Abstract

The concept of swarm intelligence is based on the collective social behaviour of decentralized body, either natural or artificial like ant, fish, bird, bee etc. Swarm intelligence has gained very high priority among the researchers from different field like commerce, science and engineering. Multiple editions of swarm intelligence's techniques made it suitable for optimization problems. In this paper, we have present a review of 4 different algorithms based on swarm intelligence for finding the path by mobile robot. Path planning is an interesting problem in mobile robotics. It is about finding the shortest, collision free and smooth path by the robot form predefined starting position to fixed goal position in an environment with obstacles either moving or stationary. This problem is difficult to solve particularly in the case of dynamic environment where the optimal path needs to be rerouted in real time when a new obstacle come out.

References

• Sierakowski, Cezar A. , and Leandro dos S. Coelho. "Study of two swarm intelligence techniques for path planning of mobile robots. " IFAC World Congress 2005.
• Binitha, S. , and S. Siva Sathya. "A Survey of Bio inspired Optimization Algorithms. " International Journal of Soft Computing and Engineering (IJSCE), ISSN (2012): 2231-2307.
• Bhattacharjee, P. ; Rakshit, P. ; Goswami, I. ; Konar, A. ; Nagar, A. K. , "Multi-robot path-planning using artificial bee colony optimization algorithm," Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress on , vol. , no. , pp. 219,224, 19-21 Oct. 2011.
• Saffari, M. H. ; Mahjoob, M. J. , "Bee colony algorithm for real-time optimal path planning of mobile robots," Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009. ICSCCW 2009. Fifth International Conference on , vol. , no. , pp. 1,4, 2-4 Sept. 2009.
• Gengqian Liu; Tiejun Li; Yuqing Peng; Xiangdan Hou, "The Ant Algorithm for Solving Robot Path Planning Problem," Information Technology and Applications, 2005. ICITA 2005. Third International Conference on , vol. 2, no. , pp. 25,27, 4-7 July 2005.
• Khatib, O. , Real-Time Obstacle Avoidance for Manipulators and Mobile Robots, 5, 1986. The International Journal of Robotics Research: p. 90- 98.
• Xin, D. , Hua-Hua C, Wei K. G. , , Neural Network and Genetic Algorithm Based Global Path Planning in A Static Environment. Journal of Zhejiang University Science, 2005. 6A: p. 549-554.
• Zelinsky, A. , Yuta S. A unified approach to planning, sensing and navigation for mobile robots. in 3rd Int. Symposium on Experimental Robotics. 1993. Kyoto, Japan.
• 14. Warren, C. W. , Fast path planning using modified A* Method. IEEE Transactions on Systems, Man and Cybernetics, , 1993.
• Stentz, A. Optimal and Efficient Path Planning for Partially-Known Environments. in IEEE International Conference on Robotics and Automation. 1994.
• Netnevitsky, M. , Artificial Intelligence: A guide to intelligent Systems. 2nd Ed ed. 2002: Addison-Wesley.
• Goss, S. , Aron, S. , Deneubourg, J. L. , and Pasteels, J. M. (1989). Self-organized Shortcuts in the Argentine Ant. Naturwissenchaften Issue 76, pg. 579-581. Springer-Verlag.
• Hwan-il Kang; Byunghee Lee; Kabil Kim, "Path Planning Algorithm Using the Particle Swarm Optimization and the Improved Dijkstra Algorithm," Computational Intelligence and Industrial Application, 2008. PACIIA '08. Pacific-Asia Workshop on , vol. 2, no. , pp. 1002,1004, 19-20 Dec. 2008
• Yarmohamadi, Maryam, H. Haj Seyyed Javadi, and Hossein Erfani. "Improvement of Robot Path Planning Using Particle Swarm Optimization in Dynamic Environments with Mobile Obstacles and Target. " Advanced Studies in Biology 3. 1 (2011): 43-53.
• Nasrollahy, A. Z. ; Javadi, H. , "Using Particle Swarm Optimization for Robot Path Planning in Dynamic Environments with Moving Obstacles and Target," Computer Modeling and Simulation, 2009. EMS '09. Third UKSim European Symposium on , vol. , no. , pp. 60,65, 25-27 Nov. 2009.
• Song-Hiang Chia; Kuo-Lan Su; Jr-Hung Guo; Cheng-Yun Chung, "Ant Colony System Based Mobile Robot Path Planning," Genetic and Evolutionary Computing (ICGEC), 2010 Fourth International Conference on , vol. , no. , pp. 210,213, 13-15 Dec. 2010
• Selvi, V. , and R. Umarani. "Comparative analysis of ant colony and particle swarm optimization techniques. " International Journal of Computer Applications 5. 4 (2010): 1-6.
• Wen, Zhi-qiang, and Zi-xing Cai. "Global path planning approach based on ant colony optimization algorithm. " JOURNAL-CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY-ENGLISH EDITION- 13. 6 (2006): 707.
• Oliveira, IMS de, Roberto Schirru, and J. A. C. C. Medeiros. "On the Performance of an Artificial Bee Colony Optimization Algorithm applied to the accident diagnosis in a PWR Nuclear Power Plant. " International Nuclear Atlantic Conference. 2009.
• Saffari, M. H. ; Mahjoob, M. J. , "Bee colony algorithm for real-time optimal path planning of mobile robots," Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009. ICSCCW 2009. Fifth International Conference on , vol. , no. , pp. 1,4, 2-4 Sept. 2009
• Lin, Jiann-Horng, and Li-Ren Huang. "Chaotic bee swarm optimization algorithm for path planning of mobile robots. " Proceedings of the 10th WSEAS international conference on evolutionary computing. World Scientific and Engineering Academy and Society (WSEAS), 2009.
• Butani, Ravi C. , Bhavin D. Gajjar, and Rajesh A. Thakker. "Performance evaluation of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithm". International Conference on Advanced Computing, Communication and Networks'11.
• Yousif, A. , et al. "Scheduling Jobs on Grid Computing using Firefly Algorithm. " Journal of Theoretical and Applied Information Technology 33. 2 (2011): 155-164.
• Nandy, Sudarshan, Partha Pratim Sarkar, and Achintya Das. "Analysis of a Nature Inspired Firefly Algorithm based Back-propagation Neural Network Training. " arXiv preprint arXiv:1206. 5360 (2012).
• Farahani, Sh M. , et al. "A Gaussian Firefly Algorithm. " International Journal of Machine Learning and Computing 1. 5 (2011): 448-453.
• Liu, Chang, Zhongqiang Gao, and Weihua Zhao. "A New Path Planning Method Based on Firefly Algorithm. " Computational Sciences and Optimization (CSO), 2012 Fifth International Joint Conference on. IEEE, 2012.
• Farahani, S. , et al. "An Improved Firefly Algorithm with Directed Movement. " Proceedings of 4th IEEE International Conference on Computer Science and Information Technology, Chengdu. 2011.
• Nandy, Sudarshan, et al. "Agent based adaptive firefly back-propagation neural network training method for dynamic systems. " Hybrid Intelligent Systems (HIS), 2012 12th International Conference on. IEEE, 2012.
• Wang, Gaige, et al. "A modified firefly algorithm for UCAV path planning. " International Journal of Hybrid Information Technology 5. 3 (2012): 123-144.
• Utkarsh Jaiswal, Shweta Aggarwal. "Ant Colony Optimization". International Journal of Scientific & Engineering Research Volume 2, Issue 7, July-2011 1 ISSN 2229-5518.