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Intelligent Hybridization of Regression Technique with Genetic Algorithm for Navigation of Humanoids in Complex Environments

Published online by Cambridge University Press:  19 June 2019

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

In the current investigation, a novel navigational controller has been designed and implemented for humanoids in cluttered environments. Here, regression analysis is hybridized with genetic algorithm (GA) for designing the controller. The obstacle distances collected in the form of sensor outputs are initially fed to the regression controller; and based on the previous training pattern data, an intermediate advancing angle (AA) is obtained as the first output. The intermediate AA obtained from the regression controller along with other inputs is again fed to the GA controller, which generates the final AA as the desired final output to avoid the obstacles present in a complex environment and reach the destination successfully. The working of the controller is tested on a V-REP simulation platform. In the current work, navigation of both single as well as multiple humanoids has been attempted. To avoid inter-collision among multiple humanoids during their navigation in a common platform, a Petri-Net model has been proposed. The simulation results are validated through a real-time experimental platform developed under laboratory conditions. The results obtained from both the simulation and experimental platforms are compared against each other and are found to be in good agreement with acceptable percentage of errors. Finally, the proposed controller is also compared with other existing navigational controller and an improvement in performance has been observed.

Keywords

Humanoid NAORA controllerGA controllerPetri-NetHybridizationV-REP
Type
Articles
Information
Robotica , Volume 38 , Issue 4 , April 2020 , pp. 565 - 581
Copyright
© Cambridge University Press 2019 

