Height Varying Humanoid Robot Walking through Model Predictive Control - Silva, Caroline; Maximo, Marcos; Góes, Luiz (Anais do Simpósio Brasileiro de Robótica e Simpósio Latino Americano de Robótica (SBR/LARS); 2019: Anais do VII Simpósio Brasileiro de Robótica e XVI Simpósio Latino Americano de Robótica, 2019)
The present paper proposes the application of Model Predictive Control (MPC) to the bipedal walking problem. Classically, bipedal robots maintains constant height of the center of mass (CoM) during walking, since this constraint makes the underlying dynamical system linear. Nevertheless, researches show that vertical CoM motion is one of many mechanisms humans use to reduce energetic cost during walking. In this paper, we show that if the height is modified through a predefined function, the system becomes linear time-varying, which may be handled by MPC techniques. By means of simulations, the stability behavior of the robot is verified. Finally, a high-fidelity simulation model based on the Gazebo simulator is used to validate the energetic cost reduction due to the vertical CoM motion.