Perception Maps for the Navigation of a Mobile Robot using Ultrasound Data Fusion

  • Vítor Santos
  • João G. M. Gonçalves
  • Francisco Vaz
Part of the Research Reports Esprit book series (ESPRIT, volume 1)


Despite appearing insufficient for robotics global navigation, sonar is still meaningful when short-range (local) navigation is concerned. Still, ultrasonic data interpretation is affected by problems such as specular reflections or sensor crosstalk. The first of these problems occurs quite often in navigating situations, and the reliance on a single sensor has little chance of acceptable success. A consequent possibility is using multiple sensors in appropriate geometric lay-outs to overcome this limitation. The paper focuses on the problem of local navigation by using special perception maps built after multi-sensorial ultrasonic data. The main properties of these maps are their topology and geometry. These properties are adapted to sensors characteristics, measurement reliability and spatial redundancy. This results in a non-Cartesian grid where the decision to consider a cell free or occupied comes from more than one sensor. These maps are built using exclusively real data directly from a 24-ultrasonic sensor array. For the effect, a neural network performs the mapping between ultrasonic data and cells’ occupancy. A 3-layer supervised learning network is trained with real data and gives as output the state of each grid cell. A room model is needed during the learning phase to generate the training set, and is no longer required during the operating phase. Large amounts of data are needed so most of the representative situations of navigation are covered in the training set. The networks converged slowly but very efficiently. After the training phase, all the presented patterns matched perfectly. The network is able to cope with most of the specular reflection situations. This is due to the inherent sensor multiplicity and network integrating capabilities. Changes in environment, such as obstacles in vehicle trajectory, are also detected, providing therefore an indication to the network generalisation properties. In a near future, the robot is expected to run autonomously based on these perception maps.


Mobile Robot Specular Reflection Ultrasonic Sensor Successive Node Occupancy Grid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    J. Gonialves, G. Campos, V. Santos, V. Sequeira, F. Silva - Mobile Robotics for the Surveillance of Fissile Materials Storage Areas: Sensors and Data Fusion, Edited by S. Pfleger, J. Gongalves, D. Vernon, Springer-Verlag, 1993.Google Scholar
  2. 2.
    ROBOSOFT - Robuter Users’s Manual, V3.1, August 1991.Google Scholar
  3. 3.
    Alberto Elfes - Sonar-Based Real-World Mapping and Navigation, IEEE Journal of Robotics and Automation, vol. RA-3, n. 3, June 1987.Google Scholar
  4. 4.
    J. Borenstein, Y. Koren - Noise Rejection for Ultrasonic Sensors in Mobile Robot Applications, Proc. of the IEEE Int. Conf. on Robotics and Automation - Nice, France, May 1992.Google Scholar
  5. 5.
    Alberto Elfes - Dynamic Control of Robot Perception Using Multi-Property Inference Grids, Proc. of the IEEE Int. Conf. on Robotics and Automation - Nice, France, May 1992.Google Scholar
  6. 6.
    Hans P. Moravec, Alberto Elfes - High Resolution Maps from Wide Angle Sonar, Proc. IEEE Int. Conference on Robotics and Automation, Washington D.C., pp. 116–121, March 1985.Google Scholar
  7. 7.
    Alberto Elfes - Using Occupancy Grids For Mobile Robot Perception and Navigation, IEEE Computer, pp. 46–57, June 1989.Google Scholar
  8. 8.
    Michael Drumheller - Mobile Robot Localization Using Sonar, IEEE Trans, on Pattern Analysis and Machine Intelligence, vol. 9, n. 2, March 1987.Google Scholar
  9. 9.
    A. Zelinsky - Environment Mapping with a Mobile Robot Using Sonar, Proc. of the Australian Joint Artificial Intelligence Conference - Al 88, pp. 373 - 388, November 1988.Google Scholar
  10. 10.
    R. P. Gorman, T. J. Sejnowski - Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets, Neural Networks, vol. 1, 1988.Google Scholar
  11. 11.
    D. A. Pomerleau - Efficient Training of Artificial Neural Networks for Autonomous Navigation, Neural Computation, vol. 3, n. 1, 1991.Google Scholar
  12. 12.
    J. Borenstein, Yoram Koren - Obstacle Avoidance with Ultrasonic Sensors, IEEE Journal of Robotics and Automation, vol. RA-4, n. 2, April 1988.Google Scholar
  13. 13.
    V. Santos, J. G. M. Gongalves, F. Vaz - Ultrasound Sensors for Environment Perception, JRC Technical Note No. 1. 93. 128, September 1993.Google Scholar
  14. 14.
    Eric B. Baum, David Haussler - What Size Net Gives Valid Generalization?, NIPS-88, 1988.Google Scholar
  15. 15.
    Shih-Chi Huang, Yih-Fang Huang - Bounds on the Number of Hidden Neurons in Multilayer Perceptrons, IEEE trans, on Neural Networks, vol. 2, n. 1, January 1991.Google Scholar
  16. 16.
    Gagan Mirchandani, Wei Cao - On Hidden Nodes for Neural Nets, IEEE trans, on Circuits and Systems, vol. 36, n. 5, May 1989.Google Scholar
  17. 17.
    D. E. Rumelhart, G. E. Hinton, R. J. Williams - Learning Representations by Back-propagation errors, Nature, n. 323, 9 October 1986.CrossRefGoogle Scholar
  18. 18.
    T. P. Vogl, J. K. Mangis, A. K. Rigler, W. T. Zink, D. L. Alkon - Accelerating the Convergence of the Back-Propagation Method, Biological Cybernetics, vol. 59, pp. 257–263, 1988.CrossRefGoogle Scholar

Copyright information

© ECSC-EC-EAEC, Brussels-Luxembourg 1995

Authors and Affiliations

  • Vítor Santos
    • 1
  • João G. M. Gonçalves
    • 1
  • Francisco Vaz
    • 2
  1. 1.Commission of the European Communities Joint, Research CentreIspra (VA)Italy
  2. 2.Universidade de Aveiro/INESCAveiroPortugal

Personalised recommendations