Online Jumping Motion Generation via Model Predictive Control

Legged robots can move on various grounds via walking; however, they have difficulty in moving fast. To overcome this limitation, some legged robots have been studying dynamic motions such as jumping and running. In this paper, we present a strategy for generating an optimal jumping motion for legged robots in real-time. For convenience, we divided the proposed jumping trajectory into vertical and horizontal directions. The vertical motion trajectory ensures a continuous center of mass position, speed, and acceleration, as well as minimizes the maximum torque and maximum speed of the joints; we generated it via a nonlinear optimal process. Besides, for the horizontal motion, we proposed a novel model predictive control using a height varying inverted pendulum model. In the proposed method, the zero moment point is placed in the support polygon for stable jumping; also, the robot does not slip on the ground and takes off at a desired velocity. We verified the jumping motion using ROK-3, a biped robot developed for the experiment.