Adaptive control algorithm for quadruped robots in unknown high-slope terrain

Abstract

The capability to navigate high-slope terrains is crucial for quadruped robots engaged in field operations. We propose a novel terrain estimation and adaptation strategy tailored for facilitating quadruped robot locomotion on complex slope terrains. Our approach involves predicting terrain slopes by analyzing foot positions and IMU data, subsequently adjusting the robot’s body orientation and height in real-time to accommodate varying slope conditions. To effectively control the motion of quadruped robots on such challenging terrains, we introduce a multimodal motion control algorithm that integrates Model Predictive Control (MPC) with Quadratic Programming (QP) torque control. This combined approach ensures stable and efficient locomotion even on steep slopes. Furthermore, we present a state estimation method based on Kalman filtering, enabling accurate self-assessment by the robot without heavy reliance on visual sensors. In simulation validation, our quadruped robot achieved notable performance metrics. It achieved a forward speed of 0.7 m/s on slopes with angles up to 43∘ and demonstrated stable rotational capability at a speed of 2 rad/s on a 32∘ slope. When subjected to external force interference, the robot exhibited resilience, withstanding constant external forces of up to 60Nm and external torques of up to 35Nm on flat ground. Moreover, on a 30∘ slope, the robot maintained stable locomotion in the face of impulses reaching 64Nm ⋅ s along the x and y directions of its body coordinate system and 7Nm ⋅ s along the z-axis. This comprehensive strategy and experimental validation highlight the efficacy and robustness of our adaptive control algorithm, paving the way for enhanced performance of quadruped robots navigating high-slope terrains with unpredictable characteristics.