We propose a method to detect and identify faults in wheeled mobile robots. The idea behind the method is to use adaptive estimation to predict the outcome of several faults, and to learn them collectively as a failure pattern. Models of the system behavior under each type of fault are embedded in multiple parallel Kalman filter (KF) estimators. Each KF is tuned to a particular fault and predicts, using its embedded model, the expected values for the sensor readings. The residual, the difference between the predicted readings (based on certain assumptions for the system model and the sensor models) and the actual sensor readings, is used as an indicator of how well each filter is performing. A backpropagation neural network processes this set of residuals as a pattern and decides which fault has occurred, that is, which filter is better tuned to the correct state of the mobile robot. The technique has been implemented on a physical robot and results from experiments are discussed.

An efficient strategy based on an artificial neural network is presented to enable a mobile robot to move autonomously and build a rough map of its environment. The paper describes a technique based on a neural network which uses data from sonars to recognize features online. Our method is systematic, simple and yields very good results. Moreover, it is generalizable in the sense that one can train the robot to recognize particular features of interest. We have implemented and tested this technique successfully on-board a mobile robot which uses a 68332 processor and does not have support for floating point arithmetic and file structure. Once trained, the robot can recognize the features on the fly and react to them appropriately.

A low cost strategy based on well calibrated odometry is presented for localizing mobile robots. The paper describes a two-step process for correction of 'systematic errors' in encoder measurements followed by fusion of the calibrated odometry with a gyroscope and GPS resulting in a robust localization scheme. A Kalman filter operating on data from the sensors is used for estimating position and orientation of the robot. Experimental results are presented that show an improvement of at least one order of magnitude in accuracy compared to the un-calibrated, un-filtered case. Our method is systematic, simple and yields very good results. We show that this strategy proves useful when the robot is using GPS to localize itself as well as when GPS becomes unavailable for some time. As a result robot can move in and out of enclosed spaces, such as buildings, while keeping track of its position on the fly.