Distance Estimation With a Long-Range Ultrasonic Sensor System

This paper presents the results of tests conducted with an ultrasonic proximity measurement system (composed of Polaroid 600 sensors and a Sonar Ranging Module SN28827), which is used in sensory subsystems in many mobile robots. The tests took into account distances ranging from 0.4 to 11 m. The analysis of the obtained measuring results created the basis for selecting a distance estimator that would guarantee the smallest measuring errors in the whole measuring range of the system. The research described was carried out in a closed room at constant environmental conditions so as to eliminate the influence of external factors on the measurement result. Each measuring series was composed of 100 measurements. For the best estimator chosen in such environment, the value of absolute measuring error never exceeded plusmn 0.03 m. Histograms that present the scatter of measurement results are also included. The minimal number of measurements necessary to achieve a reliable measurement with the selected distance estimator was determined. It is shown that in order to achieve a relative measuring error smaller than 1% with 0.99 probability there is a need to perform at least seven measurements. A proposition of the distance measuring procedure with the chosen estimator is presented. The analysis described in this paper helps to evaluate the reliability of measurements performed with ultrasonic sensors.

[1]  Viii Supervisor Sonar-Based Real-World Mapping and Navigation , 2001 .

[2]  James L. Crowley,et al.  Navigation for an intelligent mobile robot , 1985, IEEE J. Robotics Autom..

[3]  Robert V. Brill,et al.  Applied Statistics and Probability for Engineers , 2004, Technometrics.

[4]  Peter Krammer,et al.  Scene analysis with ultrasonic sensors , 2005, 2005 IEEE Conference on Emerging Technologies and Factory Automation.

[5]  H. Durrant-Whyte,et al.  Mobile vehicle navigation in unknown environments: a multiple hypothesis approach , 1995 .

[6]  Michael I. Jordan,et al.  Probabilistic Networks and Expert Systems , 1999 .

[7]  Lindsay Kleeman,et al.  An optimal sonar array for target localization and classification , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[8]  Wendelin Feiten,et al.  Field test of a navigation system: autonomous cleaning in supermarkets , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[9]  Michael Drumheller,et al.  Mobile Robot Localization Using Sonar , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Roman Kuc Forward model for sonar maps produced with the Polaroid ranging module , 2003, IEEE Trans. Robotics Autom..

[11]  C. E. Jensen,et al.  Instrumentation for time-resolved measurement of ultrasound velocity deviation , 1989 .

[12]  Ken Sasaki,et al.  Classification Of Objects' Surface By Acoustic Transfer Function , 1992, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Roderic A. Grupen,et al.  Feature detection and identification using a sonar-array , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[14]  Henrik I. Christensen,et al.  Triangulation-based fusion of sonar data with application in robot pose tracking , 2000, IEEE Trans. Robotics Autom..

[15]  W. Burgard,et al.  Markov Localization for Mobile Robots in Dynamic Environments , 1999, J. Artif. Intell. Res..

[16]  Billur Barshan,et al.  Differentiating Sonar Reflections from Corners and Planes by Employing an Intelligent Sensor , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Hans Wehn,et al.  Ultrasound-based robot position estimation , 1997, IEEE Trans. Robotics Autom..