Cellular automaton model for evacuation process with obstacles. Physica A: Statistical Mechanics and its Applications, 382(2):631 - 642.
  25. Vargas, P. A., Benhalen, A. M., Pessin, G., and Osório, F. S. (2012). Applying particle swarm optimization to a garbage and recycling collection problem. In Computational Intelligence (UKCI), 2012 12th UK Workshop on, pages 1-8. IEEE.
  26. Yamamoto, K., Kokubo, S., and Nishinari, K. (2007). Simulation for pedestrian dynamics by real-coded cellular automata (rca). Physica A: Statistical Mechanics and its Applications, 379(2):654-660.
  27. Yang, Z., Zhao, L., Li, J., and Fang, Y. (2005). Simulation of the kin behavior in building occupant evacuation based on cellular automaton. Building and Environment, pages 411 - 415.
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Paper Citation


in Harvard Style

Lima D., Tinoco C., Viedman J. and Oliveira G. (2017). Coordination, Synchronization and Localization Investigations in a Parallel Intelligent Robot Cellular Automata Model that Performs Foraging Task . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 355-363. DOI: 10.5220/0006081403550363


in Bibtex Style

@conference{icaart17,
author={Danielli A. Lima and Claudiney R. Tinoco and Juan M. N. Viedman and Gina M. B. Oliveira},
title={Coordination, Synchronization and Localization Investigations in a Parallel Intelligent Robot Cellular Automata Model that Performs Foraging Task},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={355-363},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006081403550363},
isbn={978-989-758-220-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Coordination, Synchronization and Localization Investigations in a Parallel Intelligent Robot Cellular Automata Model that Performs Foraging Task
SN - 978-989-758-220-2
AU - Lima D.
AU - Tinoco C.
AU - Viedman J.
AU - Oliveira G.
PY - 2017
SP - 355
EP - 363
DO - 10.5220/0006081403550363