Note that in the swing foot MPC problem, the horizon is given at each control cycle by the high-level MPC and is not part of the decision variables.Īll components of the control pipeline are exactly the same, except the swing foot trajectory generation part. Using the swing foot dynamics and constraint on the available force, we write down a VH-MPC problem that finds a dynamically consistent trajectory for the swing foot to land at the desired position at the desired time given by the high-level MPC. We also take into account the stance foot friction cone and joint torque constraints to find bounds on the available force that can be applied by the swing force. Since this dynamic model is nonlinear, we use a linearized version of it. ![]() For the swing foot dynamics, we project the dynamics of the robot to the swing foot space. In this paper, we propose a two-level VH-MPC framework, where the high-level MPC adapts the next step location and timing to stabilize the divergent component of motion (DCM) of the CoM dynamics, and the low-level MPC takes into account the swing foot dynamics to land as close as possible to the planned location at the desired time. However, in order to make sure that this is the case, one needs to generate a feasible swing foot trajectory that establishes contact at the desired time and location, and be consistent with the CoM trajectory and planned step location and time. They all consider some proxy constraints to guide roughly the swing foot to touch the ground at the desired time and location. The optimal control problem (OCP) solved at each control cycle in these approaches focuses on the center of mass (CoM) dynamics and investigates how to stabilize it using force modulation, for predefined or adaptive step locations and timings. Therefore, Linear Model predictive control (MPC) has become a powerful tool for controlling bipedal walking. Using the linear inverted pendulum model (LIPM), a walking controller can be written as a linear quadratic program that can be solved quickly. Therefore, a walking controller should decide where and when to step and how to control highly constrained interaction forces to generate a desired walking behaviour and reject disturbances. the role of the stance and swing foot switches at each walking phase. READ FULL TEXT VIEW PDFįor bipedal walking on regular surfaces, there is no ambiguity in contact sequence, i.e. Simulations and real robot experiments in the presence of various disturbancesĪnd uncertainties. Torque-controlled and open-source biped robot, Bolt. We show the effectiveness of our proposedĬontrol framework by implementing robust walking patterns on our Takes into account joint torque constraints as well as the friction coneĬonstraints of the stance foot. Use a simplified model of the robot dynamics projected in swing foot space that The desired time as close as possible to the desired location. The lower level takes into account the swingįoot dynamics and generates dynamically consistent trajectories for landing at Swing foot to stabilize the unstable part of the center of mass (CoM) dynamics, Higher level computes the landing location and timing (horizon length) of the In this paper, we present a novel two-level variable Horizon Model PredictiveĬontrol (VH-MPC) framework for bipedal locomotion.
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