41 research outputs found
Push Recovery of a Position-Controlled Humanoid Robot Based on Capture Point Feedback Control
In this paper, a combination of ankle and hip strategy is used for push
recovery of a position-controlled humanoid robot. Ankle strategy and hip
strategy are equivalent to Center of Pressure (CoP) and Centroidal Moment Pivot
(CMP) regulation respectively. For controlling the CMP and CoP we need a
torque-controlled robot, however most of the conventional humanoid robots are
position controlled. In this regard, we present an efficient way for
implementation of the hip and ankle strategies on a position controlled
humanoid robot. We employ a feedback controller to compensate the capture point
error. Using our scheme, a simple and practical push recovery controller is
designed which can be implemented on the most of the conventional humanoid
robots without the need for torque sensors. The effectiveness of the proposed
approach is verified through push recovery experiments on SURENA-Mini humanoid
robot under severe pushes
Humanoid gait generation for walk-to locomotion using single-stage MPC
We consider the problem of gait generation for a humanoid robot that must walk to an assigned Cartesian goal. As a first solution, we consider a rather straightforward adaptation of our previous work: An external block produces high-level velocities, which are then tracked by a double-stage intrinsically stable MPC scheme where the orientation of the footsteps is chosen before determining their location and the CoM trajectory. The second solution, which represents the main contribution of the paper, is conceptually different: no high-level velocity is generated, and footstep orientations are chosen at the same time of the other decision variables in a singlestage MPC. This is made possible by carefully redesigning the motion constraints so as to preserve linearity. Preliminary results on a simulated NAO confirm that the single-stage method outperforms the conventional double-stage scheme
Pattern Generation for Walking on Slippery Terrains
In this paper, we extend state of the art Model Predictive Control (MPC)
approaches to generate safe bipedal walking on slippery surfaces. In this
setting, we formulate walking as a trade off between realizing a desired
walking velocity and preserving robust foot-ground contact. Exploiting this
formulation inside MPC, we show that safe walking on various flat terrains can
be achieved by compromising three main attributes, i. e. walking velocity
tracking, the Zero Moment Point (ZMP) modulation, and the Required Coefficient
of Friction (RCoF) regulation. Simulation results show that increasing the
walking velocity increases the possibility of slippage, while reducing the
slippage possibility conflicts with reducing the tip-over possibility of the
contact and vice versa.Comment: 6 pages, 7 figure
A Reactive and Efficient Walking Pattern Generator for Robust Bipedal Locomotion
Available possibilities to prevent a biped robot from falling down in the
presence of severe disturbances are mainly Center of Pressure (CoP) modulation,
step location and timing adjustment, and angular momentum regulation. In this
paper, we aim at designing a walking pattern generator which employs an optimal
combination of these tools to generate robust gaits. In this approach, first,
the next step location and timing are decided consistent with the commanded
walking velocity and based on the Divergent Component of Motion (DCM)
measurement. This stage which is done by a very small-size Quadratic Program
(QP) uses the Linear Inverted Pendulum Model (LIPM) dynamics to adapt the
switching contact location and time. Then, consistent with the first stage, the
LIPM with flywheel dynamics is used to regenerate the DCM and angular momentum
trajectories at each control cycle. This is done by modulating the CoP and
Centroidal Momentum Pivot (CMP) to realize a desired DCM at the end of current
step. Simulation results show the merit of this reactive approach in generating
robust and dynamically consistent walking patterns
Gait generation via intrinsically stable MPC for a multi-mass humanoid model
We consider the problem of generating a gait with no a priori assigned footsteps while taking into account the contribution of the swinging leg to the total Zero Moment Point (ZMP). This is achieved by considering a multi-mass model of the humanoid and distinguishing between secondary masses with known pre-defined motion and the remaining, primary, masses. In the case of a single primary mass with constant height, it is possible to transform the original gait generation problem for the multi-mass system into a single LIP-like problem. We can then take full advantage of an intrinsically stable MPC framework to generate a gait that takes into account the swinging leg motion