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Multi-Objective Optimization in Gait Planning of Biped Robot Using Genetic Algorithm and Particle Swarm Optimization Algorithm

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Abstract (2. Language): 
This paper deals with multi-objective optimization in gait planning of a 7-dof biped robot ascending and descending some staircases. Both its power consumption as well as dynamic balance margin depends on a few common design parameters. The biped robot should have a maximum dynamic balance margin but at the expense of minimum power. Thus, a conflicting relationship exists between these two objectives. The said gait planning problem has been modeled and solved using two modules of adaptive neuro-fuzzy inference system. The said multi-objective optimization problems have been solved using a genetic algorithm and particle swarm optimization algorithm, separately. Pareto-optimal fronts of solutions have been obtained, which may help a designer to select the most appropriate solution out of several possibilities. Particle swarm optimization algorithm is found to perform better than genetic algorithm, as the former performs both local and global searches simultaneously, whereas the latter is seen to be weak in terms of its local search capability. Therefore, the main contribution of this paper lies with the application of two optimization algorithms to tackle multi-objective optimization in gait planning of biped robot.
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REFERENCES

References: 

[1]
[2]
M. Vukobratovic, A.A. Frank and D. Juricic, “On the stability of biped locomotion,” IEEE Trans. on Biomedical Engineering, 17(1), pp. 25-36, 1970.
S.G.Capi, K. Kaneko, Mitobe, Barolli , Y. Nasu., “Optimal trajectory generation for a prismatic joint biped robot using genetic algorithm,” Robotics and Autonomous Systems,
[3] G. Capi, , Y.Nasu,, L. Barolli, and K. Mitobe., “Real-time gait generation for autonomous humanoid robots: A case study for walking,” Robotics and Autonomous Systems, 42, pp. 107-116, 2003.
38, pp. 119-128, 2002.
[4] K.S. Jeon, O. Kwon, and J.H. Park., “Optimal trajectory generation for a biped robot walking a staircase based on genetic algorithms,” Proc. of IEEE Intl. Conf. on Intelligent Robots and Systems, Sendai, Japan
[5]
, pp. 2837- 2842, 2004.
[6]
A.W. Salatian, and Y.F. Zheng., “Gait synthesis for a biped robot climbing sloping surfaces using neural networks. Part I. Static learning.” Proc. of IEEE Intl. Conf. on Robotics and Automation, Nice, France, pp. 2601-2606, 1992.
A.W. Salatian., and Y.F. Zheng., “Gait synthesis for a biped robot climbing sloping surfaces using neural networks. Part II. Dynamic learning.” Proc. of IEEE Intl. Conf. on Robotics and Automation, Nice, France, pp. 2607-2611, 1992.
Journal of Control Engineering and Technology (JCET)
JCET Vol.1 No.2 October 2011 PP.81-94 www.ijcet.org ○C World Academic Publishing
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[7] S.Fan, M.Sun and M.Shi., “Real-time gait generation for humanoid robot based on fuzzy neural networks,”
[8] A.Kun and W.T.Miller III., “Adaptive fuzzy systems of a biped robot using neural networks,” Proc. of IEEE Intl. Conf. on Robotics and Automation, Minneapolis, Minnesota, USA, pp. 240-245, 1996.
Proc. of the Third Intl. Conf. on Natural Computation Haikou, Hainan, China, 2, pp. 343-348, 2007.
[9] W.T.Miller III., “Real-time neural network control of a biped walking robot,” IEEE Trans. on Control Systems, 14, pp. 41-48, 1994.
[10] C. Zhou and Q. Meng, Q., “Dynamic balance of a biped robot using fuzzy reinforcement learning agents,” Fuzzy Sets and Systems, 134, pp. 189-203, 2003.
[11] R.K Jha, B. Singh and D.K. Pratihar., “Online stable gait generation of a two-legged robot using a genetic-fuzzy system,” Robotics and Autonomous System,
[12]
53, pp. 15-35, 2005.
[13] P.R Vundavilli, S.K.Sahu and D.K.Pratihar., “Online dynamically balanced ascending and descending gait generations of a biped robot using soft computing,” Intl Jl. of Humanoid Robotics, 4(4), pp. 777- 814, 2007.
