Constrained Analytical Trajectory Filter for stabilizing humanoid robot motions

Abstract

Mimicking human motion with a humanoid robot is essential for allowing humanoid robots to be used in service applications. Simply creating motions without considerations for balance and stability or directly copying motion from a human using motion capture and implementing it on a humanoid robot may not be successful because of the difference in physical properties between the human and the humanoid robot, which may cause instability and make it fall. Using the Zero Moment Point as the stability criteria, this work proposes a Constrained Analytical Trajectory Filter as part of an Analytical Motion Filter, which stabilizes a reference motion that can come from human motion capture data, kinematic synthesis, or animation software. The resulting solutions used in the Constrained Analytical Trajectory Filter provide insight into the complex interactions of motion and stability. The solutions were verified in simulation and with hardware, showing that the analytical filter can be successfully applied for stabilizing reference motions for humanoid robots which may be unstable otherwise.