Skip to main content
SpringerLink
Log in

Modified type-2 fuzzy controller for intercollision avoidance of single and multi-humanoid robots in complex terrains

  • Original Research Paper
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

Abstract

The path planning methodology followed by the humanoid robots generally focuses on the improvisation of path length and travel time in completing assigned tasks. The overall computational cost decreases when the robot is guided to the target with minimum path length and travel time. This can be achieved by hybridizing two algorithms. In this paper, the navigational controller of the humanoid robots is based on the hybridization of the Type-2 fuzzy system and the adaptive ant colony optimization (AACO) method. The proposed hybridized technique cumulates the global search Type-2 fuzzy controller with the local improvisation AACO method for efficient path planning. The localization system as an input to the Type-2 fuzzy controller provides global positioning of the obstacles and the target in the unknown terrain. Type-2 fuzzy controller algorithm specifies the initial direction vector to guide the robot to the target based on the IF–THEN rule. If the robot encounters an obstacle in its path, the Type-2 fuzzy controller provides an initial turning angle, which is the input to the AACO. It gives an optimized turning angle, which further refines the direction vector. The proposed controller is demonstrated in a single and multi-humanoid robot system. Single and multiple humanoid robots are placed in various static complex terrains with unique targets for all of them to reach. Due to the possibility of inter-collision among humanoid robots in the muti-humanoid system, a novel dining philosopher controller is employed to solve the conflicting scenario with an optimized solution. Simulations and experiments are carried out on the single and multi-humanoid system based on the proposed hybridized approach yields efficient and collision-free navigation with a proper conformation between the results under 5%. The proposed technique has been compared with the previously developed approach for navigation of the humanoid robot based on travel time. In comparison, it proves to be sufficiently efficient and robust for the path planning of the humanoid robot in complex terrains.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Pan L, Deng Y (2022) A novel similarity measure in intuitionistic fuzzy sets and its applications. Eng Appl Artif Intell 107:104512. https://doi.org/10.1016/j.engappai.2021.104512

    Article  Google Scholar 

  2. Sánchez D, Melin P, Castillo O (2017) Optimization of modular granular neural networks using a firefly algorithm for human recognition. Eng Appl Artif Intell 64:172–186. https://doi.org/10.1016/j.engappai.2017.06.007

    Article  Google Scholar 

  3. Kumar PB, Sahu C, Parhi DR (2018) A hybridized regression-adaptive ant colony optimization approach for navigation of humanoids in a cluttered environment. Appl Soft Comput 68:565–585. https://doi.org/10.1016/j.asoc.2018.04.023

    Article  Google Scholar 

  4. Elhaki O, Shojaei K (2020) A robust neural network approximation-based prescribed performance output-feedback controller for autonomous underwater vehicles with actuators saturation. Eng Appl Artif Intell 88:103382. https://doi.org/10.1016/j.engappai.2019.103382

    Article  Google Scholar 

  5. Sangdani MH, Tavakolpour-Saleh AR, Lotfavar A (2018) Genetic algorithm-based optimal computed torque control of a vision-based tracker robot: simulation and experiment. Eng Appl Artif Intell 67:24–38. https://doi.org/10.1016/j.engappai.2017.09.014

    Article  Google Scholar 

  6. Pandey A, Parhi DR (2017) Optimum path planning of mobile robot in unknown static and dynamic environments using fuzzy-wind driven optimization algorithm. Def Technol 13:47–58. https://doi.org/10.1016/j.dt.2017.01.001

    Article  Google Scholar 

  7. Raja P, Pugazhenthi S (2012) On-line path planning for mobile robots in dynamic environments. Neural Netw World 22:67–83. https://doi.org/10.14311/NNW.2012.22.005

    Article  Google Scholar 

  8. Zhang Z, Li Z, Zhang D, Chen J (2013) Path planning and navigation for mobile robots in a hybrid sensor network without prior location information. Int J Adv Robot Syst 10:172. https://doi.org/10.5772/55790

