Skip to main content

A Hardware Architecture and Physical Prototype for General-Purpose Swarm Minirobotics: Proteus II

  • Chapter
  • First Online:
Applied Optimization and Swarm Intelligence

Abstract

Swarm intelligence is a branch of artificial intelligence focused on the collective behavior of decentralized and self-organized systems composed of relatively simple agents interacting locally with one another and with the environment. It takes its inspiration from the surprising collective behavior of colonies of several social insects (ants, fireflies, bees, wasps) and other groups of animals (fish schooling, animal herding, bird flocking, hawks hunting, and others). These groups exhibit sophisticated behavioral patterns and are able to accomplish collectively very difficult tasks unattainable for single individuals. These ideas can also be applied to the coordinated behavior of a swarm of simple, decentralized self-organized robots, an exciting field known as swarm robotics. In this chapter, we are particularly interested in the case of minirobots, robotic units with characteristic dimensions less than 10 cm (4 inches). An important issue in this regard is the definition of suitable hardware architectures for the minirobotic swarm since this size limitation imposes strong constraints on the different components (electronic, mechanical, etc.) of the minirobot. This chapter describes a hardware solution developed by the authors for a general-purpose robotic prototype particularly tailored for swarm minirobotics: Proteus II, an evolution of the previous Proteus I prototype. The chapter discusses the hardware architecture, its components, the main features and advantages of this new robotic prototype, and its potential applicability to swarm minirobotics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Arkin CR (1998) Behavior-based robotics. MIT Press, Cambridge, Mass, USA

    Google Scholar 

  2. Arvin F, Samsudin K, Ramli AR (2009) Development of a miniature robot for swarm robotic application. Int J Comput Electr Eng 1:436–442

    Article  Google Scholar 

  3. Arvin F, Murray JC, Licheng S, Zhang C, Yue S (2014) Development of an autonomous micro robot for swarm robotics. In: Proceedings of the IEEE international conference on mechatronics and automation—ICMA’2014, pp 635–640

    Google Scholar 

  4. Arvin F, Yue S, Xiong C (2015) Colias-\(\Phi \): an autonomous micro robot for artificial pheromone communication. Int J Mech Eng Robot Res 4(4):349–353

    Google Scholar 

  5. Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, New York

    Google Scholar 

  6. Caprari G, Estier T, Siegwart R (2002) Fascination of down scaling—alice the sugar cube robot. J Micro-Mechatron 1(3):177–189

    Google Scholar 

  7. Caprari G, Siegwart R (2005) Mobile micro-robots ready to use: Alice. In: Proceedings of the IEEE IRS/RSJ international conference on intelligent robots and systems—IROS 2005. Edmonton, Canada, pp 3845–3850

    Google Scholar 

  8. Dorigo M (1992) Optimization, learning and natural algorithms. Ph.D. thesis, Dipartimento di Elettronica, Politecnico di Milano, Milan, Italy

    Google Scholar 

  9. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evolut Comput 1(1):53–66

    Article  Google Scholar 

  10. Engelbretch AP (2005) Fundamentals of computational swarm intelligence. Wiley, Chichester, England

    Google Scholar 

  11. Faigl J, Krajnik T, Chudoba J, Preucil L, Saska M (2013) Low-cost embedded system for relative localization in robotic swarms. In: Proceedings of the IEEE international conference on robotics and automation—ICRA’2013, pp 993–998

    Google Scholar 

  12. Iglesias A, Gálvez A, Suárez P (2020) Swarm robotics—a case study: bat robotics. In: Yang XS (ed) Nature-inspired computation and swarm intelligence, Algorithms, Theory and Applications. Academic Press, Elsevier, pp 273–302

    Google Scholar 

  13. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    Article  MathSciNet  Google Scholar 

  14. Kernbach S, Thenius R, Kernbach O, Schmickl T (2009) Re-embodiment of honeybee aggregation behavior in an artificial micro-robotic system. Adapt Behav 17(3):237–259

    Article  Google Scholar 

  15. Kennedy J, Eberhart RC (1995) Particle swarm optimization. IEEE Int Conf Neural Netw (Perth, Australia) 1:942–1948

    Google Scholar 

  16. Kennedy J, Eberhart RC, Shi Y (2001) Swarm intelligence. Morgan Kaufmann Publishers, San Francisco, CA

    Google Scholar 

  17. Kicad software web site. www.kicad.org

  18. JMcLurkin J (2004) Stupid robot tricks: a behavior-based distributed algorithm library for programming swarms of robots. M.S. thesis, Massachusetts Institute of Technology

