Multi-Terrain Classification for Legged Robots Using HistGradient Boosting Machine Learning Technique

The increasing adoption of legged robots for applications such as search and rescue, environmental monitoring, and planetary exploration presents unique challenges in navigating diverse and complex terrains. Accurate terrain classification is crucial for adaptive locomotion and reliable performance, yet existing approaches often suffer from limited generalizability and accuracy due to the inherent variability of terrains and sensor noise. This study proposes a multi-terrain classification framework leveraging the HistGradient Boosting (HGB) machine learning technique to address these challenges. The system utilizes a force sensor and IMU data from a quadruped robot to extract meaningful features for robust classification. The HistGradient Boosting model achieved the highest accuracy of 0.9931, highlighting its superior classification performance compared to k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF) with accuracy of 0.9410, 0.9878, and 0.9729, demonstrating its effectiveness in handling the complexities of multi-terrain environments.