Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-10T17:12:28.316Z Has data issue: false hasContentIssue false
  • English
  • Français

Trajectory planning and control of multiple mobile robot using hybrid MKH-fuzzy logic controller

Published online by Cambridge University Press:  02 June 2022

Saroj Kumar Open the ORCID record for Saroj Kumar [Opens in a new window]  and
Dayal R. Parhi
Saroj Kumar*
Affiliation:
Robotics Laboratory, National Institute of Technology, Rourkela, Odisha769008, India
Dayal R. Parhi
Affiliation:
Robotics Laboratory, National Institute of Technology, Rourkela, Odisha769008, India
*
*Corresponding author. E-mail: Saroj4sks@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Robotics with artificial intelligence techniques have been the center of attraction among researchers as it is well equipped in the area of human intervention. Here, the krill herd (KH) optimization algorithm is modified and hybridized with a fuzzy logic controller to frame an intelligent controller for optimal trajectory planning and control of mobile robots in obscure environments. The controller is demonstrated for single and multiple robot’s trajectory planning. A Petri-net controller has also been added to avoid conflict situations in multi-robot navigation. MATLAB and V-REP software are used to simulate the work, backed with real-time experiments under laboratory conditions. The robots efficiently achieved the goals by tracing an optimal path without any collision. Trajectory length and time spent during navigation are recorded, and a good agreement between the results is observed. The proposed technique is compared against existing research techniques, and an improvement of 14.26% is noted in terms of path length.

Keywords

autonomous robotcontrolpath planningfuzzy logic
Type
Research Article
Information
Robotica , Volume 40 , Issue 11 , November 2022 , pp. 3952 - 3975
Copyright
© The Author(s), 2022. 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

