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Water cycle algorithm: an approach for improvement of navigational strategy of multiple humanoid robots

Published online by Cambridge University Press:  23 June 2021

Manoj Kumar Muni Open the ORCID record for Manoj Kumar Muni [Opens in a new window] ,
Saroj Kumar Open the ORCID record for Saroj Kumar [Opens in a new window] ,
Dayal R. Parhi  and
Krishna Kant Pandey Open the ORCID record for Krishna Kant Pandey [Opens in a new window]
Manoj Kumar Muni*
Affiliation:
Department of Mechanical engineering, Indira Gandhi Institute of Technology, Sarang, 759146, Odisha, India
Saroj Kumar
Affiliation:
Robotics Laboratory, Department of Mechanical Engineering, National Institute of Technology, Rourkela, 769008, Odisha, India
Dayal R. Parhi
Affiliation:
Robotics Laboratory, Department of Mechanical Engineering, National Institute of Technology, Rourkela, 769008, Odisha, India
Krishna Kant Pandey
Affiliation:
Department of Mechanical Engineering, G.H. Raisoni Institute of Engineering and Technology, Pune, 412207, Maharashtra, India
*
*Corresponding author. Email: [email protected]
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Abstract

This paper presents an efficient water cycle algorithm based on the processes of water cycle with movement of streams and rivers in to the sea. This optimization algorithm is applied to obtain the optimal feasible path with minimum travel duration during motion planning of both single and multiple humanoid robots in both static and dynamic cluttered environments. This technique discards the rainfall process considering falling water droplets forming streams during raining and the process of flowing. The flowing process searches the solution space and finds the more accurate solution and represents the local search. Motion planning of humanoids is carried out in V-REP software. The performance of proposed algorithm is tested in experimental scenario under laboratory conditions and shows the developed algorithm performs well in terms of obtaining optimal path length and minimum time span of travel. Here, navigational analysis has been performed on both single as well as multiple humanoid robots. Statistical analysis of results obtained from both simulation and experimental environments is carried out for both single and multiple humanoids, along with the comparison with another existing optimization technique that indicate the strength and effectiveness of the proposed water cycle algorithm.

