Soft Computing

, Volume 17, Issue 12, pp 2311–2325 | Cite as

Swarm intelligence and the quest to solve a garbage and recycling collection problem

  • Gustavo Pessin
  • Daniel O. Sales
  • Maurício A. Dias
  • Rafael L. Klaser
  • Denis F. Wolf
  • Jó Ueyama
  • Fernando S. Osório
  • Patrícia A. Vargas
Methodologies and Application

Abstract

This work focuses on the application of Swarm Intelligence to a problem of garbage and recycling collection using a swarm of robots. Computational algorithms inspired by nature, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization, have been successfully applied to a range of optimization problems. Our idea is to train a number of robots to interact with each other, attempting to simulate the way a collective of animals behave, as a single cognitive entity. What we have achieved is a swarm of robots that interacts like a swarm of insects, cooperating with each other accurately and efficiently. We describe two different PSO topologies implemented, showing the obtained results, a comparative evaluation, and an explanation of the rationale behind the choices of topologies that enhanced the PSO algorithm. Moreover, we describe and implement an Ant Colony Optimization (ACO) approach that presents an unusual grid implementation of a robot physical simulation. Hence, generating new concepts and discussions regarding the necessary modifications for the algorithm towards an improved performance. The ACO is then compared to the PSO results in order to choose the best algorithm to solve the proposed problem.

References

  1. Balaguer B, Erinc G, Carpin S (2012) Combining classification and regression for WiFi localization of heterogeneous robot teams in unknown environments. In: 2012 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 3496–3503Google Scholar
  2. Biswas J, Veloso M (2010) WiFi localization and navigation for autonomous indoor mobile robots. In: 2010 IEEE international conference on robotics and automation (ICRA), pp 4379–4384Google Scholar
  3. Bonabeau E, Dorigo M, Theraulaz G (2000) Inspiration for optimization from social insect behaviour. Nature 406Google Scholar
  4. Bongard J (2009) Biologically inspired computing. IEEE Comput 42:95–98CrossRefGoogle Scholar
  5. Clerc M (2006) Particle swarm optimization. Wiley, New YorkGoogle Scholar
  6. Denby B, Le Hégarat-Mascle S (2003) Swarm intelligence in optimisation problems. Nucl Instrum Meth Phys Res Sect A Accel Spectrom Detec Assoc Equip 502:364–368CrossRefGoogle Scholar
  7. Ding Q, Hu X, Sun L, Wang Y (2012) An improved ant colony optimization and its application to vehicle routing problem with time windows. Neurocomputing. 98:101–107CrossRefGoogle Scholar
  8. Dorigo M, Blum C (2005) Ant Colony Optimization theory: a survey. Theor Comput Sci 344:243–278MathSciNetCrossRefMATHGoogle Scholar
  9. Dorigo M, Di Caro G (1999) Ant colony optimization: A new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation (CEC’99)Google Scholar
  10. Dorigo M, Stützle T (2004) Ant Colony Optimization. MIT Press, USAGoogle Scholar
  11. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the micro machine and human science, pp 39–43Google Scholar
  12. Eberhart R, Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 congress on evolutionary computation, pp 81–86Google Scholar
  13. Elbeltagi E, Hegazy T, Grierson D (2005) Comparison among five evolutionary-based optimization algorithms. Adv Eng Info (Elsevier) 19:43–53CrossRefGoogle Scholar
  14. Holland O, Melhuish C (1999) Stigmergy, self-organization, and sorting in collective robotics. Artif Life 5:173–202CrossRefGoogle Scholar
  15. ITU (2012) Propagation data and prediction methods for the planning of indoor radiocommunication systems and radio local area networks in the frequency range 900 MHz to 100 GHz. ITU-R P.1238-7, P-Series, Radiowave propagationGoogle Scholar
  16. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, pp 1942–1948Google Scholar
  17. Kennedy J, Eberhart R (2001) Swarm Intelligence. Morgan Kaufmann, USAGoogle Scholar
  18. Liu B, Wang L, Jin Y, Tang F, Huang D (2005) Improved particle swarm optimization combined with chaos. Chaos. Solitons & Fractals 25:1261–1271CrossRefMATHGoogle Scholar
  19. Michelan R, Von Zuben FJ (2002) Decentralized control system for autonomous navigation based on an evolved artificial immune network. In: Proceedings of the 2002 congress on evolutionary computation, pp 1021–1026Google Scholar
  20. Mohan BC, Baskaran R (2012) A survey: Ant Colony Optimization based recent research and implementation on several engineering domain. Expert Sys Appl 39:4618–4627CrossRefGoogle Scholar
  21. Ozcan E, Mohan CK (1999) Particle swarm optimization: surfing the waves. In: Proceedings of the 1999 congress on evolutionary computation (CEC’99)Google Scholar
  22. Pessin G, Osório F, Souza J, Ueyama J, Costa F, Wolf D, Dimitrova D, Braun T, Vargas P (2013) Investigation on the evolution of an indoor robotic localization system based on wireless networks. Appl Artif Intel (to appear)Google Scholar
  23. Pugh J, Martinoli A (2006) Multi-robot learning with particle swarm optimization. In: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (AAMAS), pp 441–448Google Scholar
  24. Settles M (2005) An Introduction to Particle Swarm Optimization. University of IdahoGoogle Scholar
  25. Stutzle T, Hoos HH (1997) Improvements on the ant system: introducing the MAX-MIN antsystem. In: Proceedings of the international conference on artificial neural networks and genetic algorithms, pp 245–249 Google Scholar
  26. Taylor I (2010) Swarm bots. BBC Magazine, vol 213Google Scholar
  27. Vargas PA , Benhalen A, Pessin G, Osório FS (2012) Applying particle swarm optimization to a garbage and recycling collection problem. 2012 12th UK workshop on computational intelligence (UKCI)Google Scholar
  28. Vargas PA, de Castro LN, Michelan R, Zuben V (2003) Implementation of an immuno-genetic network on a real Khepera II robot. The 2003 congress on evolutionary computation (CEC’03), pp 420–426Google Scholar
  29. Xing GH, Yu SL (2007) Dynamic stage ant colony algorithm and its convergence. Control Decis 22:685–688Google Scholar
  30. Watanabe Y, Ishiguro A, Shirai Y, Uchikawa Y (1998) Emergent construction of behavior arbitration mechanism based on the immune system. In: IEEE international conference on evolutionary computation proceedings, pp 481–486Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gustavo Pessin
    • 1
  • Daniel O. Sales
    • 1
  • Maurício A. Dias
    • 1
  • Rafael L. Klaser
    • 1
  • Denis F. Wolf
    • 1
  • Jó Ueyama
    • 1
  • Fernando S. Osório
    • 1
  • Patrícia A. Vargas
    • 2
  1. 1.Institute of Mathematics and Computer Science (ICMC)University of São Paulo (USP)São CarlosBrazil
  2. 2.School of Mathematical and Computer Sciences (MACS)Heriot-Watt UniversityEdinburghUK

Personalised recommendations