Distance Sensor Data Integration and Prediction

  • Authors:

  • Zoe Falomir;M. Teresa Escrig;Juan Carlos Peris;Vicente Castelló

  • Affiliations:

  • Universitat Jaume I, Engineering and Computer Science Department, Campus Riu Sec, Castellón, E-12071 (Spain), zfalomir@icc.uji.es, escrigm@icc.uji.es, castellv@guest.uji.es;Universitat Jaume I, Engineering and Computer Science Department, Campus Riu Sec, Castellón, E-12071 (Spain), zfalomir@icc.uji.es, escrigm@icc.uji.es, castellv@guest.uji.es;Universitat Jaume I, Languages and Computer Science Systems Department, Campus Riu Sec, Castellón, E-12071 (Spain), jperis@lsi.uji.es;Universitat Jaume I, Engineering and Computer Science Department, Campus Riu Sec, Castellón, E-12071 (Spain), zfalomir@icc.uji.es, escrigm@icc.uji.es, castellv@guest.uji.es

  • Venue:
  • Proceedings of the 2007 conference on Artificial Intelligence Research and Development
  • Year:
  • 2007

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Abstract

In this paper we describe an approach to sensor data integration in order to obtain a qualitative and robust interpretation of the robot environment, which could be used as input in the qualitative robot navigation algorithms developed by our group. This approach consists of: obtaining patterns of distance zones from the sensor readings; comparing statically these patterns in order to detect non-working sensors; integrating the patterns obtained by each kind of sensor in order to obtain a final pattern which detects obstacles of any sort; predicting how the distance pattern will be influenced by robot motion and comparing this prediction with reality in order to detect incoherent sensorimotor situations. This approach has been applied to a real Pioneer 2 robot and promising results are obtained.