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Smart Navigation of Humanoid Robots Using DAYKUN-BIP Virtual Target Displacement and Petri-Net Strategy

Published online by Cambridge University Press:  24 December 2018

Dayal R. Parhi  and
Priyadarshi Biplab Kumar
Dayal R. Parhi*
Affiliation:
Robotics Laboratory, Mechanical Engineering Department, National Institute of Technology, Rourkela, Odisha, 769008, India. E-mail: [email protected]
Priyadarshi Biplab Kumar
Affiliation:
Robotics Laboratory, Mechanical Engineering Department, National Institute of Technology, Rourkela, Odisha, 769008, India. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

With an ability to mimic the human behaviour and replace human efforts in proper platforms, humanoid robots have always acquired a special place among robotics practitioners. Being a complex method of analysis, navigation and path planning, humanoid robots still possess an interesting yet challenging area of investigation. In the current work, a novel navigational strategy has been proposed for smooth and hassle-free movement of single as well as multi-humanoid robots in complex environments. Here, the navigational plan is based on a virtual target displacement strategy which is activated when the robot is unable to find a safe path along the actual target line. After detection of a potential obstacle by the sensors of the robot, a number of virtual targets are generated around the actual target. Then, the most feasible path and point to move are calculated by assigning suitable weightage through several selected parameters to each target line and visualizing the safest path. The proposed approach is implemented on a V-REP simulation platform, and the simulation results are also validated against an experimental set-up prepared under test conditions. The validation of simulation results against experimental counterparts has revealed satisfactory agreement between them. To avoid possibility of any inter-collision during navigation of multi-humanoids under a common platform, a Petri-Net strategy has been integrated along with the proposed control strategy. Finally, the developed approach is also assessed against another existing navigational controller, and a significant performance improvement has been observed.

Keywords

Humanoid NAONavigationVirtual target displacementPetri-NetV-REP
Type
Articles
Information
Robotica , Volume 37 , Issue 4 , April 2019 , pp. 626 - 640
Copyright
Copyright © Cambridge University Press 2018 

