Learning Ground Traversability from Simulations

Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific robot model (wheeled, tracked, legged, snake-like) using simulation data on procedurally generated training terrains; the trained classifier can be applied to unseen large heightmaps to yield oriented traversability maps, and then plan traversable paths. We extensively evaluate the approach in simulation on six real-world elevation datasets, and run a real-robot validation in one indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation

Specializing in generality: Firm strategies when intermediate markets work

This paper studies the relationship between two decisions shaping the organizational configuration of a firm: whether to make the upstream resources more general and deployable to more markets (vs. keeping them tailored to a few markets), and whether to trade with downstream firms as an upstream supplier of intermediate products and services (vs. directly entering downstream markets). While the literature has looked at these two decisions separately, we argue that they depend on each other. This has the important implication that they can generate organizational complementarities, inducing firms to implement them jointly. We are motivated in particular by the observation that an increasing number of firms invest in general upstream resources and exploit them as upstream suppliers of intermediate services or products— a strategy that we refer to as specialization in generality. Interestingly, the literature following the seminal work by Penrose (1959) and Nelson (1959) has highlighted the use of general upstream resources to enter new downstream markets. We identify the supply and demand conditions under which specialization in generality is instead more likely to emerge: lack of prior downstream assets, on the supply side, and a roughly equal distribution of buyers across intermediate markets (a “broad” demand), on the demand side. We test our predictions using a sample of firms in the U.S. laser industry between 1993 and 2001. A regulatory shock that increases the value of trading relative to downstream entry provides the setting for a quasi-natural experiment, which corroborates our theoretical predictions

In-flight calibration of the fine pointing Sun sensor on the solar maximum mission

The attitude control objectives of solar maximum mission are to point the boresight of the payload fine pointing sun sensor (FPSS) to any point within 30 arc-minutes of the Sun's center with an accuracy of 5 arc-seconds (3 sigma, pitch and yaw) and a jitter of less than 3 arc-seconds (3 sigma). To meet these stringent accuracy requirements, a procedure was developed for in-flight calibration of the FPSS. The spacecraft was maneuvered using FPSS offset commands to position the Sun at different points within the FPSS field of view. The coefficients of the FPSS digital to analog nonlinear transfer function were determined by minimizing the residuals between the pitch and yaw angles computed from the FPSS measurements and the corresponding reference angles obtained from inertial reference unit measurements. The actual in-flight calibration and the calibration algorithm are discussed

Letters

www.elsevier.com/locate/dsw A branch and bound algorithm for the robust shortest path problem with interval data

Ant Colony System for a Dynamic Vehicle Routing Problem

An aboundant literature on vehicle routing problems is available. However, most of the work deals with static problems, where all data are known in advance, i.e. before the optimization has started. The technological advances of the last few years give rise to a new class of problems, namely the dynamic vehicle routing problems, where new orders are received as time progresses and must be dynamically incorporated into an evolving schedule. In this paper a dynamic vehicle routing problem is examined and a solving strategy, based on the Ant Colony System paradigm, is proposed. Some new public domain benchmark problems are defined, and the algorithm we propose is tested on them. Finally, the method we present is applied to a realistic case study, set up in the city of Lugano (Switzerland

Quadratic Algebra associated with Rational Calogero-Moser Models

Classical Calogero-Moser models with rational potential are known to be superintegrable. That is, on top of the r involutive conserved quantities necessary for the integrability of a system with r degrees of freedom, they possess an additional set of r-1 algebraically and functionally independent globally defined conserved quantities. At the quantum level, Kuznetsov uncovered the existence of a quadratic algebra structure as an underlying key for superintegrability for the models based on A type root systems. Here we demonstrate in a universal way the quadratic algebra structure for quantum rational Calogero-Moser models based on any root systems.Comment: 19 pages, LaTeX2e, no figure

Ant colony optimisation and local search for bin-packing and cutting stock problems

The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimization (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can compete with existing evolutionary methods, whereas the hybrid approach can outperform the best-known hybrid evolutionary solution methods for certain problem classes. The hybrid ACO approach is also shown to require different parameter values from the pure ACO approach and to give a more robust performance across different problems with a single set of parameter values. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO

Element-resolved x-ray ferrimagnetic and ferromagnetic resonance spectroscopy

We report on the measurement of element-specific magnetic resonance spectra at gigahertz frequencies using x-ray magnetic circular dichroism (XMCD). We investigate the ferrimagnetic precession of Gd and Fe ions in Gd-substituted Yttrium Iron Garnet, showing that the resonant field and linewidth of Gd precisely coincide with Fe up to the nonlinear regime of parametric excitations. The opposite sign of the Gd x-ray magnetic resonance signal with respect to Fe is consistent with dynamic antiferromagnetic alignment of the two ionic species. Further, we investigate a bilayer metal film, Ni$_{80}$Fe$_{20}$(5 nm)/Ni(50 nm), where the coupled resonance modes of Ni and Ni$_{80}$Fe$_{20}$ are separately resolved, revealing shifts in the resonance fields of individual layers but no mutual driving effects. Energy-dependent dynamic XMCD measurements are introduced, combining x-ray absorption and magnetic resonance spectroscopies.Comment: 16 pages, 8 figure