6,073 research outputs found
Electrophoretic separation of proteins via complexation with a polyelectrolyte
We suggest to augment standard isoelectronic focusing for separation of
proteins in a gradient of pH by a similar focusing in the presence of a
strongly charged polyelectrolyte (PE). Proteins which have the same isoelectric
point but different "hidden" charge of both signs in pI point make complexes
with PE, which focus in different pH. This is a result of charge inversion of
such proteins by adsorbed PE molecules, which is sensitive to the hidden
charge. Hence the hidden charge is a new separation parameter
Unsupervised Contact Learning for Humanoid Estimation and Control
This work presents a method for contact state estimation using fuzzy
clustering to learn contact probability for full, six-dimensional humanoid
contacts. The data required for training is solely from proprioceptive sensors
- endeffector contact wrench sensors and inertial measurement units (IMUs) -
and the method is completely unsupervised. The resulting cluster means are used
to efficiently compute the probability of contact in each of the six
endeffector degrees of freedom (DoFs) independently. This clustering-based
contact probability estimator is validated in a kinematics-based base state
estimator in a simulation environment with realistic added sensor noise for
locomotion over rough, low-friction terrain on which the robot is subject to
foot slip and rotation. The proposed base state estimator which utilizes these
six DoF contact probability estimates is shown to perform considerably better
than that which determines kinematic contact constraints purely based on
measured normal force.Comment: Submitted to the IEEE International Conference on Robotics and
Automation (ICRA) 201
Learning a Structured Neural Network Policy for a Hopping Task
In this work we present a method for learning a reactive policy for a simple
dynamic locomotion task involving hard impact and switching contacts where we
assume the contact location and contact timing to be unknown. To learn such a
policy, we use optimal control to optimize a local controller for a fixed
environment and contacts. We learn the contact-rich dynamics for our
underactuated systems along these trajectories in a sample efficient manner. We
use the optimized policies to learn the reactive policy in form of a neural
network. Using a new neural network architecture, we are able to preserve more
information from the local policy and make its output interpretable in the
sense that its output in terms of desired trajectories, feedforward commands
and gains can be interpreted. Extensive simulations demonstrate the robustness
of the approach to changing environments, outperforming a model-free gradient
policy based methods on the same tasks in simulation. Finally, we show that the
learned policy can be robustly transferred on a real robot.Comment: IEEE Robotics and Automation Letters 201
Trajectory generation for multi-contact momentum-control
Simplified models of the dynamics such as the linear inverted pendulum model
(LIPM) have proven to perform well for biped walking on flat ground. However,
for more complex tasks the assumptions of these models can become limiting. For
example, the LIPM does not allow for the control of contact forces
independently, is limited to co-planar contacts and assumes that the angular
momentum is zero. In this paper, we propose to use the full momentum equations
of a humanoid robot in a trajectory optimization framework to plan its center
of mass, linear and angular momentum trajectories. The model also allows for
planning desired contact forces for each end-effector in arbitrary contact
locations. We extend our previous results on LQR design for momentum control by
computing the (linearized) optimal momentum feedback law in a receding horizon
fashion. The resulting desired momentum and the associated feedback law are
then used in a hierarchical whole body control approach. Simulation experiments
show that the approach is computationally fast and is able to generate plans
for locomotion on complex terrains while demonstrating good tracking
performance for the full humanoid control
Humanoid Momentum Estimation Using Sensed Contact Wrenches
This work presents approaches for the estimation of quantities important for
the control of the momentum of a humanoid robot. In contrast to previous
approaches which use simplified models such as the Linear Inverted Pendulum
Model, we present estimators based on the momentum dynamics of the robot. By
using this simple yet dynamically-consistent model, we avoid the issues of
using simplified models for estimation. We develop an estimator for the center
of mass and full momentum which can be reformulated to estimate center of mass
offsets as well as external wrenches applied to the robot. The observability of
these estimators is investigated and their performance is evaluated in
comparison to previous approaches.Comment: Submitted to the 15th IEEE RAS Humanoids Conference, to be held in
Seoul, Korea on November 3 - 5, 201
State Estimation for a Humanoid Robot
This paper introduces a framework for state estimation on a humanoid robot
platform using only common proprioceptive sensors and knowledge of leg
kinematics. The presented approach extends that detailed in [1] on a quadruped
platform by incorporating the rotational constraints imposed by the humanoid's
flat feet. As in previous work, the proposed Extended Kalman Filter (EKF)
accommodates contact switching and makes no assumptions about gait or terrain,
making it applicable on any humanoid platform for use in any task. The filter
employs a sensor-based prediction model which uses inertial data from an IMU
and corrects for integrated error using a kinematics-based measurement model
which relies on joint encoders and a kinematic model to determine the relative
position and orientation of the feet. A nonlinear observability analysis is
performed on both the original and updated filters and it is concluded that the
new filter significantly simplifies singular cases and improves the
observability characteristics of the system. Results on simulated walking and
squatting datasets demonstrate the performance gain of the flat-foot filter as
well as confirm the results of the presented observability analysis.Comment: IROS 2014 Submission, IEEE/RSJ International Conference on
Intelligent Robots and Systems (2014) 952-95
Hybrid PCM\u2014aluminium foams\u2019 thermal storages: an experimental study
The latent heat absorption phenomenon associated with melting of a suitable Phase Change Material can be an effective way to improve the Thermal Energy Storage behaviour in many applications. However, the most suitable materials to be used in heating and refrigeration systems find intrinsic limitations due to their poor heat transfer capabilities. This work experimentally studies the use of aluminum foams as heat transfer medium to improve the overall heat transfer of paraffin waxes that can be possible phase change materials to be implemented in hybrid sensible-latent water thermal energy storages. The experimental tests were run in a dedicated setup designed, developed, and built at the Department of Management and Engineering of the University of Padova. The effects of the use of aluminum foams as enhancing heat transfer medium were studied by comparing the loading and unloading processes of a paraffin wax with melting temperature around 40 \ub0C, with and without metal foams, in a water thermal storage unit. The effects of three different foams with 5, 20, and 40 Pores Per Inch (PPI) were investigated
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