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References

Parhi, D. R. and Singh, M. K., “Real-time navigational control of mobile robots using an artificial neural network,” Proc. Inst. Mech. Eng. Part C 223(7), 1713–1725 (2009).10.1243/09544062JMES1410CrossRefGoogle Scholar
Pandey, A., Sonkar, R. K., Pandey, K. K. and Parhi, D. R., “Path Planning Navigation of Mobile Robot with Obstacles Avoidance Using Fuzzy Logic Controller,” IEEE 8th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, India (2014) pp. 39–41.Google Scholar
Pun-Cheng, L. S. C., Tang, M. Y. F. and Cheung, I. K. L., “Exact cell decomposition on base map features for optimal path finding,” Int. J. Geogr. Inf. Sci. 21(2), 175–185 (2007).10.1080/13658810600852206CrossRefGoogle Scholar
Glavaski, D., Volf, M. and Bonkovic, M., “Mobile robot path planning using exact cell decomposition and potential field methods,” WSEAS Trans. Circuits Syst. 8(9), 789–800 (2009).Google Scholar
Cai, C. and Ferrari, S., “Information-driven sensor path planning by approximate cell decomposition,” IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 39(3), 672–689 (2009).Google ScholarPubMed
Bhattacharya, P. and Gavrilova, M. L., “Voronoi Diagram in Optimal Path Planning,” 4th International Symposium on Voronoi Diagrams in Science and Engineering, Glamorgan, UK (2007) pp. 38–47.10.1109/ISVD.2007.43CrossRefGoogle Scholar
Bhattacharya, P. and Gavrilova, M. L., “Roadmap-based path planning-using the voronoi diagram for a clearance-based shortest path,” IEEE Robot. Autom. Mag. 15(2), 58–66 (2008).10.1109/MRA.2008.921540CrossRefGoogle Scholar
Chen, P., Xiaoqing, L., Jiyang, D. and Linfei, Y., “Research of Path Planning Method Based on the Improved Voronoi Diagram,” 25th Chinese Control and Decision Conference (CCDC), Guiyang, China (2013) pp. 2940–2944.10.1109/CCDC.2013.6561448CrossRefGoogle Scholar
Haihan, C. and Li, S., “Research on the Path Planning Based on Voronoi Diagram and Dijkatra’s Algorithm,” IEEE Conference Anthology, China (2013) pp. 1–4.Google Scholar
Nattharith, P. and Güzel, M. S., “Machine vision and fuzzy logic-based navigation control of a goal-oriented mobile robot,” Adapt. Behav. 24(3), 168–180 (2016).10.1177/1059712316645845CrossRefGoogle Scholar
Dirik, M., “Collision-free mobile robot navigation using fuzzy logic approach,” Int. J. Comput. Appl. 179(9), 33–39 (2018).Google Scholar
Shi, H., Li, X., Pan, W., Hwang, K. S. and Li, Z., “A novel fuzzy three-dimensional grid navigation method for mobile robots,” Int. J. Adv. Robot. Syst. 14(3), (2017). doi: 10.1177/1729881417710444CrossRefGoogle Scholar
Al-Mutib, K. and Abdessemed, F., “Indoor mobile robot navigation in unknown environment using fuzzy logic based behaviors,” Adv. Sci. Technol. Eng. Syst. J. 2(3), 327–337 (2017).10.25046/aj020342CrossRefGoogle Scholar
Van Nguyen, T. T., Phung, M. D. and Tran, Q. V. (2017) “Behavior-based navigation of mobile robot in unknown environments using fuzzy logic and multi-objective optimization,” preprint arXiv:1703.03161.Google Scholar
Zhong, C., Liu, S., Lu, Q. and Zhang, B., “Continuous learning route map for robot navigation using a growing-on-demand self-organizing neural network,” Int. J. Adv. Robot. Syst. 14(6), (2017). doi: 10.1177/1729881417743612CrossRefGoogle Scholar
Sierakowski, C. A. and dos Santos Coelho, L., “Path Planning Optimization for Mobile Robots Based on Bacteria Colony Approach,” In: Applied Soft Computing Technologies: The Challenge Of Complexity (Springer, Berlin, Heidelberg, 2006) pp. 187–198.10.1007/3-540-31662-0_15CrossRefGoogle Scholar
Jhankal, N. K. and Adhyaru, D., “Comparative analysis of bacterial foraging optimization algorithm with simulated annealing,” Int. J. Sci. Res. (IJSR) 3(3), 10–13 (2014).Google Scholar
Chen, H., Zhu, Y. and Hu, K., “Adaptive bacterial foraging optimization,” Abstr. Appl. Anal. 2011, 1–27 (2011).Google Scholar
Sharma, A. and Satav, S., “Path navigation using computational intelligence,” Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2(7), 395–398 (2012).Google Scholar
Patle, B. K., Parhi, D., Jagadeesh, A. and Sahu, O. P., “Real time navigation approach for mobile robot,” J. Chem. Phys. 12(2), 135–142 (2017).Google Scholar
Patle, B. K., Parhi, D. R., Jagadeesh, A. and Kashyap, S. K., “On firefly algorithm: Optimization and application in mobile robot navigation,” World J. Eng. 14(1), 65–76 (2017).10.1108/WJE-11-2016-0133CrossRefGoogle Scholar
Parhi, D. R. and Singh, M. K., “Navigational strategies of mobile robots: A review,” Int. J. Autom. Control 3(2–3), 114–134 (2009).10.1504/IJAAC.2009.025237CrossRefGoogle Scholar
Mohanty, P. K. and Parhi, D. R., “A new hybrid optimization algorithm for multiple mobile robots navigation based on the CS-ANFIS approach,” Memetic Comput. 7(4), 255–273 (2015).10.1007/s12293-015-0160-3CrossRefGoogle Scholar
Deepak, B. B. V. L. and Parhi, D. R., “Control of an automated mobile manipulator using artificial immune system,” J. Exp. Theor. Artif. Intell. 28(1–2), 417–439 (2016).10.1080/0952813X.2015.1132261CrossRefGoogle Scholar
Pandey, A. and Parhi, D. R., “Multiple mobile robots navigation and obstacle avoidance using minimum rule based ANFIS network controller in the cluttered environment,” Int. J. Adv. Robot. Autom. 1(1), 1–11 (2016).Google Scholar
