The typical use of ultrasonic sensors has been limited to estimation of the location of targets in a robot workspace. CTFM sonars have also been used successfully in classifying primitive targets. In this paper the classification is extended to include textures typical of these found in pathways the robot may need to follow or identify. The pathway classes examined are considered to be plane surfaces of various roughness corresponding to hard smooth floor, carpet, and asphalt. Each class is modeled using an extension of the Kirchhoff approximation method describing the scattering of the acoustic wave on rough surfaces. The CTFM sonar image corresponding to each class is derived and compared with the experimental one. Then a feature is extracted that exploits the differences between the three surface models. A neural network is trained for recognition with excellent results.

Ultrasonic sensors have been widely used as time-of-flight range finding systems in mobile robots. Different variations of this scheme lead to robust identification of simple reflector types, like walls, corners and edges. In this paper an alternative approach is attempted, able not only to locate and identify simple reflectors, but to detect and recognize more complicated objects. A more sophisticated sensor, the CTFM sonar, produces an image which corresponds to a range map. The image provides information about the location and type of the reflector. A reflectivity model for planes, corners, and edges is presented and compared with some experimental results. A method to distinguish well structured reflectors from complex objects is then described and the application of the system in a room mapping task is demonstrated.