Hostname: page-component-8448b6f56d-c4f8m Total loading time: 0 Render date: 2024-04-17T07:35:04.079Z Has data issue: false hasContentIssue false
  • English
  • Français

Improved Motion Planning of Humanoid Robots Using Bacterial Foraging Optimization

Published online by Cambridge University Press:  07 May 2020

Manoj Kumar Muni Open the ORCID record for Manoj Kumar Muni [Opens in a new window] ,
Dayal R. Parhi  and
Priyadarshi Biplab Kumar Open the ORCID record for Priyadarshi Biplab Kumar [Opens in a new window]
Manoj Kumar Muni*
Affiliation:
Robotics Laboratory, Mechanical Engineering Department, National Institute of Technology Rourkela, Rourkela769008, Odisha, India. E-mails: manoj1986nitr@gmail.com, dayaldoc@yahoo.com
Dayal R. Parhi
Affiliation:
Robotics Laboratory, Mechanical Engineering Department, National Institute of Technology Rourkela, Rourkela769008, Odisha, India. E-mails: manoj1986nitr@gmail.com, dayaldoc@yahoo.com
Priyadarshi Biplab Kumar
Affiliation:
Mechanical Engineering Department, National Institute of Technology Hamirpur, Hamirpur177005, Himachal Pradesh, India. E-mail: p.biplabkumar@gmail.com
*
*Corresponding author. E-mail: manoj1986nitr@gmail.com
Get access Rights & Permissions [Opens in a new window]

Summary

This paper emphasizes on Bacterial Foraging Optimization Algorithm for effective and efficient navigation of humanoid NAO, which uses the foraging quality of bacteria Escherichia coli for getting shortest path between two locations in minimum time. The Gaussian cost function assigned to both attractant and repellent profile of bacterium performs a major role in obtaining the best path between any two locations. Mathematical formulations have been performed to design the control architecture for humanoid navigation using the proposed methodology. The developed approach has been tested in a simulation platform, and the simulation results have been validated in an experimental platform. Here, motion planning for both single and multiple humanoid robots on a common platform has been performed by integrating a petri-net architecture for multiple humanoid navigation. Finally, the results obtained from both the platforms are compared in terms of suitable navigational parameters, and proper agreements have been observed with minimal amount of error limits.

Keywords

Humanoid NAOBFOAPath planningPetri-net
Type
Articles
Information
Robotica , Volume 39 , Issue 1 , January 2021 , pp. 123 - 136
Copyright
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