Mohanty, P. K. and Parhi, D. R., “A new intelligent motion planning for mobile robot navigation using multiple adaptive neuro-fuzzy inference system,” Appl. Math. Inf. Sci. 8(5), 2527 (2014).10.12785/amis/080551CrossRefGoogle Scholar
Pothal, J. K. and Parhi, D. R., “Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system,” Robot. Auton. Syst. 72, 48–58 (2015).10.1016/j.robot.2015.04.007CrossRefGoogle Scholar
Pal, N. S. and Sharma, S., “Robot path planning using swarm intelligence: A survey,” Int. J. Comput. Appl. 83(12), 5–12 (2013).Google Scholar
Hidalgo-Paniagua, A., Vega-Rodríguez, M. A., Ferruz, J. and Pavón, N., “Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach,” Soft Comput. 21(4), 949–964 (2017).10.1007/s00500-015-1825-zCrossRefGoogle Scholar
Liu, C., Zhao, Y., Gao, F. and Liu, L., “Three-dimensional path planning method for autonomous underwater vehicle based on modified firefly algorithm,” Math. Probl. Eng. 2015, 1–10 (2015).Google Scholar
Liu, C., Gao, Z. and Zhao, W., “A New Path Planning Method Based on Firefly Algorithm,” Fifth International Joint Conference on Computational Sciences and Optimization (CSO), Harbin, China (2012) pp. 775–778.Google Scholar
Brand, M. and Yu, X. H., “Autonomous Robot Path Optimization Using Firefly Algorithm,” International Conference on Machine Learning and Cybernetics (ICMLC), Tianjin, China, Vol. 3 (2013) pp. 1028–1032.Google Scholar
Lee, K. B., Myung, H. and Kim, J. H., “Online multiobjective evolutionary approach for navigation of humanoid robots,” IEEE Trans. Ind. Electron. 62(9), 5586–5597 (2015).10.1109/TIE.2015.2405901CrossRefGoogle 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).10.15282/jmes.12.2.2018.1.0313CrossRefGoogle 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).10.1016/j.dt.2018.03.008CrossRefGoogle 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,” Modell. Measur. Control A 91(1), 32–36 (2018).10.18280/mmc_a.910105CrossRefGoogle Scholar
Ariffin, I. M., Rasidi, A. I. H. M., Yussof, H., Mohamed, Z., Miskam, M. A., Amin, A. T. M. and Omar, A. R., “Sensor based mobile navigation using humanoid robot Nao,” Procedia Comput. Sci. 76, 474–479 (2015).10.1016/j.procs.2015.12.319CrossRefGoogle Scholar
Kumar, A., Kumar, P. B. and Parhi, D. R., “Intelligent navigation of humanoids in cluttered environments using regression analysis and genetic algorithm,” Arabian J. Sci. Eng. 43(12), 7655–7678 (2018).10.1007/s13369-018-3157-7CrossRefGoogle 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, e1858 (2018). doi: 10.1002/cav.1858CrossRefGoogle Scholar
Kumar, P. B., Pandey, K. K., Sahu, C., Chhotray, A. and Parhi, D. R., “A Hybridized RA-APSO Approach for Humanoid Navigation,” Nirma University International Conference on Engineering (NUiCONE), Ahmedabad, India (2017) pp. 1–6.Google 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).10.1016/j.asoc.2018.04.023CrossRefGoogle Scholar
Kumar, P. B., Sahu, C., Parhi, D. R., Pandey, K. K. and Chhotray, A., “Static and dynamic path planning of humanoids using an advanced regression controller,” Sci. Iranica (2018). doi: 10.24200/SCI.2018.5064.1071CrossRefGoogle Scholar
Karkowski, P., Oßwald, S. and Bennewitz, M., “Real-Time Footstep Planning in 3D Environments,” IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), Cancun, Mexico (2016) pp. 69–74.10.1109/HUMANOIDS.2016.7803256CrossRefGoogle Scholar
Sahu, C., Kumar, P. B. and Parhi, D. R. “An intelligent path planning approach for humanoid robots using adaptive particle swarm optimisation,” Int. J. Artif. Intell. Tools 27(5), 1850015 (2018).10.1142/S021821301850015XCrossRefGoogle Scholar
Sahu, C., Parhi, D. R. and Kumar, P. B., “An approach to optimize the path of humanoids using adaptive ant colony optimization,” J. Bionic Eng. 15(4), 623–635 (2018).10.1007/s42235-018-0051-7CrossRefGoogle Scholar
Yoo, J. K. and Kim, J. H., “Gaze control-based navigation architecture with a situation-specific preference approach for humanoid robots,” IEEE/ASME Trans. Mechatron. 20(5), 2425–2436 (2015).10.1109/TMECH.2014.2382633CrossRefGoogle Scholar
Moulard, T., Alcantarilla, P. F., Lamiraux, F., Stasse, O. and Dellaert, F., “Reliable indoor navigation on humanoid robots using vision-based localization,” Rapport LAAS Number 12549 (2012).Google Scholar
Xu, W. L., Tso, S. K. and Fung, Y. H., “Fuzzy reactive control of a mobile robot incorporating a real/virtual target switching strategy,” Robot. Auton. Syst. 23(3), 171–186 (1998).10.1016/S0921-8890(97)00066-3CrossRefGoogle Scholar
Xu, W. L. and Tso, S. K., “Sensor-based fuzzy reactive navigation of a mobile robot through local target switching,” IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 29(3), 451–459 (1999).10.1109/5326.777079CrossRefGoogle Scholar
Kofinas, N., Orfanoudakis, E. and Lagoudakis, M. G., “Complete analytical forward and inverse kinematics for the NAO humanoid robot,” J. Intell. Robot. Syst. 77(2), 251–264 (2015).10.1007/s10846-013-0015-4CrossRefGoogle Scholar
Peterson, J. L., Petri Net Theory and the Modeling of Systems (Prentice-Hall, Englewood Cliffs, NJ, 1981).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).10.1017/S0263574702004526CrossRefGoogle Scholar
Al Yahmedi, A. S. and Fatmi, M. A., “Fuzzy Logic Based Navigation of Mobile Robots,” In: Recent Advances in Mobile Robotics (InTech, London, UK, 2011).Google Scholar