This paper introduces a novel method for wheel-slippage detection and correction based on motor current measurements. Our proposed method estimates wheel slippage from motor current measurements, and adjusts encoder readings affected by wheel slippage accordingly. The correction of wheel slippage based on motor currents works only in the direction of motion, but not laterally, and it requires some knowledge of the terrain. However, this knowledge does not have to be provided ahead of time by human operators. Rather, we propose three tuning techniques for determining relevant terrain parameters automatically, in real time, and during motion over unknown terrain. Two of the tuning techniques require position ground truth (i.e., GPS) to be available either continuously or sporadically. The third technique does not require any position ground truth, but is less accurate than the two other methods. A comprehensive set of experimental results have been included to validate this approach

Most mobile robots use a combination of absolute and relative sensing techniques for position estimation. Relative positioning techniques are generally known as dead-reckoning. Many systems use odometry as their only dead-reckoning means. However, fiber optic gyroscopes have become more affordable and are being used on many platforms to supplement odometry, especially in indoor applications. Still, if the terrain is not level (i.e., rugged or rolling terrain), the tilt of the vehicle introduces errors into the conversion of gyro readings to vehicle heading. In order to overcome this problem vehicle tilt must be measured and factored into the heading computation. The paper introduces a new fuzzy logic expert rule-based navigation (FLEXnav) method for fusing data from multiple low- to medium-cost gyroscopes and accelerometers in order to estimate accurately the heading and tilt of a mobile robot. Experimental results of mobile robot runs over rugged terrain are presented, showing the effectiveness of our FLEXnav method.

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.