Static kinematics for an antagonistically actuated robot based on a beam-mechanics-based model

Soft robotic structures might play a major role in the 4th industrial revolution. Researchers have successfully demonstrated advantages of soft robotics over traditional robots made of rigid links and joints in several application areas including manufacturing, healthcare and surgical interventions. However, soft robots have limited ability to exert higher forces when it comes to interaction with the environment, hence, change their stiffness on demand over a wide range. One stiffness mechanism embodies tendon-driven and pneumatic air actuation in an antagonistic way achieving variable stiffness values. In this paper, we apply a beammechanics-based model to this type of soft stiffness controllable robot. This mathematical model takes into account the various stiffness levels of the soft robotic manipulator as well as interaction forces with the environment at the tip of the manipulator. The analytical model is implemented into a robotic actuation system made of motorised linear rails with load cells (obtaining applied forces to the tendons) and a pressure regulator. Here, we present and analyse the performance and limitations of our model

Fast Convergence for Object Detection by Learning how to Combine Error Functions

In this paper, we introduce an innovative method to improve the convergence speed and accuracy of object detection neural networks. Our approach, CONVERGE-FAST-AUXNET, is based on employing multiple, dependent loss metrics and weighting them optimally using an on-line trained auxiliary network. Experiments are performed in the well-known RoboCup@Work challenge environment. A fully convolutional segmentation network is trained on detecting objects' pickup points. We empirically obtain an approximate measure for the rate of success of a robotic pickup operation based on the accuracy of the object detection network. Our experiments show that adding an optimally weighted Euclidean distance loss to a network trained on the commonly used Intersection over Union (IoU) metric reduces the convergence time by 42.48%. The estimated pickup rate is improved by 39.90%. Compared to state-of-the-art task weighting methods, the improvement is 24.5% in convergence, and 15.8% on the estimated pickup rate.Comment: Accepted for publication at IROS 201

Development and validation of a reference data set for assigning Staphylococcus species based on next-generation sequencing of the 16S-23S rRNA region

Many members of the Staphylococcus genus are clinically relevant opportunistic pathogens that warrant accurate and rapid identification for targeted therapy. The aim of this study was to develop a careful assignment scheme for staphylococcal species based on next-generation sequencing (NGS) of the 16S-23S rRNA region. All reference staphylococcal strains were identified at the species level using Sanger sequencing of the 16S rRNA, sodA, tuf, and rpoB genes and NGS of the 16S-23S rRNA region. To broaden the database, an additional 100 staphylococcal strains, including 29 species, were identified by routine diagnostic methods, 16S rRNA Sanger sequencing and NGS of the 16S-23S rRNA region. The results enabled development of reference sequences encompassing the 16S-23S rRNA region for 50 species (including one newly proposed species) and 6 subspecies of the Staphylococcus genus. This study showed sodA and rpoB targets were the most discriminative but NGS of the 16S-23S rRNA region was more discriminative than tuf gene sequencing and much more discriminative than 16S rRNA gene sequencing. Almost all Staphylococcus species could be distinguished when the max score was 99.0% or higher and the sequence similarity between the best and second best species was equal to or >0.2% (min. 9 nucleotides). This study allowed development of reference sequences for 21 staphylococcal species and enrichment for 29 species for which sequences were publicly available. We confirmed the usefulness of NGS of the 16S-23S rRNA region by identifying the whole species content in 45 clinical samples and comparing the results to those obtained using routine diagnostic methods. Based on the developed reference database, all staphylococcal species can be reliably detected based on the 16S-23S rRNA sequences in samples composed of both single species and more complex polymicrobial communities. This study will be useful for introduction of a novel diagnostic tool, which undoubtedly is an improvement for reliable species identification in polymicrobial samples. The introduction of this new method is hindered by a lack of reference sequences for the 16S-23S rRNA region for many bacterial species. The results will allow identification of all Staphylococcus species, which are clinically relevant pathogens

Development of a reference data set for assigning Streptococcus and Enterococcus species based on next generation sequencing of the 16S-23S rRNA region

