Advertisement

Neural Computing and Applications

, Volume 31, Issue 1, pp 263–286 | Cite as

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

  • Nunzia PalmieriEmail author
  • Xin-She Yang
  • Floriano De Rango
  • Salvatore Marano
Original Article

Abstract

Many applications, related to autonomous mobile robots, require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue missions can be fire, mines, human victims, or dangerous material that the robots have to handle. In these scenarios, some cooperation among the robots is required for accomplishing the mission. This paper focuses on the application of different bio-inspired metaheuristics for the coordination of a swarm of mobile robots that have to explore an unknown area in order to rescue and handle cooperatively some distributed targets. This problem is formulated by first defining an optimization model and then considering two sub-problems: exploration and recruiting. Firstly, the environment is incrementally explored by robots using a modified version of ant colony optimization. Then, when a robot detects a target, a recruiting mechanism is carried out to recruit a certain number of robots to deal with the found target together. For this latter purpose, we have proposed and compared three approaches based on three different bio-inspired algorithms (Firefly Algorithm, Particle Swarm Optimization, and Artificial Bee Algorithm). A computational study and extensive simulations have been carried out to assess the behavior of the proposed approaches and to analyze their performance in terms of total energy consumed by the robots to complete the mission. Simulation results indicate that the firefly-based strategy usually provides superior performance and can reduce the wastage of energy, especially in complex scenarios.

Keywords

Multi-robot systems Swarm intelligence Energy consumption Nature-inspired algorithms Metaheuristics 

Notes

Compliance with ethical standards

Conflict of interests

The authors declare that they have no conflict of interest.

