Real-Time Trajectory Generation by Offline Footstep Planning for a Humanoid Soccer Robot

摘要

In recent years, humanoid soccer robots improved considerably. Elementary soccer skills, such as bipedal walking, visual perception, and collision avoidance have matured enough to provide for dynamic and exciting soccer games. While the elementary skills still remain hot research topics, it is time to move forward and address higher level skills, such as motion planning and team play. In this work, we present a new method to generate ball approach trajectories by planning footstep sequences offline and training an online policy to meet the real time requirements of embedded systems with low computational power, as typically used for soccer robots. We compare the results with our current reactive behavior that was used in the last RoboCup competitions and show the improvements we achieved.