5,529 research outputs found
A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control
This paper introduces a family of iterative algorithms for unconstrained
nonlinear optimal control. We generalize the well-known iLQR algorithm to
different multiple-shooting variants, combining advantages like
straight-forward initialization and a closed-loop forward integration. All
algorithms have similar computational complexity, i.e. linear complexity in the
time horizon, and can be derived in the same computational framework. We
compare the full-step variants of our algorithms and present several simulation
examples, including a high-dimensional underactuated robot subject to contact
switches. Simulation results show that our multiple-shooting algorithms can
achieve faster convergence, better local contraction rates and much shorter
runtimes than classical iLQR, which makes them a superior choice for nonlinear
model predictive control applications.Comment: 8 page
Multiframe Scene Flow with Piecewise Rigid Motion
We introduce a novel multiframe scene flow approach that jointly optimizes
the consistency of the patch appearances and their local rigid motions from
RGB-D image sequences. In contrast to the competing methods, we take advantage
of an oversegmentation of the reference frame and robust optimization
techniques. We formulate scene flow recovery as a global non-linear least
squares problem which is iteratively solved by a damped Gauss-Newton approach.
As a result, we obtain a qualitatively new level of accuracy in RGB-D based
scene flow estimation which can potentially run in real-time. Our method can
handle challenging cases with rigid, piecewise rigid, articulated and moderate
non-rigid motion, and does not rely on prior knowledge about the types of
motions and deformations. Extensive experiments on synthetic and real data show
that our method outperforms state-of-the-art.Comment: International Conference on 3D Vision (3DV), Qingdao, China, October
201
Multiframe Scene Flow with Piecewise Rigid Motion
We introduce a novel multiframe scene flow approach that jointly optimizes
the consistency of the patch appearances and their local rigid motions from
RGB-D image sequences. In contrast to the competing methods, we take advantage
of an oversegmentation of the reference frame and robust optimization
techniques. We formulate scene flow recovery as a global non-linear least
squares problem which is iteratively solved by a damped Gauss-Newton approach.
As a result, we obtain a qualitatively new level of accuracy in RGB-D based
scene flow estimation which can potentially run in real-time. Our method can
handle challenging cases with rigid, piecewise rigid, articulated and moderate
non-rigid motion, and does not rely on prior knowledge about the types of
motions and deformations. Extensive experiments on synthetic and real data show
that our method outperforms state-of-the-art.Comment: International Conference on 3D Vision (3DV), Qingdao, China, October
201
Recent Advances in Computational Methods for the Power Flow Equations
The power flow equations are at the core of most of the computations for
designing and operating electric power systems. The power flow equations are a
system of multivariate nonlinear equations which relate the power injections
and voltages in a power system. A plethora of methods have been devised to
solve these equations, starting from Newton-based methods to homotopy
continuation and other optimization-based methods. While many of these methods
often efficiently find a high-voltage, stable solution due to its large basin
of attraction, most of the methods struggle to find low-voltage solutions which
play significant role in certain stability-related computations. While we do
not claim to have exhausted the existing literature on all related methods,
this tutorial paper introduces some of the recent advances in methods for
solving power flow equations to the wider power systems community as well as
bringing attention from the computational mathematics and optimization
communities to the power systems problems. After briefly reviewing some of the
traditional computational methods used to solve the power flow equations, we
focus on three emerging methods: the numerical polynomial homotopy continuation
method, Groebner basis techniques, and moment/sum-of-squares relaxations using
semidefinite programming. In passing, we also emphasize the importance of an
upper bound on the number of solutions of the power flow equations and review
the current status of research in this direction.Comment: 13 pages, 2 figures. Submitted to the Tutorial Session at IEEE 2016
American Control Conferenc
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