    Article  Google Scholar 

  9. Pradhan SK, Parhi DR, Panda AK (2009) Motion control and navigation of multiple mobile robots for obstacle avoidance and target seeking: a rule-based neuro-fuzzy technique. Proc Inst Mech Eng Part I J Syst Control Eng 223:275–288. https://doi.org/10.1243/09596518JSCE631

    Article  Google Scholar 

  10. Okada K, Inaba M, Inoue H (2016) Walking navigation system of humanoid robot using stereo vision based floor recognition and path planning with multi-layered body image. In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453). pp. 2155–2160. IEEE

  11. Mirjalili R, Yousefi-Koma A, Shirazi FA, Mansouri S (2017) Online path planning for SURENA III humanoid robot using model predictive control scheme. 4th RSI International Conference on Intelligent Robots Mechatronics, ICRoM 2016. 416–421. doi: https://doi.org/10.1109/ICRoM.2016.7886774

  12. Kusuma M, Riyanto Machbub C (2019) Humanoid Robot Path Planning and Rerouting Using A-Star Search Algorithm. In: Proceedings - 2019 IEEE International Conference on Signals and Systems, ICSigSys 2019. pp. 110–115. IEEE

  13. Zhan X (2019) Research on path planning method of humanoid robot based on improved genetic algorithm. J Phys: Conf Ser 25:022028

    Google Scholar 

  14. Raković M, Savić S, Santos-Victor J, Nikolić M, Borovac B (2019) Human-inspired online path planning and biped walking realization in unknown environment. Front Neurorobot 13:1–13. https://doi.org/10.3389/fnbot.2019.00036

    Article  Google Scholar 

  15. Zhang R, Zhao M, Wang C-L (2018) Standing push recovery based on LIPM dynamics control for biped humanoid robot. In: 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO). pp. 1732–1737. IEEE

  16. Weerakoon T, Ishii K, Nassiraei AAF (2015) An artificial potential field based mobile robot navigation method to prevent from deadlock. J Artif Intell Soft Comput Res 5:189–203. https://doi.org/10.1515/jaiscr-2015-0028

    Article  Google Scholar 

  17. Guo D, Wang H, Leang KK (2018) Nonlinear vision-based observer for visual servo control of an aerial robot in global positioning system denied environments. J Mech Robot 10:1–13. https://doi.org/10.1115/1.4041431

    Article  Google Scholar 

  18. Bhargava D, Vyas S (2018) Agent based solution for dining philosophers problem. 2017 International Conference Infocom Technol. Unmanned Syst Trends Futur Dir ICTUS 2017. 2018-Janua, 563–567. doi: https://doi.org/10.1109/ICTUS.2017.8286072

  19. Choppella V, Sanjeev A, Viswanath K, Jayaraman B (2020) Generalised dining philosophers as feedback control. In: International Conference on Distributed Computing and Internet Technology. pp. 144–164. Springer

  20. Mishra P, Jain U, Choudhury S, Singh S, Pandey A, Sharma A, Singh R, Pathak VK, Saxena KK, Gehlot A (2022) Footstep planning of humanoid robot in ROS environment using Generative Adversarial Networks (GANs) deep learning. Rob Auton Syst 158:104269. https://doi.org/10.1016/j.robot.2022.104269

    Article  Google Scholar 

  21. Ruan S, Poblete KL, Wu H, Ma Q, Chirikjian GS (2020) Efficient path planning in narrow passages for robots with ellipsoidal components. IEEE Trans Robot 45:1–18. https://doi.org/10.1109/TRO.2022.3187818

    Article  Google Scholar 

  22. McCrory S, Mishra B, An J, Griffin R, Pratt J, Sevil HE (2022) Humanoid path planning over rough terrain using traversability assessment.

