Document Type : Methodologies


Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran.


In many applications of the robotics, the mobile robot should be guided from a source to a specific destination. The automatic control and guidance of a mobile robot is a challenge in the context of robotics. So, in current paper, this problem is studied using various machine learning methods. Controlling a mobile robot is to help it to make the right decision about changing direction according to the information read by the sensors mounted around waist of the robot. Machine learning methods are trained using 3 large datasets read by the sensors and obtained from machine learning database of UCI. The employed methods include (i) discriminators: greedy hypercube classifier and support vector machines, (ii) parametric approaches: Naive Bayes’ classifier with and without dimensionality reduction methods, (iii) semiparametric algorithms: Expectation-Maximization algorithm (EM), C-means, K-means, agglomerative clustering, (iv) nonparametric approaches for defining the density function: histogram and kernel estimators, (v) nonparametric approaches for learning: k-nearest neighbors and decision tree and (vi) Combining Multiple Learners: Boosting and Bagging. These methods are compared based on various metrics. Computational results indicate superior performance of the implemented methods compared to the previous methods using the mentioned dataset. In general, Boosting, Bagging, Unpruned Tree and Pruned Tree (θ = 10-7) have given better results compared to the existing results. Also the efficiency of the implemented decision tree is better than the other employed methods and this method improves the classification precision, TP-rate, FP- rate and MSE of the classes by 0.1%, 0.1%, 0.001% and 0.001%.


