Motion planning with dynamics awareness for long reach manipulation in aerial robotic systems with two arms

Human activities in maintenance of industrial plants pose elevated risks as well as significant costs due to the required shutdowns of the facility. An aerial robotic system with two arms for long reach manipulation in cluttered environments is presented to alleviate these constraints. The system consists of a multirotor with a long bar extension that incorporates a lightweight dual arm in the tip. This configuration allows aerial manipulation tasks even in hard-to-reach places. The objective of this work is the development of planning strategies to move the aerial robotic system with two arms for long reach manipulation in a safe and efficient way for both navigation and manipulation tasks. The motion planning problem is addressed considering jointly the aerial platform and the dual arm in order to achieve wider operating conditions. Since there exists a strong dynamical coupling between the multirotor and the dual arm, safety in obstacle avoidance will be assured by introducing dynamics awareness in the operation of the planner. On the other hand, the limited maneuverability of the system emphasizes the importance of energy and time efficiency in the generated trajectories. Accordingly, an adapted version of the optimal Rapidly-exploring Random Tree algorithm has been employed to guarantee their optimality. The resulting motion planning strategy has been evaluated through simulation in two realistic industrial scenarios, a riveting application and a chimney repairing task. To this end, the dynamics of the aerial robotic system with two arms for long reach manipulation has been properly modeled, and a distributed control scheme has been derived to complete the test bed. The satisfactory results of the simulations are presented as a first validation of the proposed approach.Unión Europea H2020-644271Ministerio de Ciencia, Innovación y Universidades DPI2014-59383-C2-1-

Spectral Densities from Dynamic Density-Matrix Renormalization

Dynamic density-matrix renormalization provides valuable numerical information on dynamic correlations by computing convolutions of the corresponding spectral densities. Here we discuss and illustrate how and to which extent such data can be deconvolved to retrieve the wanted spectral densities. We advocate a nonlinear deconvolution scheme which minimizes the bias in the ansatz for the spectral density. The procedure is illustrated for the line shape and width of the Kondo peak (low energy feature) and for the line shape of the Hubbard satellites (high energy feature) of the single impurity Anderson model. It is found that the Hubbard satellites are strongly asymmetric.Comment: RevTeX 4, 11 pages, 7 eps figures; published versio

Fearful faces have a sensory advantage in the competition for awareness

Only a subset of visual signals give rise to a conscious percept. Threat signals, such as fearful faces, are particularly salient to human vision. Research suggests that fearful faces are evaluated without awareness and preferentially promoted to conscious perception. This agrees with evolutionary theories that posit a dedicated pathway specialized in processing threat-relevant signals. We propose an alternative explanation for this "fear advantage." Using psychophysical data from continuous flash suppression (CFS) and masking experiments, we demonstrate that awareness of facial expressions is predicted by effective contrast: the relationship between their Fourier spectrum and the contrast sensitivity function. Fearful faces have higher effective contrast than neutral expressions and this, not threat content, predicts their enhanced access to awareness. Importantly, our findings do not support the existence of a specialized mechanism that promotes threatening stimuli to awareness. Rather, our data suggest that evolutionary or learned adaptations have molded the fearful expression to exploit our general-purpose sensory mechanisms

Third-harmonic generation in photonic topological metasurfaces

We study nonlinear effects in two-dimensional photonic metasurfaces supporting topologically-protected helical edge states at the nanoscale. We observe strong third-harmonic generation mediated by optical nonlinearities boosted by multipolar Mie resonances of silicon nanoparticles. Variation of the pump-beam wavelength enables independent high-contrast imaging of either bulk modes or spin-momentum-locked edge states. We demonstrate topology-driven tunable localization of the generated harmonic fields and map the pseudospin-dependent unidirectional waveguiding of the edge states bypassing sharp corners. Our observations establish dielectric metasurfaces as a promising platform for the robust generation and transport of photons in topological photonic nanostructures.Comment: 5 pages, 5 figure

Excitonic Wave Function Reconstruction from Near-Field Spectra Using Machine Learning Techniques

A general problem in quantum mechanics is the reconstruction of eigenstate wave functions from measured data. In the case of molecular aggregates, information about excitonic eigenstates is vitally important to understand their optical and transport properties. Here we show that from spatially resolved near field spectra it is possible to reconstruct the underlying delocalized aggregate eigenfunctions. Although this high-dimensional nonlinear problem defies standard numerical or analytical approaches, we have found that it can be solved using a convolutional neural network. For both one-dimensional and two-dimensional aggregates we find that the reconstruction is robust to various types of disorder and noise