Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-26T20:48:46.523Z Has data issue: false hasContentIssue false
  • English
  • Français

Dynamic Strategy Planning of Humanoid Robots Using Glowworm-Based Optimization

Published online by Cambridge University Press:  20 October 2020

Priyadarshi Biplab Kumar Open the ORCID record for Priyadarshi Biplab Kumar [Opens in a new window] ,
Dayal R. Parhi ,
Manoj Kumar Muni Open the ORCID record for Manoj Kumar Muni [Opens in a new window] ,
Krishna Kant Pandey Open the ORCID record for Krishna Kant Pandey [Opens in a new window] ,
Animesh Chhotray Open the ORCID record for Animesh Chhotray [Opens in a new window]  and
Diana Pradhan
Priyadarshi Biplab Kumar*
Affiliation:
Mechanical Engineering Department, National Institute of Technology Hamirpur, Hamirpur-177005, Himachal Pradesh, India
Dayal R. Parhi
Affiliation:
Robotics Laboratory, Department of Mechanical Engineering, National Institute of Technology Rourkela, Rourkela-769008, Odisha, India. E-mails: dayaldoc@yahoo.com, manoj1986nitr@gmail.com, kknitrkl@yahoo.in
Manoj Kumar Muni
Affiliation:
Robotics Laboratory, Department of Mechanical Engineering, National Institute of Technology Rourkela, Rourkela-769008, Odisha, India. E-mails: dayaldoc@yahoo.com, manoj1986nitr@gmail.com, kknitrkl@yahoo.in
Krishna Kant Pandey
Affiliation:
Robotics Laboratory, Department of Mechanical Engineering, National Institute of Technology Rourkela, Rourkela-769008, Odisha, India. E-mails: dayaldoc@yahoo.com, manoj1986nitr@gmail.com, kknitrkl@yahoo.in
Animesh Chhotray
Affiliation:
Department of Mechanical Engineering, Gandhi Institute for Education & Technology, Baniatangi, Bhubaneswar, Khurdha-752060, Odisha, India. E-mail: chhotrayanimesh@gmail.com
Diana Pradhan
Affiliation:
Department of Physics and Astronomy, National Institute of Technology Rourkela, Rourkela-769008, Odisha, India. E-mail: dianapradhan17@gmail.com
*
*Corresponding author. E-mail: p.biplabkumar@gmail.com
Get access Rights & Permissions [Opens in a new window]

Summary

In this paper, a novel dynamic navigational planning strategy is proposed for single as well as multiple humanoids in intricate environments on a glowworm-based optimization method. The sensory information regarding the obstacle distances and target information are supplied as inputs to the navigational model. The essential turning angle is generated as the output of the controller to avoid obstacles present in the environment and reach the target location with ease. The proposed model is certified in a V-REP simulation software, and the simulation results are authenticated in a real-time setup arranged under testing conditions.

