Robust MPC-Based Gait Generation in Humanoids

We introduce a robust gait generation framework for humanoid robots based on our Intrinsically Stable Model Predictive Control (IS-MPC) scheme, which features a stability constraint to guarantee internal stability. With respect to the original version, the new framework adds multiple components addressing the robustness problem from different angles: an observer-based disturbance compensation mechanism; a ZMP constraint restriction that provides robustness with respect to bounded disturbances; and a step timing adaptation module to prevent the loss of feasibility. Simulation and experimental results are presented

MPC-based gait generation for humanoids: From walking to running

We present a Model Predictive Control (MPC) algorithm for 3D walking and running in humanoids. The scheme makes use of the Variable Height Inverted Pendulum (VH-IP) as prediction model, and generates a Center of Mass (CoM) trajectory and footstep positions online. The MPC works with the nonlinear dynamics by decomposing the problem into a vertical and a horizontal component. The vertical is solved first making the horizontal dynamics linear time-varying and therefore solvable in real-time. A stability constraint is incorporated to ensure internal stability. The algorithm is validated with dynamic simulations in DART

Handling Non-Convex Constraints in MPC-Based Humanoid Gait Generation

In most MPC-based schemes used for humanoid gait generation, simple Quadratic Programming (QP) problems are considered for real-time implementation. Since these only allow for convex constraints, the generated gait may be conservative. In this paper we focus on the non-convex reachable region of the swinging foot, also known as Kinematic Admissible Region (KAR), and the corresponding constraint. We represent an approximation of such non-convex region as the union of multiple non-overlapping convex sub-regions. By leveraging the concept of feasibility region, i.e., the subset of the state space for which a QP problem is feasible, and introducing a proper selection criterion, we are able to maintain linearity of the constraints and thus use our Intrinsically Stable Model Predictive Control (IS-MPC) scheme with a negligible additional computational load. This approach allows for a wider range of possible generated motions and is very effective when reacting to a push or avoiding an obstacle, as illustrated in dynamically simulated scenarios

Task-Oriented Generation of Stable Motions for Wheeled Inverted Pendulum Robots

We present a whole-body control architecture for the generation of stable task-oriented motions in Wheeled Inverted Pendulum (WIP) robots. Controlling WIP systems is challenging because the successful execution of tasks is subordinate to the ability to maintain balance. Our feedback control approach relies both on partial feedback linearization and Model Predictive Control (MPC). The partial feedback linearization reshapes the system into a convenient form, while the MPC computes inputs to execute the desired task by solving a constrained optimization problem. Input constraints account for actuation limits and a stability constraint is in charge of stabilizing the unstable body pitch angle dynamics. The proposed approach is validated by simulations on an ALTER-EGO robot performing navigation and loco-manipulation tasks

Cow, donkey and human milk affects metabolic homeostasis and inflammatory state by modulating hepatic mitochondrial function and gut microbiota composition in rats

Cow, donkey and human milk affects metabolic homeostasis and inflammatory state by modulating hepatic mitochondrial function and gut microbiota composition in rat