Ricerc@Sapienza

We propose a sensor-based motion plan-ning/replanning method for a humanoid that must execute a task implicitly requiring locomotion. It is assumed that the environment is unknown and the robot is equipped with a depth sensor. The proposed approach hinges upon three modules that run concurrently: mapping, planning and execution. The mapping module is in charge of incrementally building a 3D environment map during the robot motion, based on the information provided by the depth sensor.

From a control point of view, humanoid gait generation can be seen as a problem of tracking a suitable ZMP trajectory while guaranteeing internal stability. In the presence of disturbances, both these aspects are at risk, and a fall may ultimately occur. In this paper, we extend our previously proposed Intrinsically Stable MPC (IS-MPC) method, which guarantees stable tracking for the unperturbed case, to the case of persistent disturbances. This is achieved by designing a disturbance observer whose estimate is used to set up a modified stability constraint for the QP problem.