Gandomi, A. H. and Alavi, A. H., “Krill herd: A new bio-inspired optimization algorithm,” Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012).CrossRefGoogle Scholar
Abualigah, L. M., Khader, A. T., Hanandeh, E. S. and Gandomi, A. H., “A novel hybridization strategy for krill herd algorithm applied to clustering techniques,” Appl. Soft Comput. 60, 423–435 (2017).CrossRefGoogle Scholar
Rao, D. C., Kabat, M. R., Das, P. K. and Jena, P. K., “Cooperative navigation planning of multiple mobile robots using improved krill herd,” Arab. J. Sci. Eng. 43(12), 7869–7891 (2018).Google Scholar
Singh, N. H. and Thongam, K., “Mobile robot navigation using fuzzy logic in static environments,” Proc. Comput. Sci. 125, 11–17 (2018).CrossRefGoogle Scholar
Chen, C. H., Jeng, S. Y. and Lin, C. J., “Mobile robot wall-following control using fuzzy logic controller with improved differential search and reinforcement learning,” Mathematics 8(8), 1254 (2020).CrossRefGoogle Scholar
Ben Jabeur, C. and Seddik, H., “Design of a PID optimized neural networks and PD fuzzy logic controllers for a two-wheeled mobile robot,” Asian J. Control 23(1), 23–41 (2021).CrossRefGoogle Scholar
Muni, M. K., Parhi, D. R., Kumar, P. B. and Kumar, S., “Motion control of multiple humanoids using a hybridized prim’s algorithm-fuzzy controller,” Soft Comput. 25, 1159–1180 (2020). https://doi.org/10.1007/s00500-020-05212-z.CrossRefGoogle Scholar
Muni, M. K., Parhi, D. R., Kumar, P. B., Sahu, C., Dhal, P. R. and Kumar, S., “Global Path Optimization of Humanoid NAO in Static Environment Using Prim’s Algorithm,” In: Intelligent Systems (Springer, Singapore, 2021) pp. 25–34. https://doi.org/10.1007/978-981-33-6081-5_3.CrossRefGoogle Scholar
Kumar, S., Pandey, K. K., Muni, M. K. and Parhi, D. R., “Path Planning of the Mobile Robot Using Fuzzified Advanced Ant Colony Optimization,” In: Innovative Product Design and Intelligent Manufacturing Systems (Springer, Singapore, 2020) pp. 1043–1052. https://doi.org/10.1007/978-981-15-2696-1_101.CrossRefGoogle Scholar
Kumar, S., Parhi, D. R., Kashyap, A. K. and Muni, M. K., “Static and dynamic path optimization of multiple mobile robot using hybridized fuzzy logic-whale optimization algorithm,” Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 235(21), 5718–5735 (2021). https://doi.org/10.1177/0954406220982641.CrossRefGoogle Scholar
Mohanty, P. K. and Parhi, D. R., “Optimal path planning for a mobile robot using cuckoo search algorithm,” J. Exp. Theor. Artif. Intell. 28(1–2), 35–52 (2016).CrossRefGoogle Scholar
Patle, B. K., Parhi, D. R. K., Jagadeesh, A. and Kashyap, S. K., “Matrix-binary codes based genetic algorithm for path planning of mobile robot,” Comput. Electr. Eng. 67, 708–728 (2018). https://doi.org/10.1016/j.compeleceng.2017.12.011.CrossRefGoogle Scholar
Kumar, S., Parhi, D. R., Muni, M. K. and Pandey, K. K., “Optimal path search and control of mobile robot using hybridized sine-cosine algorithm and ant colony optimization technique,” Ind. Robot 47(4), 535–545 (2020). https://doi.org/10.1108/IR-12-2019-0248.CrossRefGoogle Scholar
Singh, M. K. and Parhi, D. R., “Path optimisation of a mobile robot using an artificial neural network controller,” Int. J. Syst. Sci. 42(1), 107–120 (2011). https://doi.org/10.1080/00207720903470155.CrossRefGoogle Scholar
Parhi, D. R., Pradhan, S. K., Panda, A. K. and Behera, R. K., “The stable and precise motion control for multiple mobile robots,” Appl. Soft Comput., 9(2), 477–487 (2009). ISSN: 1568–4946. https://doi.org/10.1016/j.asoc.2008.04.017.CrossRefGoogle Scholar
Pandey, A., Kumar, S., Pandey, K. K. and Parhi, D. R., “Mobile robot navigation in unknown static environments using ANFIS controller,” Perspect. Sci., 8, 421–423 (2016). https://doi.org/10.1016/j.pisc.2016.04.094.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,” Proc. Technol., 14(1), 28–34 (2014).CrossRefGoogle Scholar
Mohanty, P. K. and Parhi, D. R., “A New Real Time Path Planning for Mobile Robot Navigation using Invasive Weed Optimization Algorithm,” In: Gas Turbine India Conference (Vol. 49644, American Society of Mechanical Engineers, 2014) p. V001T07A002.Google Scholar
Sim, K. M. and Sun, W. H., “Ant colony optimization for routing and load-balancing: survey and new directions,” IEEE Trans. Syst. Man Cybern. A Syst. Hum. 33(5), 560–572 (2003).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
Pothal, J. K. and Parhi, D. R., “Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system,” Rob. Auton. Syst. 72, 48–58 (2015).CrossRefGoogle Scholar
Kundu, S. and Parhi, D. R., “Navigation of underwater robot based on dynamically adaptive harmony search algorithm,” Memetic Comput. 8(2), 125–146 (2016).CrossRefGoogle Scholar