Robotics, A., “Nao Documentation” v1. 14.2.Google Scholar
Rath, A. K., Parhi, D. R., Das, H. C., Muni, M. K. and Kumar, P. B., “Analysis and use of fuzzy intelligent technique for navigation of humanoid robot in obstacle prone zone,” Defence Technol. 14(6), 677–682 (2018).CrossRefGoogle Scholar
Rath, A. K., Parhi, D. R., Das, H. C., Kumar, P. B., Muni, M. K. and Salony, K., “Path optimization for navigation of a humanoid robot using hybridized fuzzy-genetic algorithm,” Int. J. Intell. Unmanned Syst. 7(3), 112–119 (2019).CrossRefGoogle Scholar
Patle, B. K., Pandey, A., Jagadeesh, A. and Parhi, D. R., “Path planning in uncertain environment by using firefly algorithm,” Defence Technol. 14(6), 691–701 (2018).CrossRefGoogle Scholar
Rath, A. K., Das, H. C., Parhi, D. R. and Kumar, P. B., “Application of artificial neural network for control and navigation of humanoid robot,” J. Mech. Eng. Sci. 12(2), 3529–3538 (2018).CrossRefGoogle Scholar
Rath, A. K., Parhi, D. R., Das, H. C. and Kumar, P. B., “Behaviour based navigational control of humanoid robot using genetic algorithm technique in cluttered environment,” Measur. Control A 91(1), 32–36 (2018).CrossRefGoogle Scholar
Dewang, H. S., Mohanty, P. K. and Kundu, S., “A robust path planning for mobile robot using smart particle swarm optimization,” Procedia Comput. Sci. 133, 290–297 (2018).CrossRefGoogle Scholar
Kumar, P. B., Sahu, C. and Parhi, D. R., “A hybridized regression-adaptive ant colony optimization approach for navigation of humanoids in a cluttered environment,” Appl. Soft Comput. 68, 565–585 (2018).CrossRefGoogle Scholar
Kumar, P. B., Mohapatra, S. and Parhi, D. R., “An intelligent navigation of humanoid NAO in the light of classical approach and computational intelligence,” Comput. Animat. Virtual Worlds 30(2), e1858.CrossRefGoogle Scholar
Kumar, P. B., Verma, N. K., Parhi, D. R. and Priyadarshi, D., “Design and control of a 7 DOF redundant manipulator arm,” Australian J. Mech. Eng., 1–12 (2019). doi:10.1080/14484846.2019.1656354.CrossRefGoogle Scholar
Kumar, P. B., Sethy, M. and Parhi, D. R., “An intelligent computer vision integrated regression based navigation approach for humanoids in a cluttered environment,” Multimedia Tools Appl. 78(9), 11463–11486 (2019).CrossRefGoogle Scholar
Parhi, D. R., Sahu, C. and Kumar, P. B., “Navigation of multiple humanoid robots using hybrid adaptive swarm-adaptive ant colony optimisation technique,” Comput. Anim. Virtual Worlds 29(2), e1802.CrossRefGoogle Scholar
Paolillo, A., Faragasso, A., Oriolo, G. and Vendittelli, M., “Vision-Based maze navigation for humanoid robots,” Autonomous Robots 41(2), 293–309 (2017).CrossRefGoogle Scholar
Abbas, N. H. and Ali, F. M., “Path planning of an autonomous mobile robot using enhanced bacterial foraging optimization algorithm,” Al-Khwarizmi Eng. J. 12(4), 26–35 (2016).CrossRefGoogle Scholar
Fakoor, M., Kosari, A. and Jafarzadeh, M., “Humanoid robot path planning with fuzzy Markov decision processes,” J. Appl. Res. Technol. 14(5), 300–310 (2016).CrossRefGoogle Scholar
Pandey, A. and Parhi, D. R., “MATLAB simulation for mobile robot navigation with hurdles in cluttered environment using minimum rule based fuzzy logic controller,” Procedia Technol. 14, 28–34 (2014).CrossRefGoogle Scholar
Parhi, D. R. and Kumar, P. B., “Smart navigation of humanoid robots using DAYKUN-BIP virtual target displacement and petri-net strategy,” Robotica 37(4), 626–640 (2019).CrossRefGoogle Scholar
Hornung, A., S. Oßwald, D. Maier and M. Bennewitz, “Monte Carlo localization for humanoid robot navigation in complex indoor environments,” Int. J. Humanoid Robotics 11(02), 1441002 (2014).CrossRefGoogle Scholar
Hossain, M. A. and Ferdous, I., “Autonomous robot path planning in dynamic environment using a new optimization technique inspired by bacterial foraging technique,” Robotics Auton. Syst. 64, 137–141 (2015).CrossRefGoogle Scholar
Faragasso, A., Oriolo, G., Paolillo, A. and Vendittelli, M., “Vision-Based Corridor Navigation for Humanoid Robots,” 2013 IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany (2013) pp. 3190–3195.Google Scholar
