6,515 research outputs found
An Overview of Variational Integrators
The purpose of this paper is to survey some recent advances in variational
integrators for both finite dimensional mechanical systems as well as continuum
mechanics. These advances include the general development of discrete
mechanics, applications to dissipative systems, collisions, spacetime integration algorithms,
AVI’s (Asynchronous Variational Integrators), as well as reduction for
discrete mechanical systems. To keep the article within the set limits, we will only
treat each topic briefly and will not attempt to develop any particular topic in
any depth. We hope, nonetheless, that this paper serves as a useful guide to the
literature as well as to future directions and open problems in the subject
Towards Odor-Sensitive Mobile Robots
J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012
Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical
ones when operating in real environments. Until now, these sensorial systems mostly relied on range
sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely
been employed, they can provide a complementary sensory information, vital for some applications, as
with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities
and also reviews some of the hurdles that are preventing smell from achieving the importance of other
sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status
on the three main fields within robotics olfaction: the classification of volatile substances, the spatial
estimation of the gas dispersion from sparse measurements, and the localization of the gas source within
a known environment
Chaotic Quantum Double Delta Swarm Algorithm using Chebyshev Maps: Theoretical Foundations, Performance Analyses and Convergence Issues
Quantum Double Delta Swarm (QDDS) Algorithm is a new metaheuristic algorithm
inspired by the convergence mechanism to the center of potential generated
within a single well of a spatially co-located double-delta well setup. It
mimics the wave nature of candidate positions in solution spaces and draws upon
quantum mechanical interpretations much like other quantum-inspired
computational intelligence paradigms. In this work, we introduce a Chebyshev
map driven chaotic perturbation in the optimization phase of the algorithm to
diversify weights placed on contemporary and historical, socially-optimal
agents' solutions. We follow this up with a characterization of solution
quality on a suite of 23 single-objective functions and carry out a comparative
analysis with eight other related nature-inspired approaches. By comparing
solution quality and successful runs over dynamic solution ranges, insights
about the nature of convergence are obtained. A two-tailed t-test establishes
the statistical significance of the solution data whereas Cohen's d and Hedge's
g values provide a measure of effect sizes. We trace the trajectory of the
fittest pseudo-agent over all function evaluations to comment on the dynamics
of the system and prove that the proposed algorithm is theoretically globally
convergent under the assumptions adopted for proofs of other closely-related
random search algorithms.Comment: 27 pages, 4 figures, 19 table
Optimization and universality of Brownian search in quenched heterogeneous media
The kinetics of a variety of transport-controlled processes can be reduced to
the problem of determining the mean time needed to arrive at a given location
for the first time, the so called mean first passage time (MFPT) problem. The
occurrence of occasional large jumps or intermittent patterns combining various
types of motion are known to outperform the standard random walk with respect
to the MFPT, by reducing oversampling of space. Here we show that a regular but
spatially heterogeneous random walk can significantly and universally enhance
the search in any spatial dimension. In a generic minimal model we consider a
spherically symmetric system comprising two concentric regions with piece-wise
constant diffusivity. The MFPT is analyzed under the constraint of conserved
average dynamics, that is, the spatially averaged diffusivity is kept constant.
Our analytical calculations and extensive numerical simulations demonstrate the
existence of an {\em optimal heterogeneity} minimizing the MFPT to the target.
We prove that the MFPT for a random walk is completely dominated by what we
term direct trajectories towards the target and reveal a remarkable
universality of the spatially heterogeneous search with respect to target size
and system dimensionality. In contrast to intermittent strategies, which are
most profitable in low spatial dimensions, the spatially inhomogeneous search
performs best in higher dimensions. Discussing our results alongside recent
experiments on single particle tracking in living cells we argue that the
observed spatial heterogeneity may be beneficial for cellular signaling
processes.Comment: 19 pages, 11 figures, RevTe
On dimension reduction in Gaussian filters
A priori dimension reduction is a widely adopted technique for reducing the
computational complexity of stationary inverse problems. In this setting, the
solution of an inverse problem is parameterized by a low-dimensional basis that
is often obtained from the truncated Karhunen-Loeve expansion of the prior
distribution. For high-dimensional inverse problems equipped with smoothing
priors, this technique can lead to drastic reductions in parameter dimension
and significant computational savings.
In this paper, we extend the concept of a priori dimension reduction to
non-stationary inverse problems, in which the goal is to sequentially infer the
state of a dynamical system. Our approach proceeds in an offline-online
fashion. We first identify a low-dimensional subspace in the state space before
solving the inverse problem (the offline phase), using either the method of
"snapshots" or regularized covariance estimation. Then this subspace is used to
reduce the computational complexity of various filtering algorithms - including
the Kalman filter, extended Kalman filter, and ensemble Kalman filter - within
a novel subspace-constrained Bayesian prediction-and-update procedure (the
online phase). We demonstrate the performance of our new dimension reduction
approach on various numerical examples. In some test cases, our approach
reduces the dimensionality of the original problem by orders of magnitude and
yields up to two orders of magnitude in computational savings
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