Acquisition the profile of surfaces with complementary sensor fusion techniques

Abstract

This paper presents complementary sensor fusion techniques for the acquisition of the profile of surfaces with minimum error using low cost sensors ultrasonic sensors. These surfaces are composed by areas with different depths, corners and specular surfaces. To minimize the constraints of sonar sensors, it was developed dedicated software and hardware, as well as an empirical model was obtained from real data. This model is based in two proposed concepts: Points of Constant Depth (PCD) and Areas of Constant Depth (ACD). Having this sonar model in mind, four sensor fusion techniques are used separately to validate the PCDs and decide the ACDs: average and variance, fuzzy controller and heuristic method based in rules. In this work a PUMA 560 manipulator was equipped with a CCD video camera on the shoulder and four ultrasonic sensors on the wrist, to acquire data to model the geometry of the part’s surface, exploiting the mobility of the robot. The CCD camera view defines the working area, while the ultrasonic sensors enable the acquisition of the surface profile. For the acquisition of the profile of surfaces with a minimum error different and complementary sensor fusion techniques are implemented and applied separately, namely the average and variance, kalman filter, fuzzy controller and heuristic method based in rules.