Abstract

This article presents a novelimplementation of Particle Swarm Optimisation(PSO)forfinding the most optimal solution to path planning problem for a swarm of robots. The swarm canvasses through the configuration space having static obstaclesby applying PSO on potential fields generated by the target. The best possible path by the momentary leaders of the group is retraced toget the solution. The designed algorithm was simulated on a specially developed simulator adhering to real time constraints and conditions faced by the mobile robots. The solutions for various configuration spaces are presented to verify the effectiveness of the algorithm.

References

• A. Colorni, M. Dorigo et V. Maniezzo, Distributed Optimisation by Ant Colonies, actes de la première conférenceeuropéennesur la vie artificielle, Paris, France, Elsevier Publishing, 134-142, 1991.
• Swagatam Das, ArjitBiswas, SambartaDasgupta, and Ajith Abraham, Bacterial Foraging Optimisation Algorithm: Theoretical Foundations, Analysis and Applications, IEEE Transactions on SMC, Part A, Vol. 39, Is- 3, 670 – 679, 2009.
• Sanjay Sarma O V,Dr. S. M. GiriRajkumar, Dr. K. Ramkumar. Real time application of Ants Colony Optimisation. International Journal of Computer Applications 3(8):1–6, June 2010. Foundation of Computer Science.
• Dr. S. M. GiriRajkumar, Dr K. Ramkumar, BodlaRakesh, Sanjay Sarma O V and Deepak Jayaraj. Real Time Interfacing of a Transducer with a Non-Linear process using Simulated Annealing. Sensors and Transducers. Vol. 121, Issue 10, October 2010, pp. 56-67. Sensors Portal.
• M. Anthony Lewis and George A. Bekey (1992), The Behavioral Self-Organization of Nanorobots Using Local Rules. Proc. of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems, Raleigh, vol. II pp. 1333-1338, 1992.
• M. Dorigo and R. Groß . Cooperative transport of objects of different shapes and sizes. Ant Colony Optimisation and Swarm Intelligence – Proceedings of ANTS 2004 – Fourth International Workshop, volume 3172.
• ErolS¸ahin, Swarm Robotics: From Sources of Inspiration to Domains of Application, KOVAN – Dept. of Computer Eng. , Middle East Technical University, Ankara, 06531, Turkey.
• FengWeiXing, Wang KeJun, Ye XiuFen and GuoShuXiang, Novel Algorithms for Coordination of Underwater Swarm Robotics- Proceedings of the 2006 IEEE – International Conference on Mechatronics and Automation.
• Kennedy, J. ; Eberhart, R. (1995). "Particle Swarm Optimisation". Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942–1948.
• O. Hachour. Path Planning of Autonomous Mobile robot. International Journal of Systems Applications, Engineering & Development, pp. 178-190, Issue 4, Volume 2, 2008.
• P. Raja and S. Pugazhenthi, Path planning for a mobile robot in dynamic environments, International Journal of the Physical Sciences Vol. 6(20), pp. 4721-4731, 23 September, 2011.
• Qiang Zhao and Shaoze Yan,' Collision-Free Path Planning for Mobile Robots Using Chaotic Particle Swarm Optimisation', ADVANCES IN NATURAL COMPUTATION,Lecture Notes in Computer Science, 2005, Volume 3612/2005, 632-635, DOI: 10. 1007/11539902_77.
• Kaiyou Lei; YuhuiQiu;Yi He, A novel path planning for mobile robots using modified particle swarm optimizer'. Systems and Control in Aerospace and Astronautics, 2006.
• SedighehAhmadzadeh and Mehdi Ghanavati, Navigation of mobile robot using the PSO particle swarm optimisation, Journal of Academic and Applied Studies (JAAS), Vol. 2(1) Jan 2012, pp. 32-38.
• Robot Motion Planning, Jean-Claude Latombe, 1991, Kluwer Academic Publishers.
• Othman W. A. F. W. , Amavasai,B. P. , McKibbin, S. P. , Caparrelli, F. and Travis, J. R. An Analysis of Collective Movement Models for Robotic Swarms. In Proceedings of The International Conference on Computer as a Tool (EUROCON 2007), Poland, 2007, pp. 2373-2380