Swarm robotics forming a delusional effect

We have all witnessed some kind of patterns that nature makes up in the manifestation whole magical world of ours. Like the fishes forging a rampant attack using their delusional art of creating an image that makes their predator to gallop into the deep water and saving themselves. There are always patterns emerging in front of us, in a way that human’s have a delusional feeling of something not so certain that eludes the nature itself.

Swarm robotics is also somewhat we can call as an approach to the coordination of multiple robots as a system, which consist of large numbers of mostly simple physical robots. The artificial patterns associated with this are the main functional area where all the innovation is involved. Moreover it is supposed to be a desired collective behavior emerging from the interactions between the robots and interactions of robots with their respective environment. This approach emerged on the field of artificial swarm intelligence, as well as the biological studies of insects, ants and other fields in nature, where swarm behavior is mostly associated in a great way.

Miniaturization and cost efficiency are key factors when it comes to swarm robotics. These are various levels of constraints in building large groups of robotics; therefore the simplicity is mainly focused on the individual team member, which is supposed to be emphasized in a perpetual manner. All of these should motivate a swarm-intelligent approach to achieve meaningful behavior at swarm-level, instead of the individual level approach of traditional way of working.

A couple of research has been directed at this goal of simplicity on various individual robotic levels. The ability to use actual hardware in research of Swarm Robotics rather than simulations, allows researchers to encounter with a problematic situation and resolve many more issues and broaden the scope of our basically amazing Swarm Research. Thus, development of simple robots for Swarm intelligence research is a very important aspect of the field, in some profound language, its development is having a bomb of option to be on the face of extinction. This was just my paranoia speaking to me, as we look hopelessly into the void which is supposed to be filled with futuristic appeal.

With so much potential at their disposal to promote robustness, scalability and flexibility, swarm robotics has yet to be adopted for facing the real-world problems, which is why they were ever been considered. Various factors are preventing the real-world uptake of swarm robotics systems and have a go. There is a much needed research to be done on robotic hardware to overcome hardware shortcomings that limit the functionality of current robotic systems. Whereas going in further, research on behavioral control is needed to discover effective ways to let a human operator to interact with a robot swarm. Way more effort is a kind of pre-requisite to provide compelling case-studies that too in particular to demonstrate swarm robotics in outdoor applications. This whole idea of swarm robotics in outdoors is one of those moves, which will surely redefine the future. Not only in outdoors for cleaning the waste, but also to develop business cases and business models that are pretty much capable of showing how and where swarm robotics can be more effective than other approaches.

So the bottom line is that, swarm robotics is as fun as one could possibly imagine and is also able to cater the system with needs. If we keep ourselves sane and work on giving it a stage to showcase what it is capable of. Swarm robotics will surely be a domestic name in this ever-changing future of ours.


  1. Doucet A, Briers M, Sénécal S. Efficient Block Sampling Strategies for Sequential Monte Carlo Methods. J Comput Graph Stat 2006.
  2. Gilks WR, Berzuini C. Following a moving target—Monte Carlo inference for dynamic Bayesian models. J R Stat Soc Series B Stat Methodol 2001.
  3. Montemerlo M, Thrun S, Koller D, et al. FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem. In: Proceedings of the AAAI National Conference on Artificial Intelligence. Edmonton, Canada. 2002.
  4. Montemerlo M, Thrun S. Simultaneous localization and mapping with unknown data association using FastSLAM. In: Proceedings of the IEEE International Conference on Robotics and Automation. Sep 14-19, 2003; Taipei, Taiwan. IEEE, 2003.
  5. Crisan D, Doucet A. A survey of convergence results on particle filtering methods for practitioners. IEEE Transactions on Signal Processing 2002.
  6. Dempster AP, Laird NM, Rubin DB. Maximum Likelihood from Incomplete Data via the EM Algorithm. J R Stat Soc Series B Stat Methodol 1977.
  7. Thrun S, Martin C, Liu Y, et al. A real-time expectation-maximization algorithm for acquiring multiplanar maps of indoor environments with mobile robots. IEEE Transactions on Robotics and Automation 2004.
  8. Le Corff S, Fort G, Moulines E. Online Expectation Maximization algorithm to solve the SLAM problem. Statistical Signal Processing Workshop (SSP). Jun 28-30, 2011; Nice, France. IEEE, 2011.
  9. Hahnel D, Schulz D, Burgard W. Map building with mobile robots in populated environments. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Sep 30-Oct 04, 2002; Lausanne, Switzerland. IEEE, 2002.
  10. Hahnel D, Triebel R, Burgard W. Map building with mobile robots in dynamic environments. In: Proceedings of the IEEE International Conference on Robotics and Automation. Sep 14-19, 2003; Taipei, Taiwan. IEEE, 2003.
  11. Bibby C, Reid I. Simultaneous Localisation and Mapping in Dynamic Environments {(SLAMIDE)} with Reversible Data Association. In: Proceedings of Robotics: Science and Systems Conference. Atlanta, 2007.
  12. Rogers JG, Trevor AJ, Nieto-Granda C, et al. SLAM with Expectation Maximization for moveable object tracking. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Oct 18-22, 2010; Taipei, Taiwan. IEEE, 2010.
  13. Konolige K, Bowman J, Chen JD, et al. View-based maps. Int J Rob Res 2010.
  14. Bosse M, Newman P, Leonard J, et al. An Atlas framework for scalable mapping. In: Proceedings of the IEEE International Conference on Robotics and Automation. Sep 14-19, 2003; Taipei, Taiwan. IEEE, 2003.

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