Model Predictive Control With Environment Adaptation for Legged Locomotion

Re-planning in legged locomotion is crucial to track the desired user velocity while adapting terrain and rejecting external disturbances. In this work, we propose test experiments a real-time Nonlinear Model Predictive Control (NMPC) tailored robot for achieving dynamic on variety of terrains. We introduce mobility-based criterion define an NMPC cost that enhances quadruped robots maximizing leg mobility improves adaptation features. Our based iteration scheme allows us re-plan online at 25 Hz with prediction horizon 2 seconds.We use single rigid body model defined center mass frame order increase computational efficiency. simulations, tested traverse set pallets different sizes, walk into V-shaped chimney, locomote over rough terrain. real experiments, demonstrate effectiveness our feature allowed IIT’s 87 kg HyQ achieve omni-directional flat terrain, static pallet, adapt repositioned pallet during walk.