35 research outputs found
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
Multi-contact Stochastic Predictive Control for Legged Robots with Contact Locations Uncertainty
Trajectory optimization under uncertainties is a challenging problem for
robots in contact with the environment. Such uncertainties are inevitable due
to estimation errors, control imperfections, and model mismatches between
planning models used for control and the real robot dynamics. This induces
control policies that could violate the contact location constraints by making
contact at unintended locations, and as a consequence leading to unsafe motion
plans. This work addresses the problem of robust kino-dynamic whole-body
trajectory optimization using stochastic nonlinear model predictive control
(SNMPC) by considering additive uncertainties on the model dynamics subject to
contact location chance-constraints as a function of robot's full kinematics.
We demonstrate the benefit of using SNMPC over classic nonlinear MPC (NMPC) for
whole-body trajectory optimization in terms of contact location constraint
satisfaction (safety). We run extensive Monte-Carlo simulations for a quadruped
robot performing agile trotting and bounding motions over small stepping
stones, where contact location satisfaction becomes critical. Our results show
that SNMPC is able to perform all motions safely with 100% success rate, while
NMPC failed 48.3% of all motions
Nonlinear Stochastic Trajectory Optimization for Centroidal Momentum Motion Generation of Legged Robots
Generation of robust trajectories for legged robots remains a challenging
task due to the underlying nonlinear, hybrid and intrinsically unstable
dynamics which needs to be stabilized through limited contact forces.
Furthermore, disturbances arising from unmodelled contact interactions with the
environment and model mismatches can hinder the quality of the planned
trajectories leading to unsafe motions. In this work, we propose to use
stochastic trajectory optimization for generating robust centroidal momentum
trajectories to account for additive uncertainties on the model dynamics and
parametric uncertainties on contact locations. Through an alternation between
the robust centroidal and whole-body trajectory optimizations, we generate
robust momentum trajectories while being consistent with the whole-body
dynamics. We perform an extensive set of simulations subject to different
uncertainties on a quadruped robot showing that our stochastic trajectory
optimization problem reduces the amount of foot slippage for different gaits
while achieving better performance over deterministic planning
On the Use of Torque Measurement in Centroidal State Estimation
State of the art legged robots are either capable of measuring torque at the
output of their drive systems, or have transparent drive systems which enable
the computation of joint torques from motor currents. In either case, this
sensor modality is seldom used in state estimation. In this paper, we propose
to use joint torque measurements to estimate the centroidal states of legged
robots. To do so, we project the whole-body dynamics of a legged robot into the
nullspace of the contact constraints, allowing expression of the dynamics
independent of the contact forces. Using the constrained dynamics and the
centroidal momentum matrix, we are able to directly relate joint torques and
centroidal states dynamics. Using the resulting model as the process model of
an Extended Kalman Filter (EKF), we fuse the torque measurement in the
centroidal state estimation problem. Through real-world experiments on a
quadruped robot with different gaits, we demonstrate that the estimated
centroidal states from our torque-based EKF drastically improve the recovery of
these quantities compared to direct computation