    Google Scholar 

  19. McLurkin J, Smith J (2007) Distributed algorithms for dispersion in indoor environments using a swarm of autonomous mobile robots. In: Distributed autonomous robotic systems, vol 6. Springer, pp 399–408

    Google Scholar 

  20. McLurkin J, Lynch A, Rixner S, Barr T, Chou A, Foster K, Bilstein S (2013) A low-cost multi-robot system for research, teaching, and outreach. In: Distributed autonomous robotic systems, vol 83. Springer, pp 597–609

    Google Scholar 

  21. McLurkin J, Smith J, Frankel J, Sotkowitz D, Blau D, Schmidt B (2006) Swarmish: human-robot interface design for large swarms of autonomous mobile robots. In: AAAI spring symposium, pp 72–75

    Google Scholar 

  22. Mondada F, Franzi E, Guignard A (1999) The development of Khepera. In: Proceedings of the 1st international Khepera workshop, vol 64. HNI-Verlagsschriftenreihe, Heinz Nixdorf Institut, pp 7–14

    Google Scholar 

  23. Mondada F, Gambardella LM, Floreano D, Nolfi S, Deneubourg J-L, Dorigo M (2005) The cooperation of swarm-bots: physical interactions in collective robotics. IEEE Robot Autom Mag 12(2):21–28

    Article  Google Scholar 

  24. Mondada F, Bonani M, Raemy X, Pugh J, Cianci C, Klaptocz A, Magnenat S, Zufferey JC, Floreano D, Martinoli A (2009) The e-puck, a robot designed for education in engineering. In: Proceedings of the 9th conference on autonomous robot systems and competitions, vol 1, no 1, pp 59–65

    Google Scholar 

  25. Moustafa N, Iglesias A, Gálvez A (2018) A general-purpose hardware robotic platform for swarm robotics. Stud Comput Intell 798:259–271

    Google Scholar 

  26. Moustafa N, Iglesias A, Gálvez A (2019) Proteus-I: a flexible and adaptable low-cost general-purpose micro-robot prototype for swarm robotics. In: Proceedings of the 13th international conference on software, knowledge, information management and applications, SKIMA’19. IEEE Computer Society Press, pp 1–8

    Google Scholar 

  27. Navarro I, Matía F (2013) An introduction to swarm robotics. ISRN Robot 2013, Article ID 608164, 10

    Google Scholar 

  28. Osaba E, Yang XS (2021) Applied optimization and swarm intelligence: a systematic review and prospect opportunities. In: This book

    Google Scholar 

  29. Pugh J, Raemy X, Favre C, Falconi R, Martinoli A (2009) A fast onboard relative positioning module for multirobot systems. IEEE/ASME Trans Mechatron 14(2):151–162

    Article  Google Scholar 

  30. Rubenstein M, Ahler C, Nagpal R (2012) Kilobot: a low cost scalable robot system for collective behaviors. In: IEEE international conference on robotics and automation—ICRA’2012, pp 3293–3298

    Google Scholar 

  31. Rubenstein M, Cornejo A, Nagpal N (2014) Programmable self-assembly in a thousand-robot swarm. Science 345(6198):795–799

    Article  Google Scholar 

  32. Gauci M, Ortiz ME, Rubenstein M, Nagpa Nl (2017) Error cascades in collective behavior: a case study of the gradient algorithm on 1000 physical agents. In: International conference on autonomous agents and multi-agent systems—AAMAS 2017, pp 1404–1412

    Google Scholar 

  33. Sahin E (2005) Swarm robotics: from sources of inspiration to domains of application. In: Swarm robotics. Lecture notes in computer science, vol 3342, pp 10–20

    Google Scholar 

  34. Sauter JA, Matthews R, Parunak HVD, Brueckner SA (2007) Effectiveness of digital pheromones controlling swarming vehicles in military scenarios. J Aerosp Comput Inf Commun 4(5):753–769

    Article  Google Scholar 

  35. Saska M, Vonasek V, Krajnik T, Preucil L (2014) Coordination and navigation of heterogeneous MAVUGV formations localized by a hawk-eye-like approach under a model predictive control scheme. Int J Robot Res 33(10):1393–1412

    Google Scholar 

  36. Schmickl T, Thenius R, Moeslinger C, Radspieler G, Kernbach S, Szymanski M, Crailsheim K (2009) Get in touch: cooperative decision making based on robot-to-robot collisions. Auton Agents Multi-Agent Syst 18(1):133–155

    Article  Google Scholar 

  37. Schranz M, Umlauft M, Sende M, Elmenreich W (2020) Swarm robotic behaviors and current applications. Front Robot AI 7, Article 36

