One of the essential aspects of humanoid robot running is determining the limb-swinging trajectories. During the flight phases, where the ground reaction forces are not available for regulation, the limb swinging trajectories are significant for the stability of the next stance phase. Due to the conservation of angular momentum, improper leg and arm swinging results in highly tilted and unsustainable body configurations at the next stance phase landing. In such cases, the robotic system fails to maintain locomotion independent of the stability of the center of mass trajectories. This problem is more apparent for fast and high flight time trajectories. This paper proposes a real-time nonlinear limb trajectory optimization problem for humanoid running. The optimization problem is tested on two different humanoid robot models, and the generated trajectories are verified using a running algorithm for both robots in a simulation environment.

This paper studies the experimental comparison of two different whole-body control formulations for humanoid robots: inverse dynamics whole-body control (ID-WBC) and passivity-based whole-body control (PB-WBC). The two controllers fundamentally differ from each other as the first is formulated in task acceleration space and the latter is in task force space with passivity considerations. Even though both control methods predict stability under ideal conditions in closed-loop dynamics, their robustness against joint friction, sensor noise, unmodeled external disturbances, and non-perfect contact conditions is not evident. Therefore, we analyze and experimentally compare the two controllers on a humanoid robot platform through swing foot position and orientation control, squatting with and without unmodeled additional weights, and jumping. We also relate the observed performance and characteristic differences with the controller formulations and highlight each controller’s advantages and disadvantages.

This study extends the whole-body control (WBC) formulation for bipedal humanoid robots that include closed (parallel) kinematic chains in their structure. Along with general formulation, we also stress the implementation of this formulation on Kangaroo, which is a highly dynamic humanoid robot developed by PAL Robotics. This 76-DOF robot includes 24 independent closed-kinematic chains in its structure and constitutes a good case study for our approach. We discuss the WBC formulation for various control structures, including inverse dynamics control (IDC) and Modular Passive Tracking Control (MPTC). As a test scenario, we employ a 3D spring-loaded inverted pendulum (SLIP) jumping trajectory with disturbance rejection as the desired CoM trajectory.