This paper describes the use of support vector machine (SVM) classifier for real-time terrain estimation to improve autonomous navigation of tracked mobile robots. Real-time terrain identification and terrain property estimation has been explored previously for application on a wide range of systems from planetary exploration rovers to commercial vehicles like cars and trucks. A majority of the existing methods rely on the use of dedicated sensors including vibration sensors, accelerometers, cameras, LIDAR, etc., which makes them susceptible to the failure modes of each of these sensors. This work proposes a method for real-time classification of different terrain types based on the state evolution of a ground robot, specifically the measured change in the pose of the robot for a known control input. By using a trained SVM to perform terrain estimation based on the collected state evolution data, the proposed method does not require dedicated sensing modalities solely for the terrain estimation. In addition, this method is generally applicable in all conditions where the robot can traverse. The training data was obtained from four different terrain conditions including vinyl flooring, asphalt, artificial turf, and grass–gravel, to train the SVM to perform terrain estimation. The proposed technique is validated using a skid-steer tracked robot over multiple simulated and real terrain transitions cases, where the response to control inputs is significantly affected by terrain characteristics. The results show that the proposed method provides greater than 80% accuracy in all cases, with fast detection of terrain transitions. The paper concludes with a detailed description on the application of real-time terrain estimation in improving autonomous navigation.
Mobile robot, Estimation, Classification, Real-time systems, Navigation
You'll find it here: https://www.sciencedirect.com/science/article/abs/pii/S0957415819300935