    Google Scholar 

  38. Seyfried J, Szymanski M, Bender N, Estana R, Thiel M, Wörn H (2005) The i-swarm project: intelligent small world autonomous robots for micro-manipulation. In: Swarm robotics. Lecture notes in computer science, vol 3342, pp 70–83

    Google Scholar 

  39. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  Google Scholar 

  40. Suárez P, Gálvez A, Fister I, Osaba E, Del Ser J, Iglesias A (2018) Bat algorithm swarm robotics approach for dual non-cooperative search with self-centered mode. Lect Notes Comput Sci 11315:201–209

    Article  Google Scholar 

  41. Suárez P, Gálvez A, Fister I, Osaba E, Del Ser J, Iglesias A (2018) Interplay of two bat algorithm robotic swarms in non-cooperative target point search. Commun Comput Inf Sci 887:543–550

    Google Scholar 

  42. Suárez P, Gálvez A, Iglesias A (2017) Autonomous coordinated navigation of virtual swarm bots in dynamic indoor environments by bat algorithm. Lect Notes Comput Sci 10386:176–184

    Article  Google Scholar 

  43. Suárez P, Iglesias A (2017) Bat algorithm for coordinated exploration in swarm robotics. Adv Intell Syst Comput 514:134–144

    Google Scholar 

  44. Suárez P, Iglesias A, Gálvez A (2019) Make robots be bats: specializing robotic swarms to the bat algorithm. Swarm Evolut Comput 44(1):113–129

    Article  Google Scholar 

  45. Tan Y, Zheng ZY (2013) Research advance in swarm robotics. Def Technol J 9(1):18–39

    Article  Google Scholar 

  46. Turgut AE, Celikkanat H, Gokce F, Sahin E (2008) Self-organized flocking with a mobile robot swarm. In: International conference on autonomous agents and multiagent systems—AAMAS 2008, pp 39–46

    Google Scholar 

  47. Turgut AE, Celikkanat H, Gokce F, Sahin E (2008) Self-organized flocking in mobile robot swarms. Swarm Intell 2(2):97–120

    Article  Google Scholar 

  48. Valdastri P, Corradi P, Menciassi A, Schmickl T, Crailsheim K, Seyfried J, Dario P (2006) Micromanipulation, communication and swarm intelligence issues in a swarm microrobotic platform. Robot Autonom Syst 54(10):789–804

    Article  Google Scholar 

  49. Vasarhelyi G, Virgh C, Tarcai N, Somorjai G, Vicsek T (2014) Outdoor flocking and formation flight with autonomous aerial robots. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems—IROS 2014, pp 3866–3873

    Google Scholar 

  50. Wagner I, Bruckstein A (2011) Special issue on ant robotics. Ann Math Artif Intell 31(1–4)

    Google Scholar 

  51. Yang XS (2009) Firefly algorithms for multimodal optimization. Lect Notes Comput Sci 5792:169–178

    Article  MathSciNet  Google Scholar 

  52. Yang X-S (2010) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press, Frome, UK

    Google Scholar 

  53. Yang XS (2010) A new metaheuristic bat-inspired algorithm. Stud Comput Intell 284:65–74

    MATH  Google Scholar 

  54. Yang XS (2011) Bat algorithm for multiobjective optimization. Int J Bio-Inspir Comput 3(5):267–274

    Article  Google Scholar 

  55. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Proceedings of the world congress on nature & biologically inspired computing (NaBIC). IEEE Press, New York, pp 210–214

    Google Scholar 

  56. Zahugi EMH, Shabani AM, Prasad TV (2012) Libot: design of a low cost mobile robot for outdoor swarm robotics. In: Proceedings of IEEE international conference on cyber technology in automation, control, and intelligent systems—CYBER’2012, pp 342–347

    Google Scholar 

Download references

Acknowledgements

This research work has received funding from the project PDE-GIR of the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 778035, the Spanish Ministry of Economy and Competitiveness (Computer Science National Program) under grant #TIN2017-89275-R of the Agencia Estatal de Investigación and European Funds FEDER (AEI/FEDER, UE), and the project #JU12, supported by public body SODERCAN and European Funds FEDER (SODERCAN/FEDER UE). We are also thankful to the authors of [28] for providing us with a preliminary version of their chapter in this book during the writing of this one.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrés Iglesias .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Moustafa, N., Iglesias, A., Gálvez, A. (2021). A Hardware Architecture and Physical Prototype for General-Purpose Swarm Minirobotics: Proteus II. In: Osaba, E., Yang, XS. (eds) Applied Optimization and Swarm Intelligence. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-0662-5_8

Download citation

Publish with us

Policies and ethics