Contreras-Cruz, M. A., Ayala-Ramirez, V. and Hernandez-Belmonte, U. H., “Mobile robot path planning using artificial bee colony and evolutionary programming,” Appl. Soft Comput. 30, 319–328 (2015).CrossRefGoogle Scholar
Fen, L., Jiang-hai, Z., Xiao-bo, S., Pei-ying, Z., Shi-hui, F. and Zhong-jie, L., “Path Planning of 6-DOF Humanoid Manipulator based on Improved Ant Colony Algorithm,” In: 2012 24th Chinese Control and Decision Conference (CCDC) (IEEE, 2012) pp. 4158–4161.CrossRefGoogle Scholar
Muni, M. K., Parhi, D. R., Kumar, P., Pandey, K. K., Kumar, S. and Chhotray, A., Sugeno Fuzzy Logic Analysis: Navigation of Multiple Humanoids in Complex Environments,” In: International Conference on Artificial Intelligence in Manufacturing & Renewable Energy (ICAIMRE) (2019).Google Scholar
Muni, M. K., Parhi, D. R., Kumar, P. B., Sahu, C. and Kumar, S., “Towards motion planning of humanoids using a fuzzy embedded neural network approach,” Appl. Soft Comput. 119, 108588 (2022).CrossRefGoogle Scholar
Muni, M. K., Kumar, S., Parhi, D. R. and Pandey, K. K., “Water cycle algorithm: an approach for improvement of navigational strategy of multiple humanoid robots,” Robotica, 1–19 (2021). https://doi.org/10.1017/S0263574721000837.Google Scholar
Kumar, S., Parhi, D. R., Pandey, K. K. and Muni, M. K., “Hybrid IWD-GA: an approach for path optimization and control of multiple mobile robot in obscure static and dynamic environments,” Robotica, 1–28 (2021). https://doi.org/10.1017/S0263574721000114.Google Scholar
Montiel, O., Orozco-Rosas, U. and Sepúlveda, R., “Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles,” Expert Syst. Appl. 42(12), 5177–5191 (2015). https://doi.org/10.1016/j.eswa.2015.02.033.CrossRefGoogle Scholar
Ahmed, A., Maged, A., Soliman, A., El-Hussieny, H. and Magdy, M., “Space deformation based path planning for mobile robots,” ISA Trans. (2021). https://doi.org/10.1016/j.isatra.2021.08.019.Google ScholarPubMed
Bolaji, A. L. A., Al-Betar, M. A., Awadallah, M. A., Khader, A. T., & Abualigah, L. M. (2016). A comprehensive review: Krill Herd algorithm (KH) and its applications. Appl. Soft Comput., 49, 437–446.CrossRefGoogle Scholar
Kumar, S., Muni, M. K., Pandey, K. K., Chhotray, A. and Parhi, D. R., “Path Planning and Control of Mobile Robots using Modified Tabu Search Algorithm in Complex Environment,” In: International Conference on Artificial Intelligence in Manufacturing & Renewable Energy (ICAIMRE) (2019). https://doi.org/10.2139/ssrn.3539922.Google Scholar
Kumar, S., Parhi, D. R., Kashyap, A. K., Muni, M. K. and Dhal, P. R., “Navigational Control and Path Optimization of Mobile Robot Using Updated Sine–Cosine Algorithm in Obscure Environment,” In: Current Advances in Mechanical Engineering (Springer, Singapore, 2021) pp. 989–996. https://doi.org/10.1007/978-981-33-4795-3_91.CrossRefGoogle Scholar
Wang, D., Chen, S., Zhang, Y. and Liu, L., Path planning of mobile robot in dynamic environment: Fuzzy artificial potential field and extensible neural network. Artif. Life Robot., 26(1), 129–139 (2021). https://doi.org/10.1007/s10015-020-00630-6.CrossRefGoogle Scholar
Dirik, M., Kocamaz, A. F. and Castillo, O., “Global path planning and path-following for wheeled mobile robot using a novel control structure based on a vision sensor,” Int. J. Fuzzy Syst. 22(6), 1880–1891 (2020). https://doi.org/10.1007/s40815-020-00888-9.CrossRefGoogle Scholar
Luo, Q., Wang, H., Zheng, Y. and He, J., “Research on path planning of mobile robot based on improved ant colony algorithm,” Neural Comput. Appl. 32(6), 1555–1566 (2020). https://doi.org/10.1007/s00521-019-04172-2.CrossRefGoogle Scholar
Li, J., Wang, J., Peng, H., Hu, Y. and Su, H., “Fuzzy-torque approximation-enhanced sliding mode control for lateral stability of mobile robot,” IEEE Trans. Syst. Man Cybernet. Syst. 52(4), 2491--2500 (2022). https://doi.org/10.1109/TSMC.2021.3050616.Google Scholar
Teli, T. A. and Wani, M. A., “A fuzzy based local minima avoidance path planning in autonomous robots,” Int. J. Inf. Technol. 13(1), 33–40 (2021). https://doi.org/10.1007/s41870-020-00547-0.Google Scholar
Luan, P. G. and Thinh, N. T., “Hybrid genetic algorithm based smooth global-path planning for a mobile robot,” Mech. Des. Struct. Mach., 1–17 (2021). https://doi.org/10.1080/15397734.2021.1876569.CrossRefGoogle Scholar
Kim, P., Park, J., Cho, Y. K. and Kang, J., “UAV-assisted autonomous mobile robot navigation for as-is 3D data collection and registration in cluttered environments,” Autom. Constr. 106, 102918 (2019). https://doi.org/10.1016/j.autcon.2019.102918.CrossRefGoogle Scholar
Hu, H., Zhang, K., Tan, A. H., Ruan, M., Agia, C. and Nejat, G., “A sim-to-real pipeline for deep reinforcement learning for autonomous robot navigation in cluttered rough terrain,” IEEE Rob. Autom. Lett., 6(4), 6569–6576 (2021). https://doi.org/10.1109/LRA.2021.3093551.CrossRefGoogle Scholar