Fiber optic gyros with very low drift rates have become available and affordable. Because of their low drift rate attention is warranted to sources of errors that were previously considered as of secondary importance. In the KVH E-Core RD2100 gyros, we found that the temperature dependency and the non-linearity of the scale-factor caused significant errors. A precision calibration procedure, described in the paper, reduces the resulting errors by one order of magnitude. In addition to the calibration procedure, we integrated the gyro with the odometry system of a four-wheel drive, skid-steer Pioneer AT mobile robot by means of an indirect feedback Kalman filter that fuses the sensor data from both sensor modalities. Based on our experimental results the paper compares the relative effectiveness of both enhancements (precision gyro calibration and Kalman filter).

This paper describes two methods aimed at improving dead-reckoning accuracy with fiber-optic gyroscopes (FOGs) in mobile robots. The first method is a precision calibration procedure for FOGs, which effectively reduces the ill effects of nonlinearity of the scale-factor and temperature dependency. The second method is the implementation of an indirect feedback Kalman filter that fuses the sensor data from the FOG with the odometry system of the mobile robot. The paper also provides experimental results and compares the relative effectiveness of the two methods as implemented on a four-wheel drive/skidsteer Pioneer AT mobile robot.

Fiber-optics gyroscopes (gyros) are gaining importance as a means for improving dead-reckoning accuracy in mobile robots. In the past, the relatively high drift rate of moderately priced gyros presented the foremost technical limitation of these devices. More recently, fiber-optics gyros with very low drift rates have become available and affordable. Because of their low drift rate attention is warranted to sources of errors that were previously considered as of secondary importance. In the KVH E-Core RD2100 gyros that were examined at our lab we found that the nonlinearity of the scale-factor and temperature dependency caused significant errors. A calibration method, described in the paper, reduces the resulting errors by one order of magnitude.

In this paper, the point stabilization of mobile robots via state space exact feedback linearization is presented. The state space exact feedback linearization has not been possible for the point stabilization of mobile robots due to the restricted mobility caused by nonholonomic constraints. Under our proposed coordinates, however, the point stabilization problem can be exactly transformed into the problem of controlling a linear time invariant system. Thus, using well-established linear control theory, the point stabilization of robots can be easily formulated.

This paper describes a methodol- ogy of world modelling and path planning for a mobile robot. The world model, called a cer- tainty grid for obstacle representation,is ob- tained from readings of ultrasonic sensors and more accurate world model is built by using the information of neighboring sonar sensors. A linear programming method for the short- est paths from many origins to a destination is proposed. The global path is generated with off-line calculation but the local paths are ob- tained on-line by table look-up method. It is implemented on a physical mobile robot. The results show that the certainty grid which is updated by our proposed method well repre- sents a precise environment including the loca- tion of obstacles. The robot was enable to nav- igate to a destination avoiding obstacles with the path which is calculated by the proposed linear programming method.