[1] D. M. Helmick, S. I. Roumeliotis, Y. Cheng, D. S. Clouse, M. Bajracharya, and L. H. Matthies, "Slip-compensated path following for planetary exploration rovers," Advanced Robotics, vol. 20, no. 11, pp. 1257-1280, Jan 2006.
[2] R. Manduchi, A. Castano, A. Talukder, and L. Matthies, "Obstacle detection and terrain classification for autonomous off-road navigation," Autonomous robots, vol.  18, no. 1, pp. 81-102, Jan 2005.
[3] P. Fabiani, V. Fuertes, A. Piquereau, R. Mampey, and F. Teichteil-Königsbuch, "Autonomous flight and navigation of VTOL UAVs: from autonomy demonstrations to out-of-sight flights," Aerospace Science and Technology, vol. 11, no. 2-3, pp. 183-193, March 2007.
[4] M. Knudson, and K. Tumer, "Adaptive navigation for autonomous robots," Robotics and Autonomous Systems, vol.  59, no. 6, pp. 410-420, Jun 2011.
[5] K. M. Krishna, and P. K. Kalra, "Spatial understanding and temporal correlation for a mobile robot," Spatial Cognition and Computation, vol.  2, no. 3, pp. 219-259, Sep 2000.
[6] G.A. Bekey, "Autonomous robots: from biological inspiration to implementation and control," MIT press, May 2005.
[7] M. D. Mucientes, L. Moreno, A. Bugarín, and S. Barro, "Design of a fuzzy controller in mobile robotics using genetic algorithms," Applied Soft Computing, vol.  7, no. 2, pp. 540-546, March 2007.
[8] S. F. Desouky, and H. M. Schwartz, "Genetic-based fuzzy logic controller for a wall-following mobile robot," in 2009 American Control Conference. Jun 2009,  pp. 3555-3560.
[9] A. L. Freire, G. A. Barreto, M. Veloso, and A. T. Varela, "Short-term memory mechanisms in neural network learning of robot navigation tasks: A case study,". in 2009 6th Latin American Robotics Symposium (LARS 2009). Oct 2009, pp. 1-6.
[10] A. Katsev, B. Yershova, R. Tovar, Ghrist, and S. M. LaValle, "Mapping and pursuit-evasion strategies M. for a simple wall-following robot," IEEE Transactions on robotics, vol.  27, no 1, pp. 113-128, Jan 2011.
[11] J. He, H. Gu, and Z. Wang, "Multi-instance multi-label learning based on Gaussian process with application to visual mobile robot navigation," Information Sciences, vol.  190, pp. 162-177, May 2012.
[12] Y.-L. Chen, J., Cheng, C., Lin, X.,Wu, Y., Ou, & Y. Xu, "Classification-based learning by particle swarm optimization for wall-following robot navigation," Neuro-computing, vol.  113, pp. 27-35, Aug 2013.
[13] T. Dash, S. R., Sahu, T., Nayak, and G. Mishra, "Neural network approach to control wall-following robot navigation," in 2014 IEEE International Conference on Advanced Communications, Control, and Computing Technologies. May 2014, pp. 1072-1076.
[14] T. Dash, T. Nayak, and R.R. Swain. "Controlling wall following robot navigation based on gravitational search and feed forward neural network," in Proceedings of the 2nd international conference on perception and machine intelligence. Feb 2015, pp. 196-200.
[15] T.-C. Lin, C. C. Chen, and C. J. Lin, "Wall-following and navigation control of mobile robot using reinforcement learning based on dynamic group artificial bee colony," Journal of Intelligent and Robotic Systems, vol.  92, no. 2, pp. 343-357, Oct 2018.
[16] S. M. J. Jalali, S. Ahmadian, A., Khosravi, S. Mirjalili, M. R. Mahmoudi, and S. Nahavandi, "Neuroevolution-based autonomous robot navigation: a comparative study," Cognitive Systems Research, vol.  62, pp. 35-43, Aug 2020.
[17] N. Islam, K. Haseeb, A. Almogren, I. U. Din, M. Guizani, & A. Altameem, "A framework for topological based map building: A solution to autonomous robot navigation in smart cities," Future Generation Computer Systems, vol.  111, pp. 644-653, Oct 2020.
[18] S. M. J.  Jalali, R., Hedjam, A. Khosravi, A. A. Heidari, S. Mirjalili, and S. Nahavandi, "Autonomous robot navigation using moth-flame-based neuroevolution," in Evolutionary Machine Learning Techniques. Springer. pp. 67-83, 2020.
[19] M. L. Lagunes, O., Castillo, J., Soria, and F. Valdez, "Optimization of a fuzzy controller for autonomous robot navigation using a new competitive multi-metaheuristic model," Soft Computing,  pp. 1-20, March 2021.
[20] S.  Tiwari, Y. Zheng, M. Pattinson, M. Campo-Cossio, R. Arnau, D. Obregon,... and J. Reyes,  "Approach for Autonomous Robot Navigation in Greenhouse Environment for Integrated Pest Management," in 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS). Apr 2020, pp. 1286-1294.
[21] S. M. J. Jalali, A. Khosravi, P. M. Kebria, R. Hedjam, & S.  Nahavandi, "Autonomous robot navigation system using the evolutionary multi-verse optimizer algorithm," in 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). Oct 2019, pp. 1869-1875.
[22] S. M. J.  Jalali, P. M., Kebria, A., Khosravi, K., Saleh, D., Nahavandi, and S. Nahavandi,  "Optimal autonomous driving through deep imitation learning and neuroevolution," in 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). Oct 2019, pp. 1215-1220.
[23] E. Alpaydin, "Introduction to machine learning," MIT press, March 2020.
[24] A. Frank, and A. Asuncion, "UCI machine learning repository," URL, p. 22, 2010.
[25] A.B. Musa, "Comparative study on classification performance between support vector machine and logistic regression," International Journal of Machine Learning and Cybernetics, vol.  4, no. 1, pp. 13-24. Feb 2013
[26] M. Sokolova, N. Japkowicz, and S. Szpakowicz. "Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation," in Australasian joint conference on artificial intelligence. Dec 2006, pp. 1015-1021.
[27] B. de Bragança Pereira, and C.A. de Bragança Pereira, "A likelihood aproach to diagnostic tests in clinical medicine," REVSTAT–Statistical Journal, vol. 3, no. 1, pp. 77-98. Jun 2005
[28] A. S. Glas, J. G. Lijmer, M. H., Prins, G. J. Bonsel, and P. M. Bossuyt, "The diagnostic odds ratio: a single indicator of test performance," Journal of clinical epidemiology, vol.  56, no. 11, pp. 1129-1135. Nov 2003
[29] P.E. Hart, D.G. Stork, and R.O. Duda, "Pattern classification," Wiley Hoboken, 2000.
[30] I. Hammad, K. El-Sankary, and J. Gu."A comparative study on machine learning algorithms for the control of a wall following robot," in 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). Dec 2019, pp. 2995-3000.
[31] M. Moradizirkohi, S. Izadpanah, (2017). “Direct adaptive fuzzy control of flexible-joint robots including actuator dynamics using particle swarm optimization,” Journal of AI and Data Mining, vol.  5, no. 1, pp. 137-147, March 2017.