Advertisement

Automated Robot Communication System Using Swarm Intelligence

  • Prachi R. RajarapolluEmail author
  • Debashis Adhikari
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)

Abstract

Swarm intelligence is best example of natural phenomenon giving of team work. Here the focus is on use of swarm intelligence to form the communication between automated robots for better and reliable results. With the main aim of exploring the concept for the progressive development of swarm robotics in an engineering field and solve the complex real time applications by setting a communication among automated robots. Currently, Swarm robotics is one of the most important application areas for swarm intelligence. Swarming behaviors of groups of organisms is called as swarm intelligence. There are many advantages of swarm intelligence like, good performance, high reliability, low cost with less complexity. Swarm robotics is the group of robots which are self-assembled and self-communicating to solve such task, which are difficult to solve individually by a single robot. Swarm robots are designed in such a way that they can self-configure and dynamically change their structure to meet environmental conditions. Section 1 will give the detail introduction of research work carried out. While coming consecutive sections will explain literature review, complete implemented system, result and references used.

Keywords

Swarm robotics Swarm intelligence Multi-Robot Communication Server-Client Application Obstacle detection 

References

  1. 1.
    Zhu, Y.F., Tang, X.M.: Overview of swarm intelligence. In: 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), vol. 9, pp. 400–403 (2010)Google Scholar
  2. 2.
    Corondo, E.M., Qin, Y., Nair, S.C., Frye, M.T.: Swarm intelligence for the control of group of robots. In: 10th System of Systems Engineering Conference (SoSE), pp. 205–206 (2015)Google Scholar
  3. 3.
    Robbins, N., McLurkin, J., McMullen, A., Habibi, G.: A robot system design for low-cost multi-robot manipulation. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 912–918, September 2014Google Scholar
  4. 4.
    Kazi, F.S., Patil, D.A., Upadhye, M.Y., Singh, N.M.: Multi robot communication and target tracking system with controller design and implementation of swarm robot using arduino. In: 2015 International Conference on Industrial Instrumentation and Control (ICIC), pp. 412–416, May 2015Google Scholar
  5. 5.
    Samsudin, K., Arvin, F., Ramli, A.R.: A short-range infrared communication for swarm mobile robots. In: 2009 International Conference on Signal Processing Systems, pp. 454–458, May 2009Google Scholar
  6. 6.
    Kottas, A., Drenner, A., Papanikolopoulos, N.: Intelligent power management: Promoting power consciousness in teams of mobile robots. In: 2009 IEEE International Conference on Robotics and Automation, pp. 1140–1145, May 2009Google Scholar
  7. 7.
    Sauze, C., Neal, M.: Long term power management in sailing robots. In: Oceans 2011 IEEE-Spain, pp. 1–8, June 2011Google Scholar
  8. 8.
    Salwani, S., Jain, S., Chandwani, V.K.: Ad-hoc swarm robotics optimization in grid based navigation. In: 2010 11th International Conference Control, Automation, Robotics and Vision, 7-10 December 2010, pp. 1553–1558 (2010)Google Scholar
  9. 9.
    Ming, L.: A novel swarm intelligence optimization inspired by evolution process of a bacterial colony. In: Proceedings of the 10th World Congress on Intelligent Control and Automation, Beijing, China, 6–8 July 2012, pp. 450–453 (2012)Google Scholar
  10. 10.
    Zhu, Y., Lv, Y.: Routing algorithm based on swarm intelligence. In: Proceedings of the 10th World Congress on Intelligent Control and Automation, Beijing, China, 6–8 July 2012, pp. 47–50 (2012)Google Scholar
  11. 11.
    Geng, T., Yongli, L., Guanyan, C., Liyan, D., Sainan, Z.: Ant colony clustering algorithm based on swarm intelligence. In: 2013 6th International Conference on Intelligent Networks and Intelligent Systems, pp. 123–126, May 2013Google Scholar
  12. 12.
    Kumar, M.V., Ravinandan, M.E., Prasad, E.V.: Adaptive path exploration and cognitive map generation using swarm intelligence. In: 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), pp. 318–321 (2016)Google Scholar
  13. 13.
    Mai, S., Steup, C., Mostaghim, S.: Simultaneous localisation and optimisation for swarm robotics. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, pp. 1998–2004 (2018)Google Scholar
  14. 14.
    Bozhinoski, D., Birattari, M.: Designing control software for robot swarms: software engineering for the development of automatic design methods. In: 2018 IEEE/ACM 1st International Workshop on Robotics Software Engineering (RoSE), Gothenburg, Sweden, pp. 33–35 (2018)Google Scholar
  15. 15.
    Hou, L., Fan, F., Fu, J., Wang, J.: Time-varying algorithm for swarm robotics. IEEE/CAA J. Autom. Sinica 5(1), 217–222 (2018)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Electrical EngineeringMIT Academy of EngineeringAlandi, PuneIndia

Personalised recommendations