OPPOSITION-BASED LEARNING PARTICLE SWARM OPTIMIZATION OF RUNNING GAIT FOR HUMANOID ROBOT

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

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VOLUME 8 , ISSUE 2 (June 2015) > List of articles

OPPOSITION-BASED LEARNING PARTICLE SWARM OPTIMIZATION OF RUNNING GAIT FOR HUMANOID ROBOT

Liang Yang * / Song Xijia / Chunjian Deng

Keywords : gait planning, Humanoid Robot, Yaw Moment, opposition learning, ZMP.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 2, Pages 1,162-1,179, DOI: https://doi.org/10.21307/ijssis-2017-801

License : (CC BY-NC-ND 4.0)

Received Date : 14-January-2015 / Accepted: 16-April-2015 / Published Online: 01-June-2015

ARTICLE

ABSTRACT

This paper investigates the problem of running gait optimization for humanoid robot. In order to reduce energy consumption and guarantee the dynamic balance including both horizontal and vertical stability, a novel running gait generation based on opposition-based learning particle swarm optimization (PSO) is proposed which aims at high energy efficiency with better stability. In the proposed scheme of running gait generation, a population initiation policy based on domain knowledge is employed, which helps to guide searching direction guidance at the beginning. A population update mechanism based on opposition learning is proposed for speeding up the convergence and improving the diversity. Simulation results validate the proposed method.

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REFERENCES

[1] G. Sen Gupta, S.C.Mukhopadhyay and J. R. French, Wireless Communications and Control Module of a Web-Enabled Robot for Distributed Sensing Applications, Proceedings of IEEE International Instrumentation and Measurement Technology Conference, Victoria, Canada, May 12-15, 2008, pp. 393-398.
[2] Hanazawa Y, Yamakita M., “Limit cycle running based on flat-footed passive dynamic walking with mechanical impedance at ankles”, Advanced Intelligent Mechatronics (AIM), 2012 IEEE/ASME International Conference on. IEEE, 2012: 15-20.
[3] Wisse M, Feliksdal G, Van Frankkenhuyzen J, et al., “Passive-based walking robot, Robotics & Automation Magazine, IEEE, Vol.14, No.2, 2007, pp. 52-62.
[4] Shin H K, Kim B K. “Energy-Efficient Gait Planning and Control for Biped Robots Utilizing the Allowable ZMP Region”, IEEE Transactions on Robotics, Vol. 30, No.4, 2014, pp. 986-993.
[5] Fujimoto Y. et a1, “Trajectory Generation of Biped Running Robot with Minimum Energy Consumption”, Proceedings of the IEEE International Conference on Robotics and Automation, 2004, pp.3803~3808.
[6] Hu L, Zhou C, Sun Z., “Estimating biped gait using spline-based probability distribution function with Q-learning”, Industrial Electronics, IEEE Transactions on, Vol.55, No.3, 2008, pp. 1444-1452.
[7] Liu Z, Wang L, Chen C L P, et al., “Energy-efficiency-based gait control system architecture and algorithm for biped robots”, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, Vol.42, No.6, 2012,pp. 926-933.
[8] A. Dasgupta and Y. Nakamura, Making feasible walking motion of humanoid robots from human motion capture data, in Proc. IEEE ICRA, Detroit, 1999, pp. 1044–1049.
[9] Vukobratovic M, Borovac B. “Zero-moment point—thirty five years of its life”, International Journal of Humanoid Robotics, Vol. 1, No.01, 2004,pp. 157-173.
[10]Vukobratovic M, Borovac B, Potkonjak V. “ZMP: A review of some basic misunderstandings”, International Journal of Humanoid Robotics, Vol.3, No. 02, 2006, pp. 153-175.
[11] Ugurlu B, Saglia J A, Tsagarakis N G, et al. “Yaw moment compensation for bipedal robots via intrinsic angular momentum constraint”, International Journal of Humanoid Robotics, Vol.9, No.04, 2012, 1250033.1-1250033.27
[12] Fu G P, Chen J P, Yang Y M. “A Yaw Moment Counteracting Method for Humanoid Robot Based on Arms Swinging”, Robot, Vol. 34,No.4, 2012, pp. 498-504.
[13] Fu G P. “Research on Gait Planning and Walking Control for Humanoid Robot”, Guangdong University of technology, 2013.
[14] G. Sen Gupta, S.C. Mukhopadhyay and M Finnie, Wi-Fi Based Control of a Robotic Arm with Remote Vision, Proceedings of 2009 IEEE I2MTC Conference, Singapore, May 5-7, 2009, pp. 557-562.
[15] Rahnamayan S, Tizhoosh H R, Salama M M A. “Opposition-based differential evolution”, Evolutionary Computation, IEEE Transactions on, Vol.12, No.1, 2008, pp. 64-79.
[16] Mark Seelye, Gourab Sen Gupta, John Seelye, S. C. Mukhopadhyay, Camera-in-hand Robotic System for Remote Monitoring of Plant Growth in a Laboratory, Proceedings of 2010 IEEE I2MTC Conference, Austin, Texas, USA, May 4-6, 2010, pp. 809-814.
[17] Tan KC, Yang YJ, Goh CK., “A distributed cooperative co-evolutionary algorithm for multiobjective optimization”, IEEE Trans. On Evolutionary Computation, Vol.10, No.5, 2006, pp. 527−549.
[18] I. Emerson, W.L.Xu, S.C.Mukhopadhyay, J.C. Chang and O. Diegol, “Robotic Rehabilitation System for Stroke in Combination with Mirror Therapy: A Research Proposal”, Proceedings of the 25th International Conference of CAD/CAM, Robotics and Factories of the Future, ISBN 978-0-620-46582-3, July 13-16, 2010, Pretoria, South Africa, 12 pages.
[19] Li QH, Yang SD, Ruan YL, “Improving optimization for genetic algorithms based on level set” Journal of Computer Research and Development, Vol.43, No.9, 2006, pp.1624−1629.
[20] Liu Q, Wang X Y, Fu Q M, et al. “Double Elite Co-evolutionary Genetic Algorithm”, Journal of Software, Vol.23, No.4, 2012, pp.765-775.
[21] G. Sen Gupta, S.C.Mukhopadhyay, S. Demidenko and C.H. Messom, “Master-slave Control of a Tele-operated Anthropomorphic Robotic Arm with Gripping Force Sensing”, IEEE Transactions on Instrumentation and Measurement, Vol. 55, No. 6, pp. 2136-2145, December 2006.
[22] Mandal M, Mukhopadhyay A. “A novel PSO-based graph-theoretic approach for identifying most relevant and non-redundant gene markers from gene expression data”, International Journal of Parallel, Emergent and Distributed Systems, 2014 (ahead-of-print), pp. 1-18.

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