April 2004, Issue 165

Mini Rover 7
Electronic Compassing fo Mobile Robotics

HEADING DETERMINATION

For centuries, we have been taking advantage of the Earth’s magnetic field to orient ourselves with respect to its magnetic poles. Both the mechanical needle compass and electronic compass can provide absolute heading information. It is hard to beat the compass for this purpose. With the exception of GPS, other systems that exist require an external heading reference as calibration.

Gyroscopes use mechanical angular momentum changes to measure angular and linear movements. Traditional flywheel gyroscopes are fast-spinning gimbaled flywheels with encoders. The encoders are situated about the pivotal axes of the gyroscope’s gimbals and register angular movement of the spatially stable flywheels to its base, which is fastened to a host vessel. Modern gyroscopes use micro electromechanical systems (MEMS) and optical technologies in place of the bulky flywheels.

Gyroscopes have fast response times and are insensitive to magnetic anomalies. They are also relative angular position sensors, which require an external reference heading to initially set. A special kind of gyroscope called the gyrocompass can align itself with the Earth’s rotational axis, but it tends to be a large and costly instrument.

Differential wheel encoding is another technique used to determine heading. Relative heading changes can be computed by taking the difference of distance traveled by two opposing wheels. This technique has the same traction and terrain issues associated with the aforementioned wheel encoder odometry.

A single-antenna GPS can provide heading information, but it is not instantaneous. It inherently lags the movement of the robot or vehicle because the derived heading requires previous position data. A GPS could not tell you where you are heading if you were to stop and change directions. Like compasses, GPS receivers do not require external reference heading calibration. Once moving, the GPS heading update rate is a maximum of approximately 1 Hz, although some receivers add damping, which increases this time constant even more. A dual-antenna GPS receiver can provide instantaneous heading—or yaw—information, although the recommended distance between the two antennas is 1 m. This fact, along with its large price tag, can be a limiting factor for many mobile robot applications.

A combination of techniques is the best approach. There are many ways to determine heading, each of which has its own strengths and weaknesses. None of them are infallible. For this reason, some systems use two or more methods cooperatively to increase system accuracy and reliability. The deciding factors are cost, accuracy, efficiency, features, availability, ease of use, speed, and size.

Kalman filters are typically used to integrate the data from multiple sensors to produce a more reliable and accurate heading. Kalman filtering is a statistical method that combines the dynamic model of the system with the statistical behavior of system errors. It enables navigation systems to handle periodic GPS signal interruption, odometer slippage, magnetic anomalies, and other sensor irregularities with minimal degradation of accuracy. Kalman filters also can be extremely complicated. You must fully understand the dynamic behavior of your systems and the statistical and systemic errors of your sensors in order to make proper use of Kalman filters. It might be easier and more feasible in less demanding projects to use other software-based analytical tools like averaging, weighted averaging, limiting, and majority voting to improve heading data reliability.