Indoor Mapping with Machine Learning Algorithm using Khepera III Mobile Robot

Authors

  • Norhidayah Mohamad Yatim Department of Computer Engineering, Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia. Department of System Engineering, Faculty of Electrical Engineering, Universiti Teknologi Mara (UiTM), Malaysia.
  • Norlida Buniyamin Department of System Engineering, Faculty of Electrical Engineering. Universiti Teknologi Mara (UiTM), Malaysia

Keywords:

Indoor Mapping, Khepera III, Machine Learning, Occupancy Grid Map,

Abstract

Small robots can be beneficial in many applications as they have the advantage of reaching small spaces. For these robots to be truly autonomous, ability to map their surrounding is essential. Accuracy of mapping is related closely to sensor’s precision. However, small robots can only be equipped with small sensor that is typically has noisy characteristic with cheaper cost, such as sonar sensor and infrared sensor. To enhance the quality of map build by noisy and low-cost sensor, machine learning algorithm integration is a good approach. In this work, multiple learners, which are Naïve Bayes, Decision Tree, Neural Network and AdaBoost, were experimented with occupancy grid map algorithm using Khepera III robot platform. Then, the results of their fitness score according to the maps build were compared. The results show that Neural Network performed the best with the occupancy grid map algorithm.

Author Biography

Norlida Buniyamin, Department of System Engineering, Faculty of Electrical Engineering. Universiti Teknologi Mara (UiTM), Malaysia

Associate Prof. at Faculty of Engineering, UiTM Shah Alam

References

N. Correll and A. Martinoli, “Multirobot inspection of industrial machinery,” Robot. Autom. Mag. IEEE, vol. 16, no. 1, pp. 103–112, 2009.

F. C. Vaussard, “A Holistic Approach to Energy Harvesting for Indoor Robots,” 2015.

N. Shimoi, Y. Takita, and H. Madokoro, “Development of a wheel robot and micro fling robot using for rescue scenarios,” Am. J. Remote Sens., vol. 1, pp. 61–66, 2003.

A. A. Makarenko, S. B. Williams, F. Bourgault, and H. F. Durrant-Whyte, “An experiment in integrated exploration,” in Intelligent Robots and Systems, 2002. IEEE/RSJ International Conference on, 2002, vol. 1, pp. 534–539.

F. Abrate, B. Bona, and M. Indri, “Experimental EKF-based SLAM for Mini-rovers with IR Sensors Only.,” in European Conference on Mobile Robots, 2007.

C. M. Gifford, R. Webb, J. Bley, D. Leung, M. Calnon, J. Makarewicz, B. Banz, and A. Agah, “Low-cost multi-robot exploration and mapping,” in Technologies for Practical Robot Applications, 2008. TePRA 2008. IEEE International Conference on, 2008, pp. 74–79.

S. Magnenat, V. Longchamp, M. Bonani, P. Rétornaz, P. Germano, H. Bleuler, and F. Mondada, “Affordable slam through the co-design of hardware and methodology,” in Robotics and Automation (ICRA), 2010 IEEE International Conference on, 2010, pp. 5395–5401.

P. Tarquino and K. Nickels, “Programming an E-Puck Robot to Create Maps of Virtual and Physical Environments,” in Robot Intelligence Technology and Applications 2, Springer, 2014, pp. 13–28.

J. D. Tardós, J. Neira, P. M. Newman, and J. J. Leonard, “Robust mapping and localization in indoor environments using sonar data,” Int. J. Rob. Res., vol. 21, no. 4, pp. 311–330, 2002.

T. N. Yap and C. R. Shelton, “SLAM in large indoor environments with low-cost, noisy, and sparse sonars,” in IEEE International Conference on Robotics and Automation (ICRA), 2009., 2009, pp. 1395–1401.

J. J. Leonard and H. F. Durrant-Whyte, Directed sonar sensing for mobile robot navigation, vol. 448. Kluwer Academic Publishers Dordrecht, 1992.

Y.-H. Choi, T.-K. Lee, and S.-Y. Oh, “A line feature based SLAM with low grade range sensors using geometric constraints and active exploration for mobile robot,” Auton. Robots, vol. 24, no. 1, pp. 13–27, 2008.

S. Thrun, “Learning metric-topological maps for indoor mobile robot navigation,” Artif. Intell., vol. 99, no. 1, pp. 21–71, 1998.

Y.-S. Ha and H.-H. Kim, “Environmental map building for a mobile robot using infrared range-finder sensors,” Adv. Robot., vol. 18, no. 4, pp. 437–450, 2004.

A. Prorok, A. Arfire, A. Bahr, J. R. Farserotu, and A. Martinoli, “Indoor navigation research with the Khepera III mobile robot: An experimental baseline with a case-study on ultra-wideband positioning,” in Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on, 2010, pp. 1–9.

N. M. Yatim and N. Buniyamin, “Development of Rao-Blackwellized Particle Filter (RBPF) SLAM algorithm using Low Proximity Infrared Sensors,” in The 9th International Conference on Robotics, Vision, Signal Processing & Power Applications, 2016.

H. P. Moravec, “Sensor Fusion in Certainty Grids for Mobile Robots,” AI Magazine, vol. 9, pp. 61–74, 1988.

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Published

2016-12-07

How to Cite

Mohamad Yatim, N., & Buniyamin, N. (2016). Indoor Mapping with Machine Learning Algorithm using Khepera III Mobile Robot. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(9), 61–66. Retrieved from https://jtec.utem.edu.my/jtec/article/view/881

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