Slide-Down Prevention for Wheeled Mobile Robots on Slopes

Wheeled mobile robots on inclined terrain can slide down due to loss of traction and gravity. This type of instability, which is different from tip-over, can provoke uncontrolled motion or get the vehicle stuck. This paper proposes slide-down prevention by real-time computation of a straightforward stability margin for a given ground-wheel friction coefficient. This margin is applied to the case study of Lazaro, a hybrid skid-steer mobile robot with caster-leg mechanism that allows tests with four or five wheel contact points. Experimental results for both ADAMS simulations and the actual vehicle demonstrate the effectiveness of the proposed approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Human and Object Recognition with a High-resolution tactile sensor

This paper 1 describes the use of two artificial intelligence methods for object recognition via pressure images from a high-resolution tactile sensor. Both meth- ods follow the same procedure of feature extraction and posterior classification based on a supervised Supported Vector Machine (SVM). The two approaches differ on how features are extracted: while the first one uses the Speeded-Up Robust Features (SURF) descriptor, the other one employs a pre-trained Deep Convolutional Neural Network (DCNN). Besides, this work shows its applica- tion to object recognition for rescue robotics, by distinguishing between differ- ent body parts and inert objects. The performance analysis of the proposed methods is carried out with an experiment with 5-class non-human and 3-class human classification, providing a comparison in terms of accuracy and compu-tational load. Finally, it is discussed how feature-extraction based on SURF can be obtained up to five times faster compared to DCNN. On the other hand, the accuracy achieved using DCNN-based feature extraction can be 11.67% superior to SURF.Proyecto DPI2015-65186-R European Commission under grant agreement BES-2016-078237. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

3D Segmentation Method for Natural Environments based on a Geometric-Featured Voxel Map

This work proposes a new segmentation algorithm for three-dimensional dense point clouds and has been specially designed for natural environments where the ground is unstructured and may include big slopes, non-flat areas and isolated areas. This technique is based on a Geometric-Featured Voxel map (GFV) where the scene is discretized in constant size cubes or voxels which are classified in flat surface, linear or tubular structures and scattered or undefined shapes, usually corresponding to vegetation. Since this is not a point-based technique the computational cost is significantly reduced, hence it may be compatible with Real-Time applications. The ground is extracted in order to obtain more accurate results in the posterior segmentation process. The scene is split into objects and a second segmentation in regions inside each object is performed based on the voxel’s geometric class. The work here evaluates the proposed algorithm in various versions and several voxel sizes and compares the results with other methods from the literature. For the segmentation evaluation the algorithms are tested on several differently challenging hand-labeled data sets using two metrics, one of which is novel.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Optimizing Scan Homogeneity for Building Full-3D Lidars based on Rotating a Multi-Beam Velodyne Rangefinder

Multi-beam lidar (MBL) scanners are compact, light, and accessible 3D sensors with high data rates, but they offer limited vertical resolution and field of view (FOV). Some recent robotics research has profited from the addition of a degree-of-freedom (DOF) to an MBL to build rotating multi-beam lidars (RMBL) that can achieve high-resolution scans with full spherical FOV. In a previous work, we offered a methodology to analyze the complex 3D scan measurement distributions produced by RMBLs with a rolling DOF and no pitching. In this paper, we investigate the effect of introducing constant pitch angles in the construction of the RMBLs with the purpose of finding a kinematic configuration that optimizes scan homogeneity with a spherical FOV. To this end, we propose a scalar index of 3D sensor homogeneity that is based on the spherical formulation of Ripley's K function. The optimization is performed for the widely used Puck (VLP-16) and HDL-32 sensors by Velodyne.This work was partially funded by the Spanish project {DPI2015-65186-R}. The publication has received support from Universidad de Málaga, Campus de Excelencia Andalucía Tech

Experimental phase functions of mm-sized cosmic dust grains

We present experimental phase functions of three types of millimeter-sized dust grains consisting of enstatite, quartz and volcanic material from Mount Etna, respectively. The three grains present similar sizes but different absorbing properties. The measurements are performed at 527 nm covering the scattering angle range from 3 to 170 degrees. The measured phase functions show two well defined regions i) soft forward peaks and ii) a continuous increase with the scattering angle at side- and back-scattering regions. This behavior at side- and back-scattering regions are in agreement with the observed phase functions for the Fomalhaut and HR 4796A dust rings. Further computations and measurements (including polarization) for millimeter sized-grains are needed to draw some conclusions about the fluffy or compact structure of the dust grains

Adoption of GMHT Crops: Coexistence Policy Consequences in the European Union

Agricultural and Food Policy, Crop Production/Industries,

Fuzzy Inference System for VOLT/VAR control in distribution substations in isolated power systems

This paper presents a fuzzy inference system for voltage/reactive power control in distribution substations. The purpose is go forward to automation distribution and its implementation in isolated power systems where control capabilities are limited and it is common using the same applications as in continental power systems. This means that lot of functionalities do not apply and computational burden generates high response times. A fuzzy controller, with logic guidelines embedded based upon heuristic rules resulting from operators at dispatch control center past experience, has been designed. Working as an on-line tool, it has been tested under real conditions and it has managed the operation during a whole day in a distribution substation. Within the limits of control capabilities of the system, the controller maintained successfully an acceptable voltage profile, power factor values over 0,98 and it has ostensibly improved the performance given by an optimal power flow based automation system

Mobile Robot Lab Project to Introduce Engineering Students to Fault Diagnosis in Mechatronic Systems

This document is a self-archiving copy of the accepted version of the paper. Please find the final published version in IEEEXplore: http://dx.doi.org/10.1109/TE.2014.2358551This paper proposes lab work for learning fault detection and diagnosis (FDD) in mechatronic systems. These skills are important for engineering education because FDD is a key capability of competitive processes and products. The intended outcome of the lab work is that students become aware of the importance of faulty conditions and learn to design FDD strategies for a real system. To this end, the paper proposes a lab project where students are requested to develop a discrete event dynamic system (DEDS) diagnosis to cope with two faulty conditions in an autonomous mobile robot task. A sample solution is discussed for LEGO Mindstorms NXT robots with LabVIEW. This innovative practice is relevant to higher education engineering courses related to mechatronics, robotics, or DEDS. Results are also given of the application of this strategy as part of a postgraduate course on fault-tolerant mechatronic systems.This work was supported in part by the Spanish CICYT under Project DPI2011-22443

Strong bound between trace distance and Hilbert-Schmidt distance for low-rank states

The trace distance between two quantum states, $\rho$ and $\sigma$, is an operationally meaningful quantity in quantum information theory. However, in general it is difficult to compute, involving the diagonalization of $\rho - \sigma$. In contrast, the Hilbert-Schmidt distance can be computed without diagonalization, although it is less operationally significant. Here, we relate the trace distance and the Hilbert-Schmidt distance with a bound that is particularly strong when either $\rho$ or $\sigma$ is low rank. Our bound is stronger than the bound one could obtain via the norm equivalence of the Frobenius and trace norms. We also consider bounds that are useful not only for low-rank states but also for low-entropy states.Comment: 4 page