Towards Hybridization of Nature Inspired Metaheuristic Techniques for Collision Free Motion Planning

Abstract
Authors
Keywords
Conclusion
References

Various heuristic and metaheuristic algorithms were proposed in early 70s. Metaheuristic algorithms are very simple, flexible, derivation free mechanism and are superior to others conventional optimization techniques. They make use of domain specific knowledge and is controlled by upper level strategy, they are approximate and usually non deterministic. This paper proposes a newly developed hybrid metaheuristic algorithm (MACOCS) inspired by the behavior of ants and cuckoo birds. Path planning is very sensitive issue of mobile robot movement, here developed MACOCS technique is applied on it to find out collision free optimized path in environment having static and dynamic obstacles both. The results show that MACOCS provides competent results as compared to modified ACO and Cuckoo Search algorithms. It also shows that the proposed algorithm can also be successfully applied to any challenging problems with unknown search space.

Published In : IJCSN Journal Volume 7, Issue 3

Date of Publication : June 2018

Pages : 192-198

Figures :05

Tables : 04

Dr. Sapna Katiyar : Professor, ABES Institute of Technology, Ghaziabad.

Cuckoo Search (CS), Hybrid MACOCS, Modified ACO, Metaheuristic, Optimization, Path Planning

In the proposed work motion planning of mobile robot is considered as an optimization problem. All three algorithms perform randomly search to find out feasible route by avoiding all hindrance coming in between the route without taking large computational time. It employs ACO as global path planning technique wile CS as local path planning.

1. Sapna Katiyar, Avneesh Mittal, A. Q. Ansari, T. K. Saxena, “Ant Colony Algorithm Based Adaptive PID Temperature Controller,” Proc. 7th Int. Conf. on Trends in Industrial Measurements and Automation (TIMA 2011), CSIR, Chennai, January 2011. 2. O. Hachour, “Path planning of autonomous mobile robot”, International Journal Syst. Appl. Eng., vol.4, pp. 178-190, 2008 3. Masehian, E. & Katebi, “Robot Motion planning in dynamic environment with mobile obstacles and target”, International Journal of Mech. System Science Engineering, pp. 20-25, 2007. 4. Zhiye Li, Xiong Chen and Wendong Xiao, “A New Motion Planning Approach based on Artificial Potential Field in Unknown Environment”, PDCAT, LNCS 3320, pp. 376- 382, 2004. 5. Na Lv, Zuren F., “Numerical Potential Field and Ant Colony Optimization Based Path Planning in Dynamic Environment”, IEEE WCI CA, pp. 8966-8970, 2006. 6. Zheng TG, Huan, H. & Aaron S, “ Ant colony System Algorithm for Real Time Globally optimal path planning of mobile robots”, Acta Automatica Sinica, vol. 33, pp. 279-285, 2007. 7. Dorigo. M. and Stutzle. T., “Ant Colony Optimization”, MIT, 2004. 8. M. Dorigo and L.M. Gambardella, “Ant Colony System: a cooperative learning approach to the travelling salesman problem”, IEEE Transaction on Evolutionary Computation, vol. 1, no. 1, pp. 53-66, 1997. 9. Ramin Rajabioun, “Cuckoo Optimization Algorithm”, Applied Soft Computing, Elsevier, pp. 5508-5518, 2011. 10. Aziz Quaarab, Belaid Ahiod and Xin She Yang, “Discrete Cuckoo Search Algorithm for Travelling Salesman Problem”, Springer Verlag London, vol. 24, pp. 1659-1669, 2014. 11. S. Deb, Xin She Yang, “Cuckoo Search via Levy Flights”, World Congress on Nature & Biologically Inspired Computing, IEEE publications, pp. 210-214, 2009. 12. Abdesslem Layeb, “A Novel Quantum Inspired cuckoo search for knapsack problems”, International Journal of Bio-Inspired Computation, vol. 3(5), pp. 297-305, 2011. 13. Momin Jamil and Hans Jurgen Zepernick, “Multimodal Functional Optimization with Cuckoo Search Algorithm”, International Journal of Bio-Inspired Computation, vol. 5(2), pp. 73- 83, 2013. 14. Mukul Joshi, Praveen Ranjan Srivastava, “Query Optimization: An Intelligent Hybrid approach using Cuckoo and Tabu search”, International Journal of Intelligent Information Technologies (IJIIT), vol. 9(1), pp. 40-55, 2013. 15. A. Q. Ansari, Ibraheem, Sapna Katiyar, “Comparison and analysis of Obstacle Avoiding Path Planning of mobile robot by using ACO and TLBO”, International Conference on Information and Communication Technology for Intelligent Systems, Springer (Smart Innovation, Systems and Technologies), vol. 51, pp. 563-574, 2015. 16. A. Q. Ansari, Ibraheem, Sapna Katiyar, “Comparison and analysis of solving travelling salesman problem using GA, ACO and hybrid of ACO with GA and CS”, 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI), pp. 1- 5, 2015.