A terrain classification method based on wheel-terrain contact force

Abstract

Terrain classification is essential for accurately identifying the terrain and providing valuable information for controlling, planning, and navigation algorithms of wheeled vehicle. A novel terrain classification algorithm based on wheel-terrain interaction is proposed for wheeled vehicles in this paper. Unlike conventional terrain classification methods that rely on chassis acceleration signals, chassis gyroscope signals, motor current signals, images, or 3D points, the proposed approach utilizes wheel force to determine the type of terrain. Three classifiers which were one-dimensional Convolutional Neural Networks (1D-CNN), Long Short-Term Memory networks (LSTM), and Support Vector Machines (SVM) are employed to classify the terrain types. A measurement database was established using a vehicle test system equipped with a wheel force sensor. From this database, various datasets were constructed based on different wheel force, processing window sizes, and overlap times. Comparative tests were conducted across these datasets. The results indicate that the wheel forces F_x and F_z along with torque M_y are more effective for terrain classification purposes. Furthermore, 1D-CNN demonstrated superior performance compared to LSTM and SVM in most datasets. Additionally, the experiments revealed that larger processing window sizes and overlap times tended to enhance classification accuracy, however careful consideration must be given in practice.