1,391 research outputs found
The Soft Landing Problem: Minimizing Energy Loss by a Legged Robot Impacting Yielding Terrain
Enabling robots to walk and run on yielding terrain is increasingly vital to
endeavors ranging from disaster response to extraterrestrial exploration. While
dynamic legged locomotion on rigid ground is challenging enough, yielding
terrain presents additional challenges such as permanent ground deformation
which dissipates energy. In this paper, we examine the soft landing problem:
given some impact momentum, bring the robot to rest while minimizing foot
penetration depth. To gain insight into properties of penetration
depth-minimizing control policies, we formulate a constrained optimal control
problem and obtain a bang-bang open-loop force profile. Motivated by examples
from biology and recent advances in legged robotics, we also examine
impedance-control solutions to the dimensionless soft landing problem. Through
simulations, we find that optimal impedance reduces penetration depth nearly as
much as the open-loop force profile, while remaining robust to model
uncertainty. Through simulations and experiments, we find that the solution
space is rich, exhibiting qualitatively different relationships between impact
velocity and the optimal impedance for small and large dimensionless impact
velocities. Lastly, we discuss the relevance of this work to
minimum-cost-of-transport locomotion for several actuator design choices
The separate neural control of hand movements and contact forces
To manipulate an object, we must simultaneously control the contact forces exerted on the object and the movements of our hand. Two alternative views for manipulation have been proposed: one in which motions and contact forces are represented and controlled by separate neural processes, and one in which motions and forces are controlled jointly, by a single process. To evaluate these alternatives, we designed three tasks in which subjects maintained a specified contact force while their hand was moved by a robotic manipulandum. The prescribed contact force and hand motions were selected in each task to induce the subject to attain one of three goals: (1) exerting a regulated contact force, (2) tracking the motion of the manipulandum, and (3) attaining both force and motion goals concurrently. By comparing subjects' performances in these three tasks, we found that behavior was captured by the summed actions of two independent control systems: one applying the desired force, and the other guiding the hand along the predicted path of the manipulandum. Furthermore, the application of transcranial magnetic stimulation impulses to the posterior parietal cortex selectively disrupted the control of motion but did not affect the regulation of static contact force. Together, these findings are consistent with the view that manipulation of objects is performed by independent brain control of hand motions and interaction forces
Robotic Contact Juggling
We define "robotic contact juggling" to be the purposeful control of the
motion of a three-dimensional smooth object as it rolls freely on a
motion-controlled robot manipulator, or "hand." While specific examples of
robotic contact juggling have been studied before, in this paper we provide the
first general formulation and solution method for the case of an arbitrary
smooth object in single-point rolling contact on an arbitrary smooth hand. Our
formulation splits the problem into four subproblems: (1) deriving the
second-order rolling kinematics; (2) deriving the three-dimensional rolling
dynamics; (3) planning rolling motions that satisfy the rolling dynamics; and
(4) feedback stabilization of planned rolling trajectories. The theoretical
results are demonstrated in simulation and experiment using feedback from a
high-speed vision system.Comment: 16 pages, 14 figures. | Supplemental Video:
https://youtu.be/QT55_Q1ePfg | Code:
https://github.com/zackwoodruff/rolling_dynamic
Modulation of hexokinase association with mitochondria analyzed with quantitative three-dimensional confocal microscopy
Hexokinase isozyme I is proposed to be associated with mitochondria in vivo. Moreover, it has been suggested that this association is modulated in coordination with changes in cell metabolic state. To test these hypotheses, we analyzed the subcellular distribution of hexokinase relative to mitochondria in paraformaldehyde-fixed astrocytes using immunocytochemistry and quantitative three-dimensional confocal microscopy. Analysis of the extent of colocalization between hexokinase and mitochondria revealed that approximately 70% of cellular hexokinase is associated with mitochondria under basal metabolic conditions. In contrast to the immunocytochemical studies, between 15 to 40% of cellular hexokinase was found to be associated with mitochondria after fractionation of astrocyte cultures depending on the exact fractionation conditions. The discrepancy between fractionation studies and those based on imaging of distributions in fixed cells indicates the usefulness of using techniques that can evaluate the distributions of cytosolic enzymes in cells whose subcellular ultrastructure is not severely disrupted. To determine if hexokinase distribution is modulated in concert with changes in cell metabolism, the localization of hexokinase with mitochondria was evaluated after inhibition of glucose metabolism with 2-deoxyglucose. After incubation with 2-deoxyglucose there was an approximate 35% decrease in the amount of hexokinase associated with mitochondria. These findings support the hypothesis that hexokinase is bound to mitochondria in rat brain astrocytes in vivo, and that this association is sensitive to cell metabolic state
Self-Healing First-Order Distributed Optimization with Packet Loss
We describe SH-SVL, a parameterized family of first-order distributed
optimization algorithms that enable a network of agents to collaboratively
calculate a decision variable that minimizes the sum of cost functions at each
agent. These algorithms are self-healing in that their convergence to the
correct optimizer can be guaranteed even if they are initialized randomly,
agents join or leave the network, or local cost functions change. We also
present simulation evidence that our algorithms are self-healing in the case of
dropped communication packets. Our algorithms are the first single-Laplacian
methods for distributed convex optimization to exhibit all of these
characteristics. We achieve self-healing by sacrificing internal stability, a
fundamental trade-off for single-Laplacian methods.Comment: arXiv admin note: substantial text overlap with arXiv:2104.0195
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