Keywords

water cycle algorithmhumanoid NAOV-REPsimulationexperimentstatistical analysis
Type
Article
Information
Robotica , Volume 40 , Issue 3 , March 2022 , pp. 798 - 816
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Documentation Aldebaran, N. R., (2017).Google Scholar
Lee, T. L. and Wu, C. J., “Fuzzy motion planning of mobile robots in unknown environments,” J. Intell. Rob. Syst. 37(2), 177–191 (2003).CrossRefGoogle Scholar
Dianyong, Y. and C Jinghua, “Mobile Robot Path Planning in a Simple Unknown Environment,” 2004 IEEE International Conference on Robotics and Biomimetics (IEEE, 2004) pp. 152–156. doi: 10.1109/ROBIO.2004.1521768CrossRefGoogle Scholar
Pradhan, S. K., Parhi, D. R. and Panda, A. K., “Navigation of multiple mobile robots using rule-based neuro-fuzzy technique,” Int. J. Comput. Intell. 3(2), 142–152 (2006).Google Scholar
Hui, N. B. and Pratihar, D. K., “Soft computing-based navigation schemes for a real wheeled robot moving among static obstacles,” J. Intell. Syst. 51(3), 333–368 (2008).CrossRefGoogle Scholar
Garcia, M. P., Montiel, O., Castillo, O., SepÚlveda, R. and Melin, P., “Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation,” Appl. Soft Comput. 9(3), 1102–1110 (2009).CrossRefGoogle Scholar
Abiyev, R., Ibrahim, D. and Erin, B., “Navigation of mobile robots in the presence of obstacles,” Adv. Eng. Software. 41(10–11), 1179–1186 (2010).CrossRefGoogle Scholar
Al Yahmedi, A. S. and Fatmi, M. A., “Fuzzy logic-based navigation of mobile robots,” In: Recent Advances in Mobile Robotics (Topalov, A. ed.) (IntechOpen Ltd., London, UK, 2011) pp. 287–310.Google Scholar
Miao, H. and Tian, Y. C., “Dynamic robot path planning using an enhanced simulated annealing approach,” Appl. Math. Comput. 222, 420–437 (2013).Google Scholar
Motlagh, O., Nakhaeinia, D., Tang, S. H., Karasfi, B. and Khaksar, W., “Automatic navigation of mobile robots in unknown environments,” Neural Comput. Appl. 24(7–8), 1569–1581 (2014).CrossRefGoogle Scholar
Kala, R., “Coordination in navigation of multiple mobile robots,” Cybern. Syst. 45(1), 1–24 (2014).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
Mo, H. and Xu, L., “Research of biogeography particle swarm optimization for robot path planning,” Neurocomputing 148, 91–99 (2015).CrossRefGoogle Scholar
Karami, A. H. and Hasanzadeh, M., “An adaptive genetic algorithm for robot motion planning in 2D complex environments,” Comput. Electr. Eng. 43, 317–329 (2015).CrossRefGoogle Scholar
Hidalgo-Paniagua, A., Vega-RodrÍguez, M. A., Ferruz, J. and PavÓn, N., “MOSFLA-MRPP: multi-objective shuffled frog-leaping algorithm applied to mobile robot path planning,” Eng. Appl. Artif. Intell. 44, 123–136 (2015).CrossRefGoogle Scholar
Davoodi, M., Panahi, F., Mohades, A. and Hashemi, S. N., “Clear and smooth path planning,” Appl. Soft Comput. 32, 568–579 (2015).CrossRefGoogle 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).CrossRefGoogle Scholar
Liang, J. H. and Lee, C. H., “Efficient collision-free path-planning of multiple mobile robots system using efficient artificial bee colony algorithm,” Adv. Eng. Soft. 79, 47–56 (2015).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,” Rob. Auton. Syst. 64, 137–141 (2015).CrossRefGoogle Scholar
Das, P. K., Behera, H. S. and Panigrahi, B. K., “A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning,” Swarm Evol. Comput. 28, 14–28 (2016).CrossRefGoogle Scholar
Das, P. K., Behera, H. S., Das, S., Tripathy, H. K., Panigrahi, B. K. and Pradhan, S. K., “A hybrid improved PSO-DV algorithm for multi-robot path planning in a clutter environment,” Neurocomputing 207, 735–753 (2016).CrossRefGoogle Scholar
Dadgar, M., Jafari, S. and Hamzeh, A., “A PSO-based multi-robot cooperation method for target searching in unknown environments,” Neurocomputing 177, 62–74 (2016).CrossRefGoogle Scholar
Sadhu, A. K., Konar, A., Bhattacharjee, T. and Das, S., “Synergism of firefly algorithm and Q-learning for robot arm path planning,” Swarm Evol. Comput. 43, 50–68 (2018).CrossRefGoogle Scholar
Shao, S., Peng, Y., He, C. and Du, Y., “Efficient path planning for UAV formation via comprehensively improved particle swarm optimization,” ISA Trans. 97, 415–430 (2020).CrossRefGoogle ScholarPubMed
Gu, W., Cai, S., Hu, Y., Zhang, H. and Chen, H., “Trajectory planning and tracking control of a ground mobile robot: a reconstruction approach towards space vehicle,” ISA Trans. 87, 116–128 (2019).CrossRefGoogle ScholarPubMed
Song, C., Liu, G., Zhang, X., Zang, X., Xu, C. and Zhao, J., “Robot complex motion learning based on unsupervised trajectory segmentation and movement primitives,” ISA Trans. 97, 325–335 (2020).CrossRefGoogle ScholarPubMed
KovÁcs, B., Szayer, G., Tajti, F., Burdelis, M. and Korondi, P., “A novel potential field method for path planning of mobile robots by adapting animal motion attributes,” Rob. Auton. Syst. 82, 24–34 (2016).CrossRefGoogle Scholar
Mac, T. T., Copot, C., Tran, D. T. and De Keyser, R., “A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization,” Appl. Soft Comput. 59, 68–76 (2017).CrossRefGoogle Scholar
Pandey, A. and Parhi, D. R., “Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm,” Def. Technol. 13(1), 47–58 (2017).CrossRefGoogle Scholar
Elhoseny, M., Tharwat, A. and Hassanien, A. E., “Bezier curve based path planning in a dynamic field using modified genetic algorithm,” J. Comput. Sci. 25, 339–350 (2018).CrossRefGoogle Scholar
Chen, Y. M., Hsueh, C. S., Wang, C. K. and Wu, T. Y., “Decision fusion using fuzzy threshold scheme for target detection in sensor networks,” J. Comput. Sci. 25, 327–338 (2018).CrossRefGoogle Scholar
Li, J., Sun, R., Cheng, C. and Li, S., “Roaming path generation algorithm and optimization based on Bezier curve,” IFAC-PapersOnLine 51(17), 339–345 (2018).CrossRefGoogle Scholar
Singh, Y., Sharma, S., Sutton, R., Hatton, D. and Khan, A., “A constrained A* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents,” Ocean Eng. 169, 187–201 (2018).CrossRefGoogle Scholar
Arvanitakis, I., Tzes, A. and Giannousakis, K., “Synergistic exploration and navigation of mobile robots under pose uncertainty in unknown environments,” Int J. Adv. Rob. Syst. 15(1), 1729881417750785 (2018).Google Scholar
Zhou, L. and Tokekar, P., “Active target tracking with self-triggered communications in multi-robot teams,” IEEE Trans Autom Sci Eng. 16(3), 1085–1096 (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
Lee, H., Kim, H. and Kim, H. J., “Planning and control for collision-free cooperative aerial transportation,” IEEE Trans Autom. Sci. Eng. 15(1), 189–201 (2016).CrossRefGoogle Scholar
Eskandar, H., Sadollah, A., Bahreininejad, A. and Hamdi, M., “Water cycle algorithm–a novel metaheuristic optimization method for solving constrained engineering optimization problems,” Comput. Struct. 110, 151–166 (2012).CrossRefGoogle Scholar
Shi, W., Wang, K. and Yang, S. X., “A fuzzy-neural network approach to multisensor integration for obstacle avoidance of a mobile robot,” Intell. Autom. Soft Comput. 15(2), 289–301 (2009).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. Rob. 47(4), 535–545 (2020).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 Deepak, (B. B. V. L., Parhi, D. R. K and Jena, P. C. eds) (Springer, Singapore, 2020) pp. 1043–1052. doi: 10.1007/978-981-15-2696-1_101CrossRefGoogle 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 (2021). doi: 10.1007/s00500-020-05212-zCrossRefGoogle 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. Part C: J. Mech. Eng. Sci. (2021). doi: 10.1177/0954406220982641CrossRefGoogle 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).CrossRefGoogle Scholar
Kumar, S., Parhi, D., Pandey, K. and Muni, M., “Hybrid IWD-GA: an approach for path optimization and control of multiple mobile robot in obscure static and dynamic environments,” Robotica, 1–28 (2021). doi: 10.1017/S0263574721000114CrossRefGoogle Scholar