References

  1. 1.
    Parker LE (2008) Distributed intelligence: overview of the field and its application in multi-robot system. J Phys Agents 2(1):5–14Google Scholar
  2. 2.
    Mei Y, Lu YH, Hu YC, Lee CSG (2005) A case study of mobile robot’s energy consumption and conservation techniques. In: Proceedings of the 12th international conference on advanced robotics (ICAR ’05). Piscataway, pp 492–497Google Scholar
  3. 3.
    Liu W, Winfield AF, Sa J, Chen J, Dou L (2007) Towards energy optimization: emergent task allocation in a swarm of foraging robots. Adapt Behav 15(3):289–305CrossRefGoogle Scholar
  4. 4.
    Dorigo M, Birattari M, Stützle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39CrossRefGoogle Scholar
  5. 5.
    De Rango F, Palmieri N, Yang XS, Marano S (2015) Bio-inspired exploring and recruiting tasks in a team of distributed robots over mined regions. In: International symposium on performance evaluation of computer and telecommunication system (SPECTS 2015). Chicago, pp 1–8Google Scholar
  6. 6.
    Yang XS (2009) Firefly algorithms for multimodal optimization. In: Watanabe O, Zeugmann T (eds) Proceedings of 5th symposium on stochastic algorithms, foundations and applications, SAGA 2009, pp 169178. Springer, HeidelbergGoogle Scholar
  7. 7.
    Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks. Piscataway, pp 1942–1948CrossRefGoogle Scholar
  8. 8.
    Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132MathSciNetzbMATHGoogle Scholar
  9. 9.
    Senanayake M, Senthoorana I, Barca J, Chung H, Kamruzzamanc J, Murshedc M (2016) Search and tracking algorithms for a swarm of robots: a survey. J Robot Auton Syst 75:422–434CrossRefGoogle Scholar
  10. 10.
    Yan Z, Jouandeau N, Ali Cherif A (2013) A survey and analysis of multi-robot coordination. Int J Adv Robot Syst 10(399):1–18Google Scholar
  11. 11.
    De Rango F, Palmieri N (2012) A swarm-based robot team coordination protocol for mine detection and unknown space discovery. In: 8th international conference on wireless communications and mobile computing (IWCMC 2012). Limassol, pp 703–709Google Scholar
  12. 12.
    Chen X, Kong Y, Fang X, Wu Q (2013) A fast two-stage ACO algorithm for robotic path planning. J Neural Comput Applic 22(2):313–319CrossRefGoogle Scholar
  13. 13.
    Fujisawa R, Dobata S, Kubota D, Imamura H, Matsun F (2008) Dependency by concentration of pheromone trail for multiple robots. In: Proceedings of the 6th international conference on ant colony optimization and swarm intelligence (ANTS 2008). Berlin, pp 283–290CrossRefGoogle Scholar
  14. 14.
    Garnier S, Tache F, Combe M, Grimal A, Theraulaz G (2007) Alice in pheromone land: an experimental setup for the study ofant-like robots. In: Proceedings of the IEEE swarm intelligence symp. (SIS 2007), Washington DC, pp 37–44CrossRefGoogle Scholar
  15. 15.
    Sugawara K, Kazama T, Watanabe T (2004) Foraging behavior of interacting robots with virtual pheromone. In: Proceedings of 2004 IEEE/RSJ international conference on intelligent robots and systems. IEEE Press, Los Alamitos, pp 3074–3079Google Scholar
  16. 16.
    Masár M (2013) A biologically inspired swarm robot coordination algorithm for exploration and surveillance. In: Proceedings of the IEEE 17th internatinal conference on intelligent engineering systems (INES 2013). San Jose, pp 271–275CrossRefGoogle Scholar
  17. 17.
    Ducatelle F, Di Caro GA, Pinciroli C, Gambardella LM (2011) Selforganized cooperation between robotic swarms. Swarm Intell 5(2):73–96CrossRefGoogle Scholar
  18. 18.
    Pugh J, Martinoli A, Zhang AY (2005) Particle swarm optimization for unsupervised robotic learning. In: Proceedings of IEEE swarm intelligence symposium (SIS 2005). Piscataway, pp 92–99Google Scholar
  19. 19.
    Masár M, Zelenka J (2012) Modification of PSO algorithm for the purpose of space exploration. In: Proceedings of IEEE international conference on intelligent engineering systems (INES 2012). Piscataway, pp 223–226Google Scholar
  20. 20.
    Hereford JM, Siebold MA (2010) Bio-inspired search strategies for robot swarms. In: Swarm robotics, from biology to robotics, pp 1–27Google Scholar
  21. 21.
    Couceiro MS, Vargas PA, Rocha RP, Ferreira NMF (2014) Benchmark of swarm robotics distributed techniques in a search task. Robot Auton Syst 62(2):200–213CrossRefGoogle Scholar
  22. 22.
    Kernbach S, Thenius R, Kernbach O, Schmickl T (2009) Reembodiment of honeybee aggregation behavior in artificial micro-robotic system. Adapt Behav 17(3):237–259CrossRefGoogle Scholar
  23. 23.
    Jevtic A, Andina GAD, Jamshidi M (2012) Distributed bees algorithm for task allocation in swarm of robots. IEEE Syst J 6(2):296–304CrossRefGoogle Scholar
  24. 24.
    Contreras-Cruz MA, Ayala-Ramirez V, Hernandez-Belmonte UH (2015) Mobile robot path planning using artificial bee colony and evolutionary programming. Appl Soft Comput 30:319– 328CrossRefGoogle Scholar
  25. 25.
    Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67MathSciNetCrossRefGoogle Scholar
  26. 26.
    Yang B, Ding Y, Jin Y, Hao K (2015) Self-organized swarm robot for target search and trapping inspired by bacterial chemotaxis. Robot Auton Syst 72:83–92CrossRefGoogle Scholar
  27. 27.
    Bayindir L (2016) A review of swarm robotics tasks. Neurocomputing 172:292–321CrossRefGoogle Scholar
  28. 28.
    Mills KL (2007) A brief survey of self-organization in wireless sensor networks. J Wireless Commun Mobile Comput 7(7):823–834CrossRefGoogle Scholar
  29. 29.
    Gu Y, Bozdag D, Brewer R, Ekici E (2006) Data harvesting with mobile elements in wireless sensor networks. Comput Netw 50(17):3449–3465CrossRefzbMATHGoogle Scholar
  30. 30.
    Stirling T, Floreano D (2010) Energy efficient swarm deployment for search in unknown environments. In: Swarm intelligence. Springer, Berlin, pp 562–563. doi: 10.1007/978-3-642-15461-4_61 CrossRefGoogle Scholar
  31. 31.
    Heo N, Varshney PK (2005) Energy-efficient deployment of intelligent mobile sensor networks. IEEE Trans Syst Man Cybern Part A Syst Humans 35(1):78–92CrossRefGoogle Scholar
  32. 32.
    Barca J, Sekercioglu YA (2013) Swarm robotics reviewed. J Robotica 31(3):345–359CrossRefGoogle Scholar
  33. 33.
    Ooi CC, Schindelhauer C (2009) Minimal energy path planning for wireless robots. Mobile Netw Appl 14 (3):309–321CrossRefGoogle Scholar
  34. 34.
    Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspired Comput 2(2):78–84CrossRefGoogle Scholar
  35. 35.
    Yang XS, Deb S, Hanne T, He X (2015) Attraction and diffusion in nature-inspired optimization algorithms. J Neural Comput Applic, 1–8Google Scholar
  36. 36.
    Palmieri N, Marano S (2016) Discrete firefly algorithm for recruiting task in a swarm of robots. In: Yang XS (ed) Nature-inspired computation in engineering. Springer, pp 133–150CrossRefGoogle Scholar
  37. 37.
    Mei Y, Lu YH, Lee CSG, Hu YC (2006) Energy efficient mobile robot exploration. In: The Proceedings of the IEEE international conference of robotics and automation (ICRA 2006). Orlando FL Ma, pp 505–551Google Scholar
  38. 38.
    Clerc C, Kennedy J (2002) The particle swarm-explosion, stability and convergence in a multidimensioanl complex space. IEEE Trans Evol Comput 6(1):58–73CrossRefGoogle Scholar
  39. 39.
    Zhang B, Liu T, Zhang C (2016) Artificial bee colony algorithm with strategy and parameter adaption for artificial bee colony algorithm with strategy and parameter adaption for global optimization. J Neural Comput & Applic, 1–16Google Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Department of Computer Engineering, Modeling, Electronics and Systems ScienceUniversity of CalabriaRendeItaly
  2. 2.School of Design Engineering and MathematicsMiddlesex UniversityLondonUK

Personalised recommendations