Robust dynamic walking using online foot step optimization

To enable robust dynamic walking on the Atlas robot, we extend our previous work by adding a receding-horizon component. The new controller consists of three hierarchies: a center of mass (CoM) trajectory planner that follows a sequence of desired foot steps, a receding-horizon controller that optimizes the next foot placement to minimize future CoM tracking errors, and an inverse dynamics based full body controller that generates instantaneous joint commands to track these motions while obeying physical constraints. An approximate value function is generated by the CoM planner, and is used to guide the foot placement and inverse dynamics optimizations. The proposed controller is implemented and tested on the Atlas robot. It is capable of walking with strong external perturbations such as recovering from large pushes and traversing unstructured terrain.

Authors

• Siyuan Feng 0003
• X. Xinjilefu 0001
• Christopher G. Atkeson
• Joohyung Kim

Keywords

• Foot
• Trajectory
• Legged locomotion
• Optimization
• Acceleration
• Dynamics
• Dynamic Walking
• Value Function
• Center Of Mass
• Tracking Error
• Model Predictive Control
• Inverse Dynamics
• Trajectory Planning
• Value Function Approximation
• Mass Trajectories
• Experimental Section
• Nonlinear Model
• Precise Control
• Coronal Plane
• Walking Speed
• Angular Momentum
• Term In Eq
• Quadratic Programming
• Trajectory Optimization
• Swing Phase
• Fast Walking
• Center Of Mass Velocity
• Nominal Trajectory
• Control Of Walking
• Double Support Phase
• Double Support
• Robot Experiments
• Large Tracks
• Strong Disturbance
• Accurate State Estimation
• Second Derivative