Closed-loop MPC with Dense Visual SLAM -Stability through Reactive Stepping

International audienceWalking gaits generated using Model Predictive Control (MPC) is widely used due to its capability to handle several constraints that characterize humanoid locomotion. The use of simplified models such as the Linear Inverted Pendulum allows to perform computations in real-time, giving the robot the fundamental capacity to replan its motion to follow external inputs (e.g. reference velocity, footstep plans). However, usually the MPC does not take into account the current state of the robot when computing the reference motion, losing the ability to react to external disturbances. In this paper a closed-loop MPC scheme is proposed to estimate the robot's real state through Simultaneous Localization and Mapping (SLAM) and proprioceptive sensors (force/torque). With the proposed control scheme it is shown that the robot is able to react to external disturbances (push), by stepping to recover from the loss of balance. Moreover the localization allows the robot to navigate to target positions in the environment without being affected by the drift generated by imperfect open-loop control execution. We validate the proposed scheme through two different experiments with a HRP-4 humanoid robot

Closed-loop MPC with Dense Visual SLAM - Stability through reactive stepping

Walking gaits generated using Model Predictive Control (MPC) is widely used due to its capability to handle several constraints that characterize humanoid locomotion. The use of simplified models such as the Linear Inverted Pendulum allows to perform computations in real-time, giving the robot the fundamental capacity to replan its motion to follow external inputs (e.g. reference velocity, footstep plans). However, usually the MPC does not take into account the current state of the robot when computing the reference motion, losing the ability to react to external disturbances. In this paper a closed-loop MPC scheme is proposed to estimate the robot's real state through Simultaneous Localization and Mapping (SLAM) and proprioceptive sensors (force/torque). With the proposed control scheme it is shown that the robot is able to react to external disturbances (push), by stepping to recover from the loss of balance. Moreover the localization allows the robot to navigate to target positions in the environment without being affected by the drift generated by imperfect open-loop control execution. We validate the proposed scheme through two different experiments with a HRP-4 humanoid robot

Humanoid gait generation via MPC: stability, robustness and extensions

Research on humanoid robots has made significant progress in recent years, and Model Predictive Control (MPC) has seen great applicability as a technique for gait generation. The main advantages of MPC are the possibility of enforcing constraints on state and inputs, and the constant replanning which grants a degree of robustness. This thesis describes a framework based on MPC for humanoid gait generation, and analyzes some theoretical aspects which have often been neglected. In particular, the stability of the controller is proved. Due to the presence of constraints, this requires proving recursive feasibility, i.e., that the algorithm is able to recursively guarantee that a solution satisfying the constraints is found. The scheme is referred to as Intrinsically Stable MPC (IS-MPC). A basic scheme is presented, and its stability and feasibility guarantees are discussed. Then, several extensions are introduced. The guarantees of the basic scheme are carried over to a robust version of IS-MPC. Furthermore, extension to uneven ground and to a more accurate multi-mass model are discussed. Experiments on two robotic platforms (the humanoid robots HRP-4 and NAO) are presented in the concluding section