Trajectory generation for multi-contact momentum-control
This addresses the problem of enabling humanoid robots to perform more complex locomotion tasks beyond flat ground, though it appears incremental as an extension of previous LQR work.
The paper tackled the limitation of simplified models like LIPM for complex bipedal tasks by using full momentum equations in trajectory optimization to plan center of mass, linear and angular momentum trajectories, and contact forces for arbitrary locations, resulting in computationally fast plans for locomotion on complex terrains with good tracking performance in simulations.
Simplified models of the dynamics such as the linear inverted pendulum model (LIPM) have proven to perform well for biped walking on flat ground. However, for more complex tasks the assumptions of these models can become limiting. For example, the LIPM does not allow for the control of contact forces independently, is limited to co-planar contacts and assumes that the angular momentum is zero. In this paper, we propose to use the full momentum equations of a humanoid robot in a trajectory optimization framework to plan its center of mass, linear and angular momentum trajectories. The model also allows for planning desired contact forces for each end-effector in arbitrary contact locations. We extend our previous results on LQR design for momentum control by computing the (linearized) optimal momentum feedback law in a receding horizon fashion. The resulting desired momentum and the associated feedback law are then used in a hierarchical whole body control approach. Simulation experiments show that the approach is computationally fast and is able to generate plans for locomotion on complex terrains while demonstrating good tracking performance for the full humanoid control.