Abstract
ROBOG: The Robo-Guide is an autonomously navigating vehicle capable of learning the navigational directions of a locality by using Artificial Neural Networks. The main task of ROBOG is to guide people from one location to any other location in a trained region. The prime feature of ROBOG is its simplicity of implementation and working. The map information is learned by Artificial Neural Network using the proposed concept of branch and node. The Multi-Layered Perceptron is trained using the standard Error Back Propagation Algorithm. Road Detection & Tracking and Destination Identification are employed to achieve autonomous navigation. All the Image Processing techniques used are computationally inexpensive. The ROBOG is tested successfully in the outdoor environment for autonomous navigation and due to the simplicity in implementation it can be easily trained for any region.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Kim, G., Chung, W., Kim, K.-R., Kim, M., Han, S., Shinn, R.H.: The autonomous tour-guide robot Jinny. In: Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2004), vol. 4, pp. 3450–3455. IEEE (2004)
Thrun, S., Bennewitz, M., Burgard, W., Cremers, A.B., Dellaert, F., Fox, D.: MINERVA: a second-generation museum tour-guide robot. In: IEEE International Conference on Robotics and Automation, Detroit, Michigan, USA, pp. 1999–2005 (1999)
Willeke, T., Kunz, C., Nourbakhsh, I.R.: The history of the mobot museum robot series: an evolutionary study. In: FLAIRS Conference, pp. 514–518 (2001)
Graf, B., Barth, O.: Entertainment robotics: examples, key technologies and perspectives. In: IEEE/RSJ International Conference on Intelligent Robots and Systems - Workshop Robots in Exhibitions (2002)
Jensen, B., Froidevaux, G., Greppin, X., Lorotte, A., Mayor, L., Meisser, M., Ramel, G., Siegwart, R.: The interactive autonomous mobile system RoboX. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland, pp. 1221–1227 (2002)
Schulte, J., Rosenberg, C., Turun, S.: Spontaneous, short-term interaction with mobile robots. In: IEEE International Conference on Robotics and Automation, Detroit, Michigan, USA, pp. 1999–2005 (1999)
Harish, Y., Kranthi Kumar, R., Feroz, G.M.D., Jada, C., Anil Kumar, V., Mesa, M.: ROBOG: robo guide with simple learning strategy. In: Students’ Technology Symposium, pp. 224–228. IEEE (2014)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, Upper Saddle River (1994)
Yegnanarayana, B.: Artificial Neural Networks. PHI Learning Pvt. Ltd., New Delhi (2009)
Alvarez, J.M.A., Lopez, A.M.: Road detection based on illuminant invariance. IEEE Trans. Intell. Transp. Syst. 12(1), 184–193 (2011)
He, Y., Wang, H., Zhang, B.: Color-based road detection in urban traffic scenes. IEEE Trans. Intell. Transp. Syst. 5(4), 309–318 (2004)
Rotaru, C., Graf, T., Zhang, J.: Color image segmentation in HSI space for automotive applications. J. Real-Time Image Proces. 3(4), 311–322 (2008)
López, A., Serrat, J., Canero, C., Lumbreras, F., Graf, T.: Robust lane markings detection and road geometry computation. Int. J. Automot. Technol. 11(3), 395–407 (2010)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. Comput. Vis. Image Underst. (CVIU) 110(3), 346–359 (2008)
Gonzalez, R.C., Woods, R.E.: Digital image processing (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Rachavarapu, K.K., Gramoni Mohammed, I.F., Jada, C., Yenala, H., Vadathya, A.K. (2015). ROBOG Autonomously Navigating Outdoor Robo-Guide. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_70
Download citation
DOI: https://doi.org/10.1007/978-3-319-20294-5_70
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20293-8
Online ISBN: 978-3-319-20294-5
eBook Packages: Computer ScienceComputer Science (R0)