A Terrain Classification Method for Quadruped Robots with Proprioception

Acquiring terrain information during robot locomotion is essential for autonomous navigation, gait selection, and trajectory planning. Quadruped robots, due to their biomimetic structures, demonstrate enhanced traversability over complex terrains compared to other robotic platforms. Furthermore, the internal sensors of quadruped robots acquire rich terrain-related data during locomotion across diverse terrains. This study investigates the relationship between terrain characteristics and quadruped robots based on proprioception sensor data, and proposes a simple, efficient, and motion-independent terrain classification method by integrating multiple sensor signals. The sensors referred to in the text only include the IMU sensor and joint encoders, which means that the method has a wide range of applicability while requiring sufficiently low hardware cost. The Convolutional Neural Network will serve as the backbone of the algorithm. In addition, the control command about its own control information will serve as supporting information to eliminate the impact of motion patterns on the results. Employing a multi-label classification algorithm, the complex terrains are classified by multiple physical feature labels like roughness, slippage, softness, and slope, which depict terrain attributes. A feature-labeled terrain dataset is established by abstracting diverse terrain features across various terrains. Unlike semantic labels (e.g., grassland, sand, gravel) that are comprehensible only to humans, feature labels provide a more helpful and precise terrain characterization, including broader terrain attributes.