286
Views
3
CrossRef citations to date
0
Altmetric
Original Articles

An improved model for swarm robotics in surveillance based on cellular automata and repulsive pheromone with discrete diffusion

ORCID Icon, ORCID Icon & ORCID Icon
Pages 53-77 | Received 16 Feb 2017, Accepted 19 May 2017, Published online: 08 Jun 2017

References

  • Kıran MS, Gündüz M, Baykan OK. A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimum. Appl Math Comput. 2012;219:1515–1521.
  • Biswas S, Das S, Debchoudhury S, et al. Co-evolving bee colonies by forager migration: A multi-swarm based Artificial Bee Colony algorithm for global search space. Appl Math Comput. 2014;232:216–234.
  • Quang NN, Sanseverino ER, Di Silvestre ML, et al. Optimal power flow based on glow worm-swarm optimization for three-phase islanded microgrids. AEIT Annual Conference-From Research to Industry: The Need for a More Effective Technology Transfer (AEIT). Trieste, Italy; 2014. p. 1–6.
  • Gordon DM. The rewards of restraint in the collective regulation of foraging by harvester ant colonies. London: Nature. 2013;498:91–93.
  • Behring C, Bracho M, Castro M, et al. An algorithm for robot path planning with cellular automata. In: Bandini S, Worsch T, editors. Theory and practical issues on cellular automata. London: Springer; 2001. p. 11–19.
  • Ioannidis K, Sirakoulis GC, Andreadis I. Cellular ants: a method to create collision free trajectories for a cooperative robot team. Rob Auton Syst. 2011;59:113–127.
  • Ferreira GBS, Vargas PA, Oliveira GMB. An improved cellular automata-based model for robot path-planning. In: Mistry M, Leonardis A, Witkowski M, et al., editors. Advances in autonomous robotics systems, TAROS 2014, Lecture notes in computer science. Vol. 8717. Birmingham: Springer; 2014. p. 25–36.
  • Oliveira GMB, Vargas PA, et al. Investigating a cellular automata model that performs three distance diffusion on a robot path planning. Proceedings of the European Conference on Artificial Life 2015. York: MIT Press; 2015. p. 271–278.
  • Lima DA, Tinoco CR, Oliveira GMB. A cellular automata model with repulsive pheromone for swarm robotics in surveillance. In: Cham ZG, editor. Lecture notes in computer science. 31st ed. Vol. 9863, Springer International Publishing; 2016. p. 312–322. DOI: 10.1007/978-3-319-44365-2\_31. El Yacoubi S, Was J, Bandini S, editors. Cellular Automata: Proceedings of the 12th International Conference on Cellular Automata for Research and Industry, ACRI 2016, Fez, Morocco, Sept 5--8, 2016.
  • Lima DA, Oliveira GMB. New bio-inspired coordination strategies for multi-agent systems applied to foraging tasks. 28th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Institute of Electrical and Electronics Engineers (IEEE) Artificial Intelligence Society. Vol. 28. San Jose, CA; 2016.
  • Konstantinos I, Georgios C, Ioannis A. A path planning method based on cellular automata for cooperative robots. Appl Artif Intell. 2011;25:721–745.
  • Lima DA, Oliveira GMB. A cellular automata ant memory model of foraging in a swarm of robots. Appl Math Modell. 2017;47:551–572.
  • Calvo R, Oliveira JR, Figueiredo M, et al. Bio-inspired coordination of multiple robots systems and stigmergy mechanims to cooperative exploration and surveillance tasks. 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS). Qingdao, China; 2011. p. 223–228.
  • Dorigo M, Birattari M, Blum C, et al. Ant colony optimization and swarm intelligence. Proceedings of the 6th International Conference ANTS 2008, Brussels, Sept 22–24, 2008. Vol. 5217. Berlin: Springer; 2008.
  • Vargas PA, Benhalen AM, Pessin G, et al. Applying particle swarm optimization to a garbage and recycling collection problem. 12th UK Workshop on Computational Intelligence (UKCI), 2012. Edinburgh: IEEE; 2012. p. 1–8.
  • Castello E, Yamamoto T, Dalla F, et al. Adaptive foraging for simulated and real robotic swarms: the dynamical response threshold approach. Swarm Intell. 2016;1–31.
  • Kantor G, Singh S, Peterson R, et al. Distributed search and rescue with robot and sensor teams. Field Service Rob. 2003;529–538.
  • Lima DA, Oliveira GMB. A probabilistic cellular automata ant memory model for a swarm of foraging robots. 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), Institute of Electrical and Electronics Engineers (IEEE) Control Systems Society. Vol. 14. Phuket, Thailand; 2016. p. 1–6.
  • Liu Y, Nejat G. Robotic urban search and rescue: A survey from the control perspective. J Intell & Rob Syst. 2013;72:147–165.
  • Saska M, Vakula J, Preucil L. Swarms of micro aerial vehicles stabilized under a visual relative localization. IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, China: IEEE; 2014. p. 3570–3575.
  • Calvo R, Oliveira JR, Romero RA, et al. A bioinspired coordination strategy for controlling of multiple robots in surveillance tasks. Int J Adv Softw. 2012;5(3 &4):146–165.
  • Falleiros ELS, Calvo R, Ishii RP. Pheroslam: a collaborative and bioinspired multi-agent system based on monocular vision, in computational science and its applications-ICCSA. Alberta, Canada: Springer; 2015. p. 71–85.
  • Michel O. Webots: Professional mobile robot simulation. J Adv Rob Syst. 2004;1:39–42.
  • Weile DS, Michielssen E. Genetic algorithm optimization applied to electromagnetics: a review. IEEE Trans Antennas Propag. 1997;45:343–353.
  • Wei-Guo S, Yan-Fei Y, Bing-Hong W, et al. Evacuation behaviors at exit in ca model with force essentials: a comparison with social force model. Physica A. 2006;371:658–666.
  • Mondada F, Bonani M, Raemy X, et al. The e-puck a robot designed for education in engineering. Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions. Vol. 1. Castelo Branco, Portugal; 2009. p. 59–65.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.