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Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach

Published online by Cambridge University Press:  15 February 2019

Animesh Chhotray Open the ORCID record for Animesh Chhotray [Opens in a new window]  and
Dayal R. Parhi
Animesh Chhotray*
Affiliation:
Robotics Laboratory, Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha 769008, India. E-mail: [email protected]
Dayal R. Parhi
Affiliation:
Robotics Laboratory, Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha 769008, India. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

The present paper discusses on development and implementation of back-propagation neural network integrated modified DAYANI method for path control of a two-wheeled self-balancing robot in an obstacle cluttered environment. A five-layered back-propagation neural network has been instigated to find out the intensity of various weight factors considering seven navigational parameters as obtained from the modified DAYANI method. The intensity of weight factors is found out using the neural technique with input parameters such as number of visible intersecting obstacles along the goal direction, minimum visible front obstacle distances as obtained from the sensors, minimum left side obstacle distance within the visible left side range of the robot, average of left side obstacle distances, minimum right side obstacle distance within the visible right side range of the robot, average of right side obstacle distances and goal distance from the robot’s probable next position. Comparison between simulation and experimental exercises is carried out for verifying the robustness of the proposed controller. Also, the authenticity of the proposed controller is verified through a comparative analysis between the results obtained by other existing techniques with the current technique in an exactly similar test scenario and an enhancement of the results is witnessed.

Keywords

Two-wheeled self-balancing robotNavigational controlModified DAYANIBack-propagation neural networkPath planning
Type
Articles
Information
Robotica , Volume 37 , Issue 8 , August 2019 , pp. 1346 - 1362
Copyright
© Cambridge University Press 2019 