Keywords

Glowworm optimizationHumanoidDynamic planningPetri-NetV-REP
Type
Article
Information
Robotica , Volume 39 , Issue 6 , June 2021 , pp. 1051 - 1063
Copyright
© The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Yang, X. S., “Firefly algorithm, stochastic test functions and design optimization,” Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010).CrossRefGoogle Scholar
Krishnanand, K. N., Amruth, P., Guruprasad, M. H., Bidargaddi, S. V and Ghose, D., “Glowworm-Inspired Robot Swarm for Simultaneous Taxis Towards Multiple Radiation Sources,” Proceedings 2006 IEEE International Conference on Robotics and Automation (2006) pp. 958–963.CrossRefGoogle Scholar
Krishnanand, K. N. and Ghose, D., “Detection of Multiple Source Locations Using a Glowworm Metaphor with Applications to Collective Robotics,” Proceedings 2005 IEEE Swarm Intelligence Symposium (SIS 2005) (2005) pp. 84–91.CrossRefGoogle Scholar
Christensen, A. L., OGrady, R. and Dorigo, M., “From fireflies to fault-tolerant swarms of robots,” IEEE Trans. Evol. Comput. 13(4), 754–766 (2009).CrossRefGoogle Scholar
Hidalgo-Paniagua, A., Vega-Rodríguez, M. A., Ferruz, J. and Pavón, N., “Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach,” Soft Comput. 21(4), 949–964 (2017).CrossRefGoogle Scholar
Ahmadi, S. M., Kebriaei, H. and Moradi, H., “Constrained coverage path planning: Evolutionary and classical approaches,” Robotica 36(6), 904–924 (2018).CrossRefGoogle Scholar
Patle, B. K., Parhi, D. R., Jagadeesh, A. and Kashyap, S. K., “On firefly algorithm: Optimization and application in mobile robot navigation,” World J. Eng. 14(1), 65–76 (2017).CrossRefGoogle Scholar
Patle, B. K., Parhi, D., Jagadeesh, A. and Sahu, O. P., “Real time navigation approach for mobile robot,” JCP 12(2), 135–142 (2017).CrossRefGoogle Scholar
Sathyan, A. and Ma, O., “Collaborative control of multiple robots using genetic fuzzy systems,” Robotica 37(11), 1922–1936 (2019).Google Scholar
Brand, M. and Yu, X. H., “Autonomous Robot Path Optimization Using Firefly Algorithm,” In: 2013 IEEE International Conference on Machine Learning and Cybernetics, vol. 3 (2013) pp. 1028–1032.CrossRefGoogle Scholar
Liu, C., Gao, Z. and Zhao, W., “A New Path Planning Method Based on Firefly Algorithm,” In: 2012 Fifth IEEE International Joint Conference on Computational Sciences and Optimization (2012) pp. 775–778.Google Scholar
Zhang, L., Liu, L., Yang, X. S. and Dai, Y., “A novel hybrid firefly algorithm for global optimization,” PloS One 11(9), e0163230 (2016).CrossRefGoogle ScholarPubMed
Liu, C., Zhao, Y., Gao, F. and Liu, L., “Three-dimensional path planning method for autonomous underwater vehicle based on modified firefly algorithm,” Math. Prob. Eng. (2015). doi: https://doi.org/10.1155/2015/561394.Google Scholar
Horng, M. H. and Jiang, T. W., “Multilevel image thresholding selection based on the firefly algorithm,” In: 7th International Conference on Ubiquitous Intelligence & Computing and 7th IEEE International Conference on Autonomic & Trusted Computing (2010) pp. 58–63.Google Scholar
Wang, G., Guo, L., Duan, H., Liu, L. and Wang, H., “A modified firefly algorithm for UCAV path planning,” Int. J. Hybrid Inf. Technol. 5(3), 123–144 (2012).Google Scholar
Fister, I., Fister, I. Jr., Yang, X. S. and Brest, J. A., “A comprehensive review of firefly algorithms,” Swarm Evol. Comput. 13, 34–46 (2013).CrossRefGoogle Scholar
Ariffin, I. M., Rasidi, A. I. H. M., Yussof, H., Mohamed, Z., Miskam, M. A., Amin, A. T. M. and Omar, A. R., “Sensor based mobile navigation using humanoid robot Nao,” Proc. Comput. Sci. 76, 474–479 (2015).CrossRefGoogle Scholar
Wei-guo, W.U. and Yue-yang, H., “Research on Rapid Walking of Biped Robot Based on Parametric Surface Table Cart Model,” In: 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO) (2009) pp. 463–468.Google Scholar
Scianca, N., De Simone, D., Lanari, L. and Oriolo, G., “MPC for humanoid gait generation: Stability and feasibility,” IEEE Trans. Robot. 36(4), 1171–1188 (2020).CrossRefGoogle Scholar
Yussof, H., Yamano, M., Nasu, Y. and Ohka, M., “Design of a 21-DOF Humanoid Robot to Attain Flexibility in Human-Like Motion,” In: ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication (2006) pp. 202–207.CrossRefGoogle Scholar
Pino, O., Palestra, G., Trevino, R. and De Carolis, B., “The humanoid robot NAO as trainer in a memory program for elderly people with mild cognitive impairment,” Int. J. Soc. Robot. 12(1), 21–33 (2020).CrossRefGoogle Scholar
Nava, G., Romano, F., Nori, F. and Pucci, D., “Stability Analysis and Design of Momentum-Based Controllers for Humanoid Robots,” In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2016) pp. 680–687.CrossRefGoogle Scholar
Clever, D. and Mombaur, K. D., “An Inverse Optimal Control Approach for the Transfer of Human Walking Motions in Constrained Environment to Humanoid Robots,” In: Robotics: Science and Systems (2016).Google Scholar
Kumar, P. B. and Parhi, D. R., “Intelligent hybridization of regression technique with genetic algorithm for navigation of humanoids in complex environments,” Robotica 38(4), 565–581 (2020).CrossRefGoogle Scholar
Kumar, P. B., Sahu, C. and Parhi, D. R., “Intelligent navigation of a self-fabricated biped robot using a regression controller,” Sci. Iran. 27(1), 262–272 (2020).Google Scholar
Kumar, P. B., Muni, M. K. and Parhi, D. R., “Navigational analysis of multiple humanoids using a hybrid regression-fuzzy logic control approach in complex terrains,” Appl. Soft Comput. 89 106088 (2020).CrossRefGoogle Scholar
Baskoro, A. S. and Priyono, M. G., “Design of Humanoid Robot Stable Walking Using Inverse Kinematics and Zero Moment Point,” In: 2016 International Electronics Symposium (IES). IEEE (2016) pp. 335–339.Google Scholar
Göngör, F. and Tutsoy, Ö, “Design and implementation of a facial character analysis algorithm for humanoid robots,” Robotica 37(11), 1850–1866 (2019).CrossRefGoogle Scholar
Kofinas, N., Orfanoudakis, E. and Lagoudakis, M. G., “Complete analytical forward and inverse kinematics for the NAO humanoid robot,” J. Intell. Robot. Syst. 77(2), 251–264 (2015).CrossRefGoogle Scholar
Peterson, J. L., Petri Net Theory and the Modeling of Systems (Prentice-Hall, 1981).Google Scholar
Pham, D. T. and Parhi, D. R., “Navigation of multiple mobile robots using a neural network and a Petri Net model,” Robotica 21(1), 79–93 (2003).CrossRefGoogle Scholar
Chen, C. and Richardson, P., “Mobile robot obstacle avoidance using short memory: a dynamic recurrent neuro-fuzzy approach,” Trans. Inst. Meas. Cont. 34(2–3), 148–164 (2012).CrossRefGoogle Scholar