Skip to main content

Advertisement

Log in

Abstract

One of the most difficult tasks in robotics is to replace one human-operator by an intelligent, autonomous and emotional humanoid robot for conducting various types of real-world tasks. To perform a pre-specified task, the optimal design of a proposed humanoid robot is being frozen after carrying out its kinematics and dynamic analysis. Based on this optimized design, the robot is fabricated, and its performances are tested in the Laboratory. If it is found to be satisfactory, it is then used in the real field. Let us assume that the developed humanoid robot is facing a few undesirable disturbances due to some changes in the working environment. For example, one humanoid robot working as a soldier in the real field suddenly faces some un-expected rough terrains to be negotiated by maintaining its dynamic balance and consuming minimum energy. This paper deals with the issues related to negotiating these sudden changes in the environment and varying loads with the help of its AI-assisted adaptive vision system, multi-sensors data fusion system, motion and gait planning schemes, controller, and so on. After summarizing this study, some scopes for the future study have also been suggested. To make an intelligent humanoid robot capable of negotiating the varying situations, it should be equipped with AI-assisted vision system, adaptive motion and gait planners, adaptive and robust controller, and others.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Albus JS (1991) Outline for a theory of intelligence. IEEE Trans Syst Man Cybern 21(3):473–509

    Article  MathSciNet  Google Scholar 

  • Das AK, Pratihar DK (2019), A new Bonobo Optimizer (BO) for real-parameter optimization, Proc. of IEEE TENSYMP Conference, Kolkata, India.

  • Gupta P, Pratihar DK, Deb K (2023) Analysis and optimization of gait cycle of 25 dof robot using particle swarm optimization and genetic algorithms. Int J Humanoid Rob. https://doi.org/10.1142/S0219843623500111

    Article  Google Scholar 

  • Ha Q, Nguyen QH, Rye DC, Durrant-Whyle HF (2001) Fuzzy sliding-mode controllers with applications. IEEE Trans Indus Electron 48(1):38–46

    Article  Google Scholar 

  • Hasan MA, Mishra RK, Singh S (2019) Speed control of DC motor using adaptive neuro-fuzzy inference system based PID controller, Proc. Of 2nd International Conference on Power Energy Environmental and Intelligent Control (PEEIC), G. L. Bajaj Institute of Technology and Management, Greater Noida, UP, India, Oct. 18–19, 2019.

  • Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, USA

    Google Scholar 

  • Huan TT, Van Kien C, Anh HP, Nam NT (2018) Adaptive gait generation for humanoid robot using evolutionary neural model optimized with modified differential evolution technique. Neurocomputing 3(320):112–20

    Article  Google Scholar 

  • Ivakhnenko AG (1971) Polynomial theory of complex systems. IEEE Trans Syst Man Cybern 4:364–378

    Article  MathSciNet  Google Scholar 

  • Jahanshahi H, Jafarzadeh M, Sari NN, Pham VT, VanHuynh V, Nguyen XQ (2019) Robot motion planning in an unknown environment with danger space. Electronics 8:201

    Article  Google Scholar 

  • Karpathy A, Abbeel P, Brockman G, Chen P, Cheung V, Duan Y, Goodfellow I, Kingma D, Ho J, Houthooft R, Salimans T, Schulman J, Sutskever I, Zaremba W (2016) Generative models. OpenAI.

  • Kashyap AK, Parhi DR, Muni MK, Pandey KK (2020) A hybrid technique for path planning of humanoid robot NAO in static and dynamic terrains. Appl Soft Comput 96:106581

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995), Particle Swarm Optimization, Proc. of IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948.

  • Kohonen T (1995) Self-Organizing Maps. Springer-Verlag, Heidelberg

    Book  Google Scholar 

  • Landau YD (1979) Adaptive control: the model reference approach. Marcel Dekker, New York

    Google Scholar 

  • Mahapatra A, Roy SS, Pratihar DK (2019) Multi-body dynamic modeling of multi-legged robots. Springer Pte Ltd. https://doi.org/10.1007/978-981-15-2953-5_4

    Article  Google Scholar 

  • Majumder S, Pratihar DK (2018) Multi-sensor data fusion through fuzzy clustering and predictive tools. Exp Syst Appl. https://doi.org/10.1016/j.eswa.2018.04.026

    Article  Google Scholar 

  • Matthews D, Spielberg A, Rus D, Bongard J (2023) Efficient automatic design of robots. PNAS 120(41):e2305180120. https://doi.org/10.1073/pnas.2305180120

    Article  Google Scholar 

  • McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Article  MathSciNet  Google Scholar 

  • Omar BAA, Haikal AYM, Areed FFG (2011) Design adaptive neuro-fuzzy speed controller for an elctro-mechanical system. Ain Shams Eng J 2(2):99–107

    Article  Google Scholar 

  • Pratihar DK (2014) Soft computing: fundamentals and applications. Narosa Publishing House Pvt. Ltd., New-Delhi

    Google Scholar 

  • Pratihar DK (2017) Fundamentals of robotics. Narosa Publishing House, New-Delhi

    Google Scholar 

  • Pratihar DK, Jain LC (2010) Intelligent autonomous systems: foundations and applications. Springer-Verlag, Berlin

    Book  Google Scholar 

  • Rajendra R (2012), Modeling and Simulations of Robotic Systems Using Soft Computing, PhD Thesis, IIT Kharagpur, India.

  • Shojaei K, Shahri AM, Tabibian, b, (2013) Design and implementation of an inverse dynamics controller for uncertain nonholonomic robotic systems. J Intell Rob Syst 71:65–83

    Article  Google Scholar 

  • Tao C, Xue J, Zhang Z, Cao F, Li C, Gao H (2021) Gait optimization method for humanoid robots based on parallel comprehensive learning particle swarm optimization algorithm. Front Neurorobot 14:600885

    Article  Google Scholar 

  • Vikas, Parhi DR, Kashyap AK (2023) Humanoid robot path planning using memory-based gravity search algorithm and enhanced differential evolution approach in a complex environment. Exp Syst Appl 215(1):119423

    Article  Google Scholar 

  • Vukobratovic M, Frank AA, Juricic D (1970) On the stability of biped locomotion. IEEE Trans Biomed Eng 17(1):25–36

    Article  Google Scholar 

  • Vundavilli PR, Pratihar DK (2010) Dynamically balanced optimal gaits of a ditch-crossing biped robot. Robot Auton Syst 58:349–361

    Article  Google Scholar 

  • Vundavilli PR, Pratihar DK (2011) Near-optimal gait generations of a two-legged robot on rough terrains using soft computing. Robot Comput Integr Manuf 27:521–530

    Article  Google Scholar 

  • Website: https://www.ibm.com/ibm/history/ibm100/us/en/icons/deepblue/

  • Website: https://www.softbankrobotics.com/emea/en/nao

  • Website: ttps://vegavid.com/blog/?s=The+power+of+AI

  • Zadeh LA (1965) Fuzzy Sets. Inf Control 8:338–353

    Article  Google Scholar 

  • Ziegler JG, Nichols NB (1942) Optimum settings for automatic controllers. Trans ASME 64:759–768

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dilip Kumar Pratihar.

Ethics declarations

Conflict of interest

The author has no conflict of interest with others.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pratihar, D.K. AI-Assisted Intelligent Humanoid Robot. Trans Indian Natl. Acad. Eng. (2024). https://doi.org/10.1007/s41403-024-00468-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41403-024-00468-5

Keywords

Navigation