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References

Atkinson, A. C., “Robust and diagnostic regression analyses,” Commun. Stat. Theory Methods 11(22), 2559–2571 (1982).CrossRefGoogle Scholar
Frank, B., Stachniss, C., Abdo, N. and Burgard, W., “Using Gaussian Process Regression for Efficient Motion Planning in Environments with Deformable Objects,” Proceedings of the 9th AAAI Conference on Automated Action Planning for Autonomous Mobile Robots, San Francisco, USA (2011) pp. 2–7.Google Scholar
Frank, B., Stachniss, C., Abdo, N. and Burgard, W., “Efficient Motion Planning for Manipulation Robots in Environments with Deformable Objects,” 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Francisco, USA, IEEE (2011) pp. 2180–2185.Google Scholar
Qi, N., Ma, B., Liu, X. E., Zhang, Z. and Ren, D., “A Modified Artificial Potential Field Algorithm for Mobile Robot Path Planning,” 7th World Congress on Intelligent Control and Automation, Chongqing, China, IEEE (2008) pp. 2603–2607.Google Scholar
Lee, Y. J. and Bien, Z., “Path planning for a quadruped robot: an artificial field approach,” Adv. Robot. 16(7), 609–627 (2002).Google Scholar
Kim, E., Choi, S. and Oh, S. “Structured kernel subspace learning for autonomous robot navigation,” Sensors 18(2), 582 (2018).CrossRefGoogle ScholarPubMed
Dirik, M., “Collision-free mobile robot navigation using fuzzy logic approach,” Int. J. Comput. Appl. 179(9), 33–39 (2018).Google Scholar
Keshmiri, S. and Payandeh, S., “Multi-robots, Multi-locations Recharging Paradigm: A Regression Route Technique,” Proceedings of the 14th IASTED International Conference, Robotics and Applications, Cambridge, MA, USA (2009) pp. 160–165.Google Scholar
Keshmiri, S. and Payandeh, S., “Regression analysis of multi-rendezvous recharging route in multi-robot environment,” Int. J. Soc. Robot. 4(1), 15–27 (2012).CrossRefGoogle Scholar
Li, G., Yamashita, A., Asama, H. and Tamura, Y., “An Efficient Improved Artificial Potential Field Based Regression Search Method for Robot Path Planning,” 2012 International Conference on Mechatronics and Automation (ICMA), Chengdu, Sichuan, China, IEEE (2012), pp. 1227–1232.CrossRefGoogle Scholar
Li, G., Tamura, Y., Yamashita, A. and Asama, H., “Effective improved artificial potential field-based regression search method for autonomous mobile robot path planning,” Int. J. Mechatron. Autom. 3(3), 141–170 (2013).CrossRefGoogle Scholar
Lazaro, J. L., Gardel, A., Mataix, C., Rodriguez, F. J. and Martin, E., “Adaptive Workspace Modeling, Using Regression Methods, and Path Planning to the Alternative Guide of Mobile Robots in Environments with Obstacles,” 1999 7th IEEE International Conference on Emerging Technologies and Factory Automation, Barcelona, Spain, IEEE, vol. 1 (1999) pp. 529–534.Google Scholar
Dongre, V. and Raikwal, J., “An improved user browsing behavior prediction using regression analysis on Web Logs,” Int. J. Comput. Appl. 120(19), 19–23 (2015).Google Scholar
Kumar, P. B., Sahu, C. and R. Parhi, D., “A hybridized regression-adaptive ant colony optimization approach for navigation of humanoids in a cluttered environment,” Appl. Soft Comput. 68, 565–585 (2018).CrossRefGoogle Scholar
Kumar, P. B., Mohapatra, S. and R. Parhi, D., “An intelligent navigation of humanoid NAO in the light of classical approach and computational intelligence,” Comput. Animat. Virt. Worlds 30(12), e1858 (2018).CrossRefGoogle 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. Iran. 26(1), 375–393 (2019).Google Scholar
Kumar, P. B., Sethy, M. and R. Parhi, D., “An intelligent computer vision integrated regression based navigation approach for humanoids in a cluttered environment,” Multimedia Tools Appl. 1–24 (2018).Google Scholar
Al, S., Dülger, L. C. and Kirecci, A., “Hybrid actuator: Motion control using genetic algorithms,” Proc. Inst. Mech. Eng., Part C: J. Mech. Eng. Sci. 223(7), 1657–1665 (2009).CrossRefGoogle Scholar
Wang, S., Lu, Z., Wei, L., Ji, G. and Yang, J., “Fitness-scaling adaptive genetic algorithm with local search for solving the multiple depot vehicle routing problem,” Simulation 92(7), 601–616 (2016).CrossRefGoogle Scholar
Nagib, G. and Gharieb, W., “Path Planning for a Mobile Robot Using Genetic Algorithms,” International Conference on Electrical, Electronic and Computer Engineering, Cairo, Egypt (2004) pp. 185–189.Google Scholar
Raouf, N. and Pourtakdoust, S. H., “Launch vehicle multi-objective reliability-redundancy optimization using a hybrid genetic algorithm-particle swarm optimization,” Proc. Inst. Mech. Eng., Part G: J. Aerosp. Eng. 229(10), 1785–1797 (2015).CrossRefGoogle Scholar