  23. Choi B, Jo S (2013) A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition. PLoS ONE 8:e74583. https://doi.org/10.1371/journal.pone.0074583

    Article  Google Scholar 

  24. Wang J, Chen Z (2018) A novel hybrid map based global path planning method. In: 2018 3rd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS). pp. 66–70. IEEE

  25. Furlan F, Rubio E, Sossa H, Ponce V (2017) Humanoid robot hierarchical navigation using Petri nets and fuzzy logic. In: 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). pp. 1521–1526. IEEE

  26. Zhang W, Xu Y, Xie J (2019) Path planning of USV based on improved hybrid genetic algorithm. In: 2019 European Navigation Conference (ENC). pp. 1–7. IEEE

  27. Parhi DR, Sahu C, Kumar PB (2018) Navigation of multiple humanoid robots using hybrid adaptive swarm-adaptive ant colony optimisation technique. Comput Animat Virtual Worlds 29:1–20. https://doi.org/10.1002/cav.1802

    Article  Google Scholar 

  28. Karnik NN, Mendel JM, Liang Q (1999) Type-2 fuzzy logic systems. IEEE Trans Fuzzy Syst 7:643–658

    Article  Google Scholar 

  29. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26:29–41. https://doi.org/10.1109/3477.484436

    Article  Google Scholar 

  30. Dorigo M (2007) Ant colony optimization. Scholarpedia 2:1461. https://doi.org/10.4249/scholarpedia.1461

    Article  Google Scholar 

  31. Hao Z, Huang H, Qin Y, Cai R (2007) An ACO Algorithm with Adaptive Volatility Rate of Pheromone Trail. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 4490 LNCS, 1167–1170. doi: https://doi.org/10.1007/978-3-540-72590-9_175

  32. Parsaei MR, Mohammadi R, Javidan R (2017) A new adaptive traffic engineering method for telesurgery using ACO algorithm over software defined networks. Eur. Res. Telemedicine/La Rech. Eur. en Telemedecine. 6, 173–180

  33. Dijkstra EW (1971) Hierarchical ordering of sequential processes. The origin of concurrent programming. Springer

    Google Scholar 

  34. Jahanshahi H, Jafarzadeh M, Sari NN, Pham V-T, Huynh VV, Nguyen XQ (2019) Robot motion planning in an unknown environment with danger space. Electronics 8:201. https://doi.org/10.3390/electronics8020201

    Article  Google Scholar 

  35. Wahab MNA, Lee CM, Akbar MF, Hassan FH (2020) Path planning for mobile robot navigation in unknown indoor environments using hybrid PSOFS algorithm. IEEE Access 8:161805–161815. https://doi.org/10.1109/ACCESS.2020.3021605

    Article  Google Scholar 

Download references

Acknowledgements

This study is not financed.

Author information

Authors and Affiliations

  1. Mechanical Engineering Department, Robotics Laboratory, MIT Art, Design & Technology University, Pune, 412201, Maharashtra, India

    Abhishek Kumar Kashyap

  2. Mechanical Engineering Department, Robotics Laboratory, National Institute of Technology, Rourkela, 769008, Odisha, India

    Dayal R. Parhi

Authors
  1. Abhishek Kumar Kashyap
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dayal R. Parhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization was contributed by AKK, DRP. Methodology was contributed by AKK, DRP– Software was contributed by AKK;– Investigation was contributed by AKK;– Formal Analysis was contributed by AKK, DRP – Validation was contributed by AKK;– Data curation was contributed by AKK– Writing—original draft was contributed by AKK;– Writing—review and editing was contributed by AKK, DRP – Visualization was contributed by AKK, DRP – Resources were contributed by AKK– Project administration was contributed by AKK, DRP – Supervision was contributed by DRP

Corresponding author

Correspondence to Abhishek Kumar Kashyap.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Kashyap, A.K., Parhi, D.R. Modified type-2 fuzzy controller for intercollision avoidance of single and multi-humanoid robots in complex terrains. Intel Serv Robotics 16, 87–108 (2023). https://doi.org/10.1007/s11370-022-00448-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-022-00448-0

Keywords

Search

Navigation