An Approach of a Control System for Autonomous Driving Based on Artificial Vision Techniques and NAO Robot

  • Carlos CarrancoEmail author
  • Patricio Encalada
  • Javier Gavilanes
  • Gabriel Delgado
  • Marcelo V. Garcia
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1066)


The joint application of robotics and artificial vision for driving a car, it has been a very important study in recent years, since a small loss of concentration can cause the vehicle to deviate from its trajectory and move to the other lane or get off the road. The new applications for autonomous driving of a vehicle provide serenity in the different situations that the driver usually carries out in the routine journey or retention in a driving track. The present scientific article presents an NAO robot software architecture for autonomous driving of an electric car, this approach implements a system to control robot joints, trajectory correction based on people and track detection allowing successfully autonomous navigation.


NAO robot navigation Autonomous navigation algorithm Artificial vision Track detection Open CV techniques 



This work was financed in part by Universidad Tecnica de Ambato (UTA) and their Research and Development Department (DIDE) under project 1919-CU-P-2017.


  1. 1.
    Aguilera-Castro, D., Neira-Carcamo, M., Aguilera-Carrasco, C., Vera-Quiroga, L.: Stairs recognition using stereo vision-based algorithm in NAO robot. In: 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), pp. 1–6. IEEE, Pucon, October 2017.
  2. 2.
    Deng, G., Wu, Y.: Double lane line edge detection method based on constraint conditions Hough transform. In: 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), pp. 107–110. IEEE, Wuxi, October 2018.
  3. 3.
    Dong, E., Wang, D., Chen, C., Tong, J.: Realization of biped robot gait planning based on NAO robot development platform. In: 2016 IEEE International Conference on Mechatronics and Automation. pp. 1073–1077. IEEE, Harbin, August 2016.
  4. 4.
    Dudek, W., Banachowicz, K., Szynkiewicz, W., Winiarski, T.: Distributed NAO robot navigation system in the hazard detection application. In: 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 942–947. IEEE, Miedzyzdroje, August 2016.
  5. 5.
    Korkmaz, S.A., Akcicek, A., Binol, H., Korkmaz, M.F.: Recognition of the stomach cancer images with probabilistic HOG feature vector histograms by using HOG features. In: 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), pp. 000339–000342. IEEE, Subotica, September 2017.
  6. 6.
    Korkmaz, S.A., Binol, H., Akcicek, A., Korkmaz, M.F.: A expert system for stomach cancer images with artificial neural network by using HOG features and linear discriminant analysis: HOG\(\_\)lda\(\_\)ann. In: 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), pp. 000327–000332. IEEE, Serbia, September 2017.
  7. 7.
    Lim, K.H., Seng, K.P., Ang, L.M., Chin, S.W.: Lane detection and Kalman-based linear-parabolic lane tracking. In: 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, pp. 351–354. IEEE, Hangzhou (2009).
  8. 8.
    Munoz, J.M., Avalos, J., Ramos, O.E.: Image-driven drawing system by a NAO robot. In: 2017 Electronic Congress (E-CON UNI), pp. 1–4. IEEE, Lima, November 2017.
  9. 9.
    Truong, Q.B., Lee, B.R., Heo, N.G., Yum, Y.J., Kim, J.G.: Lane boundaries detection algorithm using vector lane concept. In: 2008 10th International Conference on Control, Automation, Robotics and Vision. pp. 2319–2325. IEEE, Hanoi, December 2008.
  10. 10.
    Weixing, L., Haijun, S., Feng, P., Qi, G., Bin, Q.: A fast pedestrian detection via modified HOG feature. In: 2015 34th Chinese Control Conference (CCC), pp. 3870–3873. IEEE, Hangzhou, July 2015.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Universidad Politecnica Salesiana, UPSQuitoEcuador
  2. 2.Escuela Politecnica del Chimborazo, ESPOCHRiobambaEcuador
  3. 3.Universidad del Azuay, UDACuencaEcuador
  4. 4.Universidad Tecnica de Ambato, UTAAmbatoEcuador
  5. 5.University of Basque Country, UPV/EHUBilbaoSpain

Personalised recommendations