Mohareri, O. and Rad, A. B., “Autonomous Humanoid Robot Navigation Using Augmented Reality Technique,” Proceedings of the 2011 IEEE International Conference on Mechatronics, Istanbul, Turkey (2011) pp. 463–468.Google Scholar
Feng, X., He, Y., Yang, H. and Juan, Y., “Self-Adaptive bacterial foraging optimization algorithm based on evolution strategies” Rev. Téc. Ing. Univ. Zulia 39(8), 350–358 (2016).Google Scholar
Zhao, Y., Pei, J. and Chen, H., “Multi-Layer radial basis function neural network based on multi-scale kernel learning,” Appl. Soft Comput. 82, 105541 (2019).CrossRefGoogle Scholar
Khatoon, S. and Ibraheem, I., “Autonomous mobile robot navigation by combining local and global techniques” Int. J. Comput. Appl. 37(3), 1–10 (2012).Google Scholar
Nurmaini, S. and Tutuko, B., “A new classification technique in mobile robot navigation” TELKOMNIKA 9(3), 453–464 (2013).CrossRefGoogle Scholar
Khriji, L., Touati, F., Benhmed, K. and Al-Yahmedi, A., “Mobile robot navigation based on Q-learning technique” Int. J. Adv. Robotic Syst. 8(1), 4 (2011).CrossRefGoogle Scholar
Parasuraman, S., Shirinzadeh, B. and Ganapathy, V., “Sensors fusion technique for mobile robot navigation using fuzzy logic control system” Mobile Robots Navig., 85–106 (2010).Google Scholar
Gueaieb, W. and Miah, M. S., “An intelligent mobile robot navigation technique using RFID technology” IEEE Tran. Inst. Meas 57(9), 1908–1917 (2008).CrossRefGoogle Scholar
Abiyev, R. H., Erin, B. and Denker, A., “Navigation of Mobile Robot Using Type-2 Fuzzy System” International Conference on Intelligent Computing (ICIC), Liverpool, United Kingdom (2017) pp. 15–26.Google Scholar
Khairunizam, W. A. N., Nor, R. M., Ayob, M. N., Hazry, D., Shahriman, A. B. and Zuradzman, M., “Mobile robot navigation by using fuzzy information of moving two-wheeled motion” Int. J. Mech. Mechatron. Eng. IJMME-IJENS 13(4), 34–39 (2013).Google Scholar
Kowalczyk, W., “Rapid navigation function control for two-wheeled mobile robots” J. Intell. Robotic Syst. 93(3–4), 1–11 (2018).Google Scholar
Shayestegan, M. and Marhaban, M. H., “A braitenberg approach to mobile robot navigation in unknown environments” International Conference on Intelligent Robotics, Automation, and Manufacturing (IRAM), Kuala Lumpur, Malaysia (2012) pp. 75–93.Google Scholar
Parhi, D. R. and Chhotray, A., “Development and analysis of DAYANI arc contour intelligent technique for navigation of two-wheeled mobile robot” Ind. Robot Int. J. 45(5), 688–702 (2018).CrossRefGoogle Scholar
Juang, C. F., Lai, M. G. and Zeng, W. T., “Evolutionary fuzzy control and navigation for two wheeled robots cooperatively carrying an object in unknown environments” IEEE Trans. Cybe. 45(9), 1731–1743 (2015).CrossRefGoogle ScholarPubMed
Chou, C. Y. and Juang, C. F., “Navigation of an autonomous wheeled robot in unknown environments based on evolutionary fuzzy control” Inventions 3(1), 3 (2018).CrossRefGoogle Scholar
Armah, S., Yi, S. and Abu-Lebdeh, T., “Implementation of autonomous navigation algorithms on two wheeled ground mobile robot” Am. J. Eng. Appl. Sci. 7(1), 149–164 (2014).CrossRefGoogle Scholar
Zolghadr, J. and Cai, Y., “Locating a two-wheeled robot using extended Kalman filter” Tehnièki vjesnik 22(6), 1481–1488 (2015).Google Scholar
Gutmann, J. S., Fukuchi, M. and Fujita, M., “Real-Time Path Planning for Humanoid Robot Navigation” Nineteenth International Joint Conference on Artificial Intelligence (IJCAI), Edinburgh, Scotland (2005) pp. 1232–1237.Google Scholar
Kuffner, J. J., Nishiwaki, K., Kagami, S., Inaba, M. and Inoue, H., “Footstep Planning Among Obstacles for Biped Robots” Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2001) pp. 500–505.Google Scholar
Koh, K. C. and Cho, H. S., “A smooth path tracking algorithm for wheeled mobile robots with dynamic constraints” J. Intell. Robotic Syst. 24(4), 367–385 (1999).CrossRefGoogle Scholar
Passino, K.M., “Biomimicry of bacterial foraging for distributed optimization and control” IEEE Control Syst. Mag. 22(3), 52–67 (2002).Google Scholar