A.D. Udai., “Optimum hip trajectory generation of a biped robot during single support phase using genetic algorithm,” Proc. of First Intl. Conf. on Emerging Trends in Engineering and Technology, Nagpur, India, pp. 739-744, 2008.
[14] J.Y.Lee and J.J. Lee., “Optimal walking trajectory generation for a biped robot using multi-objective evolutionary algorithm,” Proc. of IEEE Control Conference, Melbourne, Australia,
[15] G.
1, pp. 357- 364, 2004.
Capi, M. Yokota and K. Mitobe., “A new humanoid robot gait generation based on multi-objective optimization,” Proc. of IEEE/ASME Intl. Conf. on Advanced Intelligent Mechatronics. Monterey, California, USA, pp.
[16] D. Goswami, V. Prahlad, and P.D Kien., “Genetic algorithm-based optimal bipedal walking gait synthesis considering trade-off between stability margin and speed,” Robotica, 27, pp. 355–365, 2009.
450- 454, 2005.
[17] J. Kennedy and R. Eberhart, R., “Particle swarm optimization,” Proc. of IEEE Intl. Conf. on Neural Networks, Perth, Australia, pp. 1942–1948, 1995.
[18] M.M. Millonas., Swarms, phase transitions, and collective intelligence. C. G. Langton, (ed.), Artificial Life III. Addison Wesley, Reading, MA, (1994).
[19] C.A.Coello Coello, G.T.Pulido and M.S. Lechuga., “Handling multiple objectives with particle swarm optimization,” IEEE Trans. on Evolutionary Computation, 8(3), pp. 256-279, 2004.
[20] K.Deb, A.Pratap, S.Agarwal and T.Meyarivan., “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. on Evolutionary Computation, 6(2), pp. 182-197, 2002.
[21] M.Reyes-Sierra and C. A.Coello Coello., “Multi-objective particle swarm optimizers: a survey of the state-of-the-art,” Intl. Jl. of Computational Intelligence Research. 2(3), pp. 287– 308, 2006.
[22] R.Poli., “Analysis of the publications on the applications of particle swarm optimization,” Hindawi Publishing Corporation, Jl. of Artificial Evolution and Applications, pp: 1-10, 2008.
[23] K.Sivakumar, C.Balamurugan,S. Ramabalan and S.B.Venkata raman., “Optimal concurrent dimensional and geometrical tolerancing based on evolutionary algorithms,” Proc. of IEEE World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 300-305. 9-11, December, Coimbatore, India, 2009.
[24] N. Rokbani, E.Benbousaada, B.Ammar and M. Alimi Adel., “Biped robot control using particle swarm optimization,” Proc. of Intl. Conf. on Systems Man and Cybernetics (SMC), IEEE, pp. 506-512.,10-13 Oct, 2010, Istanbul, Turkey.
[25] C.Niehaus , T.R¨ofer., T.Laue., “Gait optimization on a humanoid robot using particle swarm optimization,” Proc. of the Second Workshop on Humanoid Soccer Robots, IEEE-RAS, Intl. Conf. on Humanoid Robots, 2007. Pittsburgh, PA, USA
[26] J.Nishii, K.Ogawa and R.Suzuki., “The optimal gait pattern in hexapods based on energetic efficiency,” Proc .of the 3rd Intl. Symp. on Artificial Life and Robotics,, 2007.
[27]
29 oct - 01 Nov, 1998, Hong Kong, pp. 106–109.
[28] D.K.Pratihar, Soft Computing, Narosa Publishing House, New Delhi, India. 2008.
J. R. Jang., “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Trans. on Systems, Man and Cybernetics, Part B, 23(3), pp. 665-685, 1993.
[29] M.Clerc and J.Kennedy., “The particle swarm-explosion, stability and convergence in a multi-dimensional complex space,” IEEE Trans. on Evolutionary Computation, 6, pp. 58-78, 2002.
[30] R.C. Eberhert, R.C., P. Simpson,P., R.Dobbins, R., Computational Intelligence PC tools: Dalian, Academic Press, San Diego, USA. Ch 6, pp. 212-226, 1996.
[31] Y.Shi and R.C. Eberhart., “Empirical study of particle swarm optimization,” Proc. of IEEE Intl. Congr. on Evolutionary Computation, 3, Washington D.C, USA, pp. 101-106, 1999.
[32] C.R.Raquel and P.C.Naval Jr., “An effective use of crowding distance in multi-objective particle swarm optimization,” Proc. of Genetic and Evolutionary Computing Conference, 2005(GECCO ’05), pp. 257-264, June 25-29, 2005, Washington DC, USA.
[33] (2010) http:// www.particleswarm.info/
[34] J.J.Kim, J.W. Lee and J.J.Lee., “Central pattern generator parameter search for a biped walking robot using nonparametric estimation based particle swarm optimization,” Intl. Jl. of Control, Automation and Systems , 7(3), pp. 447-457,2009.

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