Terrain Classification Based on RNNs for Ground Robots Using a Custom Instrumented Wheel with Nano-Structured Sensors

Abstract This paper describes the development of a novel instrumented wheel with onboard sensors to estimate tire deformation, together with a recurrent neural network capable of classifying terrain types. We present a functional prototype manufactured using 3D printing and a nanostructuring process that integrates piezoresistive sensors directly into the structure of an airless wheel. This process enables precise acquisition of wheel–terrain interaction signals that are informative for terrain recognition. Using these signals, along with the robot’s linear velocity, we train a bidirectional GRU to distinguish concrete, gravel, and grass terrains. Experiments on a service ground robot traversing a 180 m circuit yield a test accuracy of 95.7%, with the confusion matrix indicating consistent performance across classes. The results suggest that embedded wheel sensing combined with sequence modeling can provide accurate terrain classification while reducing payload and integration complexity. The proposed sensing wheel and inference pipeline offer a compact basis for terrain-aware mobility and potential extensions such as slip detection and payload monitoring.