1,060 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
Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking
This manuscript presents control of a high-DOF fully actuated lower-limb
exoskeleton for paraplegic individuals. The key novelty is the ability for the
user to walk without the use of crutches or other external means of
stabilization. We harness the power of modern optimization techniques and
supervised machine learning to develop a smooth feedback control policy that
provides robust velocity regulation and perturbation rejection. Preliminary
evaluation of the stability and robustness of the proposed approach is
demonstrated through the Gazebo simulation environment. In addition,
preliminary experimental results with (complete) paraplegic individuals are
included for the previous version of the controller.Comment: Submitted to IEEE Control System Magazine. This version addresses
reviewers' concerns about the robustness of the algorithm and the motivation
for using such exoskeleton
Data-driven Step-to-step Dynamics based Adaptive Control for Robust and Versatile Underactuated Bipedal Robotic Walking
This paper presents a framework for synthesizing bipedal robotic walking that
adapts to unknown environment and dynamics error via a data-driven step-to-step
(S2S) dynamics model. We begin by synthesizing an S2S controller that
stabilizes the walking using foot placement through nominal S2S dynamics from
the hybrid linear inverted pendulum (H-LIP) model. Next, a data-driven
representation of the S2S dynamics of the robot is learned online via classical
adaptive control methods. The desired discrete foot placement on the robot is
thereby realized by proper continuous output synthesis capturing the
data-driven S2S controller coupled with a low-level tracking controller. The
proposed approach is implemented in simulation on an underactuated 3D bipedal
robot, Cassie, and improved reference velocity tracking is demonstrated. The
proposed approach is also able to realize walking behavior that is robustly
adaptive to unknown loads, inaccurate robot models, external disturbance
forces, biased velocity estimation, and unknown slopes
Frequency-Aware Model Predictive Control
Transferring solutions found by trajectory optimization to robotic hardware
remains a challenging task. When the optimization fully exploits the provided
model to perform dynamic tasks, the presence of unmodeled dynamics renders the
motion infeasible on the real system. Model errors can be a result of model
simplifications, but also naturally arise when deploying the robot in
unstructured and nondeterministic environments. Predominantly, compliant
contacts and actuator dynamics lead to bandwidth limitations. While classical
control methods provide tools to synthesize controllers that are robust to a
class of model errors, such a notion is missing in modern trajectory
optimization, which is solved in the time domain. We propose frequency-shaped
cost functions to achieve robust solutions in the context of optimal control
for legged robots. Through simulation and hardware experiments we show that
motion plans can be made compatible with bandwidth limits set by actuators and
contact dynamics. The smoothness of the model predictive solutions can be
continuously tuned without compromising the feasibility of the problem.
Experiments with the quadrupedal robot ANYmal, which is driven by
highly-compliant series elastic actuators, showed significantly improved
tracking performance of the planned motion, torque, and force trajectories and
enabled the machine to walk robustly on terrain with unmodeled compliance
- …