Saraswathi, M., Murali, G. B. and Deepak, B. B. V. L., “Optimal path planning of mobile robot using hybrid cuckoo search-bat algorithm,” Procedia Comput. Sci. 133, 510–517 (2018).CrossRefGoogle Scholar
Singh, N. H. and Thongam, K., “Mobile robot navigation using fuzzy logic in static environments,” Procedia Comput. Sci. 125, 11–17 (2018).CrossRefGoogle Scholar
Zhang, X., Zhao, Y., Deng, N. and Guo, K., “Dynamic path planning algorithm for a mobile robot based on visible space and an improved genetic algorithm,” Int. J. Adv. Robot. Syst. 13(3), 91 (2016).Google Scholar
Tuncer, A. and Yildirim, M., “Dynamic path planning of mobile robots with improved genetic algorithm,” Comput. Electr. Eng. 38(6), 1564–1572 (2012).CrossRefGoogle Scholar
Allaire, F. C., Tarbouchi, M., Labonté, G. and Fusina, G., “FPGA Implementation of Genetic Algorithm for UAV Real-Time Path Planning,” In: Unmanned Aircraft Systems (Springer, Dordrecht, 2008) pp. 495–510.Google Scholar
Hu, L., Gu, Z. Q., Huang, J., Yang, Y. and Song, X., “Research and realization of optimum route planning in vehicle navigation systems based on a hybrid genetic algorithm,” Proc. Inst. Mech. Eng., Part D: J. Automobile Eng. 222(5), 757–763 (2008).CrossRefGoogle Scholar
Elshamli, A., Abdullah, H. A. and Areibi, S., “Genetic Algorithm for Dynamic Path Planning,” Canadian Conference on Electrical and Computer Engineering, Ontario, Canada, IEEE, vol. 2 (2004) pp. 677–680.Google Scholar
Kwaśniewski, K. K. and Gosiewski, Z., “Genetic algorithm for mobile robot route planning with obstacle avoidance,” Acta Mech. Autom. 12(2), 151–159 (2018).Google Scholar
Lamini, C., Benhlima, S. and Elbekri, A., “Genetic algorithm based approach for autonomous mobile robot path planning,” Procedia Comput. Sci. 127, 180–189 (2018).CrossRefGoogle Scholar
Bakdi, A., Hentout, A., Boutami, H., Maoudj, A., Hachour, O. and Bouzouia, B., “Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control,” Robot. Autonomous Syst. 89, 95–109 (2017).CrossRefGoogle Scholar
Silva Arantes, J. D., Silva Arantes, M. D., Motta Toledo, C. F., Júnior, O. T. and Williams, B. C., “Heuristic and genetic algorithm approaches for UAV path planning under critical situation,” Int. J. Artif. Intell. Tools 26(01), 1760008 (2017).CrossRefGoogle Scholar
Meléndez, A., Castillo, O., Valdez, F., Soria, J. and Garcia, M., “Optimal design of the fuzzy navigation system for a mobile robot using evolutionary algorithms,” Int. J. Adv. Robot. Syst. 10(2), 139 (2013).CrossRefGoogle Scholar
Hartjes, S. and Visser, H. G., “Efficient trajectory parameterization for environmental optimization of departure flight paths using a genetic algorithm,” Part G: J. Aerospace Eng. 231(6), 1115–1123 (2017).Google Scholar
Sachin, M. U. and Gaonkar, P., “Design, implementation and control of a humanoid robot for obstacle avoidance using 8051 Microcontroller,” IOSR J. Electron. Commun. Eng. 5(5), 40–50 (2013).CrossRefGoogle Scholar
Kim, J. Y., Park, I. W. and Oh, J. H., “Walking control algorithm of biped humanoid robot on uneven and inclined floor,” J. Intell. Robot. Syst. 48(4), 457–484 (2007).CrossRefGoogle Scholar
Hereid, A., Cousineau, E. A., Hubicki, C. M. and Ames, A. D., “3D Dynamic Walking with Underactuated Humanoid Robots: A Direct Collocation Framework for Optimizing Hybrid Zero Dynamics,” 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, IEEE (2016), pp. 1447–1454.CrossRefGoogle Scholar
Baskoro, A. S. and Priyono, M. G., “Design of Humanoid Robot Stable Walking Using Inverse Kinematics and Zero Moment Point,” 2016 International Electronics Symposium (IES), Denpasar, Indonesia, IEEE (2016), pp. 335–339.CrossRefGoogle Scholar
Lin, C. Y., Lee, K. F., Wang, H. C., Kuo, P. H., Ho, Y. F. and Li, T. H. S., “Design and Implementation of 3-DOF Dynamic Balancing Waist and Its Fuzzy Control for Adult-Sized Humanoid Robot,” 2014 Proceedings of the SICE Annual Conference (SICE), Sapporo, Japan, IEEE (2014), pp. 2133–2138.CrossRefGoogle Scholar
Inomata, K. and Uchimura, Y., “3DZMP-Based Control of a Humanoid Robot with Reaction Forces at 3-Dimensional Contact Points,” 2010 11th IEEE International Workshop on Advanced Motion Control, Nagaoka, Niigata, IEEE (2010), pp. 402–407.CrossRefGoogle Scholar
Kofinas, N., Orfanoudakis, E. and G., M. Lagoudakis, “Complete Analytical Inverse Kinematics for NAO,” 13th International Conference on Autonomous Robot Systems (Robotica), Lisbon, Portugal (2013) pp. 1–6.Google Scholar
Peterson, J. L., Petri Net Theory and the Modeling of Systems (Prentice-Hall, Englewood Cliffs, 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).CrossRefGoogle Scholar