A Neuro-Fuzzy System for Extracting Environment Features Based on Ultrasonic Sensors

In this paper, a method to extract features of the environment based on ultrasonic sensors is presented. A 3D model of a set of sonar systems and a workplace has been developed. The target of this approach is to extract in a short time, while the vehicle is moving, features of the environment. Particularly, the approach shown in this paper has been focused on determining walls and corners, which are very common environment features. In order to prove the viability of the devised approach, a 3D simulated environment has been built. A Neuro-Fuzzy strategy has been used in order to extract environment features from this simulated model. Several trials have been carried out, obtaining satisfactory results in this context. After that, some experimental tests have been conducted using a real vehicle with a set of sonar systems. The obtained results reveal the satisfactory generalization properties of the approach in this case.

[1]  Gonzalo Pajares,et al.  On combining classifiers through a fuzzy multicriteria decision making approach: Applied to natural textured images , 2009, Expert Syst. Appl..

[2]  Billur Barshan,et al.  Directional Processing of Ultrasonic Arc Maps and its Comparison with Existing Techniques , 2007, 2007 IEEE 15th Signal Processing and Communications Applications.

[3]  Yoshifumi Nishida,et al.  Improvement of position estimation of the ultrasonic 3D tag system , 2008, RO-MAN 2008 - The 17th IEEE International Symposium on Robot and Human Interactive Communication.

[4]  Hao Ying,et al.  General Takagi-Sugeno fuzzy systems are universal approximators , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[5]  Henrik I. Christensen,et al.  Localization and navigation of a mobile robot using natural point landmarks extracted from sonar data , 2000, Robotics Auton. Syst..

[6]  Billur Barshan,et al.  Performance Evaluation of Ultrasonic Arc Map Processing Techniques by Active Snake Contours , 2008, EUROS.

[7]  Roman Kuc,et al.  Physically Based Simulation Model for Acoustic Sensor Robot Navigation , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[9]  L. A. Zedeh.  Knowledge representation in fuzzy logic , 1989 .

[10]  J. Jaroš,et al.  Three-dimensional Measured Ultrasound Field , 2004 .

[11]  Lindsay Kleeman,et al.  Ultrasonic classification and location of 3D room features using maximum likelihood estimation - Part I , 1997, Robotica.

[12]  Juan A. Méndez,et al.  Obstacle avoidance for a mobile robot: A neuro-fuzzy approach , 2001, Fuzzy Sets Syst..

[13]  Lindsay Kleeman,et al.  Ultrasonic Autonomous Robot Localisation System , 1989, IEEE/RJS International Conference on Intelligent RObots and Systems.

[14]  Sandra Fillebrown,et al.  The MathWorks' MATLAB , 1996 .

[15]  Gonzalo Pajares,et al.  A Hopfield Neural Network for Image Change Detection , 2006, IEEE Transactions on Neural Networks.

[16]  Sushmita Mitra,et al.  Neuro-fuzzy rule generation: survey in soft computing framework , 2000, IEEE Trans. Neural Networks Learn. Syst..

[17]  Lotfi A. Zadeh,et al.  Knowledge Representation in Fuzzy Logic , 1996, IEEE Trans. Knowl. Data Eng..

[18]  John J. Leonard,et al.  Directed Sonar Sensing for Mobile Robot Navigation , 1992 .

[19]  Billur Barshan,et al.  Objective Error Criterion for Evaluation of Mapping Accuracy Based on Sensor Time-of-Flight Measurements , 2008, Sensors.

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

[21]  Billur Barshan,et al.  Surface Profile Determination from Multiple Sonar Data Using Morphological Processing , 1999, Int. J. Robotics Res..

[22]  Joachim Hertzberg,et al.  Globally consistent 3D mapping with scan matching , 2008, Robotics Auton. Syst..

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

[24]  R. W. Wall,et al.  Creating a low-cost autonomous vehicle , 2002, IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02.

[25]  P. G. Auran,et al.  Underwater sonar range sensing and 3d image formation , 1996 .

[26]  Chih-Hao Chen,et al.  Design and experimental study of an ultrasonic sensor system for lateral collision avoidance at low speeds , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[27]  Wolfgang D. Rencken,et al.  Concurrent localisation and map building for mobile robots using ultrasonic sensors , 1993, Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93).

[28]  Pere Ridao,et al.  SLAM using an Imaging Sonar for Partially Structured Underwater Environments , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[29]  Howie Choset,et al.  Arc carving: obtaining accurate, low latency maps from ultrasonic range sensors , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[30]  John J. Leonard,et al.  Robust Mapping and Localization in Indoor Environments Using Sonar Data , 2002, Int. J. Robotics Res..