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References

Raja, P. and Pugazhenthi, S., “Optimal path planning of mobile robots: A review,” Int. J. Phy. Sci. 7(9), 1314–1320 (2012).Google Scholar
Zhang, H., Butzke, J. and Likhachev, M., “Combining Global and Local Planning with Guarantees on Completeness,” IEEE International Conference on Robotics and Automation (ICRA), Saint Paul, MN, USA (2012) pp. 4500–4506.Google Scholar
Masehian, E. and Sedighizadeh, D., “Classic and heuristic approaches in robot motion planning-a chronological review,” World Acad. Sci. Eng. Technol. 29(1), 101–106 (2007).Google 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).CrossRefGoogle Scholar
Latombe, J. C., Robot Motion Planning, vol. 124 (Springer Science & Business Media, New York, 2012).Google Scholar
Garrido, S., Moreno, L., Abderrahim, M. and Martin, F., “Path Planning for Mobile Robot Navigation Using Voronoi Diagram and Fast Marching,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China (2006) pp. 2376–2381.Google Scholar
Kaluđer, H., Brezak, M. and Petroviæ, I., “A Visibility Graph Based Method for Path Planning in Dynamic Environments,” MIPRO, Proceedings of the 34th International Convention, Opatija, Croatia (2011) pp. 717–721.Google Scholar
Dugarjav, B., Lee, S. G., Quang, T. B., Gwak, K. W. and Lee, B., “Adaptive online cell decomposition with a laser range finder in unknown non-rectilinear environments,” Int. J. Precis. Eng. Manuf. 18(4), 487–495 (2017).CrossRefGoogle Scholar
Montiel, O., Sepúlveda, R. and Orozco-Rosas, U., “Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field,” J. Intell. Rob. Syst. 79(2), 237–257 (2015).CrossRefGoogle Scholar
Mac, T. T., Copot, C., Tran, D. T. and De Keyser, R., “Heuristic approaches in robot path planning: A survey,” Rob. Auton. Syst. 86, 13–28 (2016).CrossRefGoogle Scholar
Kim, D. H., “Escaping route method for a trap situation in local path planning,” Int. J. Control Autom. Syst. 7(3), 495–500 (2009).CrossRefGoogle Scholar
Ismail, A. T., Sheta, A. and Al-Weshah, M., “A mobile robot path planning using genetic algorithm in static environment,” J. Comput. Sci. 4(4), 341–344 (2008).Google Scholar
Kala, R., Shukla, A. and Tiwari, R., “Robotic path planning in static environment using hierarchical multineuron heuristic search and probability based fitness,” Neurocomputing 74(14–15), 2314–2335 (2011).CrossRefGoogle Scholar
Pradhan, S. K., Parhi, D. R. and Panda, A. K., “Fuzzy logic techniques for navigation of several mobile robots,” Appl. Soft Comput. 9(1), 290–304 (2009).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,” Arabian J. Sci. Eng. 43(12), 7869–7891 (2018).Google Scholar
Deepak, B. B. V. L., Parhi, D. R. and Raju, B. M. V. A., “Advance particle swarm optimization-based navigational controller for mobile robot,” Arabian J. Sci. Eng. 39(8), 6477–6487 (2014).CrossRefGoogle Scholar
Savkin, A. V. and Li, H., “A safe area search and map building algorithm for a wheeled mobile robot in complex unknown cluttered environments,” Robotica 36(1), 96–118 (2018).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,” Defence Technol. 13(1), 47–58 (2017).CrossRefGoogle 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).CrossRefGoogle Scholar
Parhi, D. R. and Kundu, S., “A hybrid fuzzy controller for navigation of real mobile robot,” Int. J. Appl. Artif. Intell. Eng. Syst. 3(1), 169 (2011).Google 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).CrossRefGoogle Scholar
Kumar, P. B., Pandey, K. K., Sahu, C., Chhotray, A. and Parhi, D. R., “A Hybridized RA-APSO Approach for Humanoid Navigation,” IEEE Nirma University International Conference on Engineering (NUiCONE), Ahmedabad, India (2017) pp. 1–6.Google Scholar
Parhi, D. R. and Singh, M. K., “Heuristic-rule-based hybrid neural network for navigation of a mobile robot,” Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 224(7), 1103–1118 (2010).CrossRefGoogle Scholar
Bassil, Y., “Neural network model for path-planning of robotic rover systems,” arXiv preprint arXiv:1204.0183 (2012).Google Scholar
Al-Sagban, M. and Dhaouadi, R., “Neural-Based Navigation of a Differential-Drive Mobile Robot,” 12th IEEE International Conference on Control Automation Robotics & Vision (ICARCV), Guangzhou, China (2012) pp. 353–358.Google Scholar
Luo, C., Gao, J., Li, X., Mo, H. and Jiang, Q., “Sensor-Based Autonomous Robot Navigation Under Unknown Environments with Grid Map Representation,” IEEE Symposium on Swarm Intelligence (SIS), Orlando, FL, USA (2014) pp. 1–7.Google Scholar
Boukens, M. and Boukabou, A., “Design of an intelligent optimal neural network-based tracking controller for nonholonomic mobile robot systems,” Neurocomputing 226, 46–57 (2017).CrossRefGoogle Scholar
Farias, G., Fabregas, E., Peralta, E., Vargas, H., Hermosilla, G., Garcia, G. and Dormido, S., “A neural network approach for building an obstacle detection model by fusion of proximity sensors data,” Sensors 18(3), 683 (2018).CrossRefGoogle ScholarPubMed
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).CrossRefGoogle Scholar
Singh, N. H. and Thongam, K., “Mobile robot navigation using MLP-BP approaches in dynamic environments,” Arabian J. Sci. Eng. 43(12), 8013–8028 (2018).CrossRefGoogle Scholar
Parhi, D. R. and Chhotray, A., “Development and analysis of DAYANI arc contour intelligent technique for navigation of two-wheeled mobile robot,” Ind. Rob. Int. J. 45(5), 688–702 (2018).CrossRefGoogle Scholar
Chan, R. P. M., Stol, K. A. and Halkyard, C. R., “Review of modelling and control of two-wheeled robots,” Annu. Rev. Control 37(1), 89–103 (2013).CrossRefGoogle Scholar
Maddahi, A., Shamekhi, A. H. and Ghaffari, A., “A Lyapunov controller for self-balancing two-wheeled vehicles,” Robotica 33(1), 225–239 (2015).CrossRefGoogle Scholar
Kim, H. W. and Jung, S., “Control of a two-wheel robotic vehicle for personal transportation,” Robotica 34(5), 1186–1208 (2016).CrossRefGoogle Scholar
Takei, T. and Imamura, R., “Baggage transportation and navigation by a wheeled inverted pendulum mobile robot,” IEEE Trans. Ind. Electron. 56(10), 3985–3994 (2009).CrossRefGoogle Scholar
Shu-Hua, P., Cui, H. and Deng-Hua, L., “Fuzzy Path Planning of Two-Wheeled Robot Optimized by Gold Mean,” In: Informatics in Control, Automation and Robotics (Springer, Berlin, Heidelberg, 2011) pp. 477–484.CrossRefGoogle Scholar
Mirzaeinejad, H. and Shafei, A. M., “Modeling and trajectory tracking control of a two-wheeled mobile robot: Gibbs–Appell and prediction-based approaches,” Robotica 36(10), 1551–1570 (2018).CrossRefGoogle Scholar
Lee, J. and Park, W., “Probability-Based Optimal Path Planning for Two-Wheeled Mobile Robots,” ASME 2015 Dynamic Systems and Control Conference, 2015, American Society of Mechanical Engineers, Columbus, OH, USA (2015) pp. V003T40A006-V003T40A006.Google Scholar
Ghani, N. A., Haur, L., Yon, T. and Naim, F., “Dual mode navigation for two-wheeled robot,” World Acad. Sci. Eng. Technol. 58, 278–283 (2011).Google Scholar
Chhotray, A., Pradhan, M. K., Pandey, K. K. and Parhi, D. R., “Kinematic Analysis of a Two-Wheeled Selfbalancing Mobile Robot,” Proceedings of the International Conference on Signal, Networks, Computing, and Systems, New Delhi, India (2016) pp. 87–93.CrossRefGoogle 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
Haykin, S., Neural Networks: A Comprehensive Foundation (Prentice Hall PTR, Upper Saddle River, NJ, USA, 1994).Google Scholar
Zhao, R., Lee, D. H. and Lee, H. K., “Mobile robot navigation using optimized fuzzy controller by genetic algorithm,” Int. J. Fuzzy Logic Intell. Syst. 15(1), 12–19 (2015).CrossRefGoogle Scholar
Aouf, A., Boussaid, L. and Sakly, A., “TLBO-based adaptive neurofuzzy controller for mobile robot navigation in a strange environment,” Comput. Intell. Neurosci. (2018). doi: 10.1155/2018/3145436.CrossRefGoogle Scholar