Background: Many members of Streptococcus and Enterococcus genera are clinically relevant opportunistic pathogens warranting accurate and rapid identification for targeted therapy. Currently, the developed method based on next generation sequencing (NGS) of the 16S-23S rRNA region proved to be a rapid, reliable and precise approach for species identification directly from polymicrobial and challenging clinical samples. The introduction of this new method to routine diagnostics is hindered by a lack of the reference sequences for the 16S-23S rRNA region for many bacterial species. The aim of this study was to develop a careful assignment for streptococcal and enterococcal species based on NGS of the 16S-23S rRNA region. Methods: Thirty two strains recovered from clinical samples and 19 reference strains representing 42 streptococcal species and nine enterococcal species were subjected to bacterial identification by four Sanger-based sequencing methods targeting the genes encoding (i) 16S rRNA, (ii) sodA, (iii) tuf and (iv) rpoB; and NGS of the 16S-23S rRNA region. Results: This study allowed obtainment and deposition of reference sequences of the 16S-23S rRNA region for 15 streptococcal and 3 enterococcal species followed by enrichment for 27 and 6 species, respectively, for which reference sequences were available in the databases. For Streptococcus, NGS of the 16S-23S rRNA region was as discriminative as Sanger sequencing of the tuf and rpoB genes allowing for an unambiguous identification of 93% of analyzed species. For Enterococcus, sodA, tuf and rpoB genes sequencing allowed for identification of all species, while the NGS-based method did not allow for identification of only one enterococcal species. For both genera, the sequence analysis of the 16S rRNA gene was endowed with a low identification potential and was inferior to that of other tested identification methods. Moreover, in case of phylogenetically related species the sequence analysis of only the intergenic spacer region was not sufficient enough to precisely identify Streptococcus strains at the species level. Conclusions: Based on the developed reference dataset, clinically relevant streptococcal and enterococcal species can now be reliably identified by 16S-23S rRNA sequences in samples. This study will be useful for introduction of a novel diagnostic tool, NGS of the 16S-23S rRNA region, which undoubtedly is an improvement for reliable culture-independent species identification directly from polymicrobially constituted clinical samples

The Problem of Signal and Symbol Integration: A Study of Cooperative Mobile Autonomous Agent Behaviors

This paper explores and reasons about the interplay between symbolic and continuous representations. We first provide some historical perspective on signal and symbol integration as viewed by the Artificial Intelligence (AI), Robotics and Computer Vision communities. The domain of autonomous robotic agents residing in dynamically changing environments anchors well different aspects of this integration and allows us to look at the problem in its entirety. Models of reasoning, sensing and control actions of such agents determine three different dimensions for discretization of the agent-world behavioral state space. The design and modeling of robotic agents, where these three aspects have to be closely tied together, provide a good experimental platform for addressing the signal-to-symbol transformation problem. We present some experimental results from the domain of cooperating mobile agents involved in tasks of navigation and manipulation

A large-scale multi-objective flights conflict avoidance approach supporting 4D trajectory operation

Recently, the long-term conflict avoidance approaches based on large-scale flights scheduling have attracted much attention due to their ability to provide solutions from a global point of view. However, the current approaches which focus only on a single objective with the aim of minimizing the total delay and the number of conflicts, cannot provide the controllers with variety of optional solutions, representing different trade-offs. Furthermore, the flight track error is often overlooked in the current research. Therefore, in order to make the model more realistic, in this paper, we formulate the long-term conflict avoidance problem as a multi-objective optimization problem which minimizes the total delay and reduces the number of conflicts simultaneously. As a complex air route networks needs to accommodate thousands of flights, the problem is a large-scale combinatorial optimization problem with tightly coupled variables, which make the problem difficult to deal with. Hence, in order to further improve the searching capability of the solution algorithm, a cooperative co-evolution (CC) algorithm is also introduced to divide the complex problem into several low dimensional sub-problems which are easier to solve. Moreover, a dynamic grouping strategy based on the conflict detection is proposed to improve the optimization efficiency and to avoid premature convergence. The well-known multi-objective evolutionary algorithm based on decomposition (MOEA/D) is then employed to tackle each sub-problem. Computational results using real traffic data from the Chinese air route network demonstrate that the proposed approach obtained better non-dominated solutions in a more effective manner than the existing approaches, including the multi-objective genetic algorithm (MOGA), NSGAII, and MOEA/D. The results also show that our approach provided satisfactory solutions for controllers from a practical point of view

Semantic parsing for priming object detection in RGB-D Scenes

The advancements in robot autonomy and capabilities for carrying out more complex tasks in unstructured indoors environments can be greatly enhanced by endowing existing environment models with semantic information. In this paper we describe an approach for semantic parsing of indoors environments into semantic categories of Ground, Structure, Furniture and Props. Instead of striving to categorize all object classes and instances encountered in the environment, this choice of semantic labels separates clearly objects and nonobject categories. We use RGB-D images of indoors environments and formulate the problem of semantic segmentation in the Conditional Random Fields Framework. The appearance and depth information enables us induce the graph structure of the random field, which can be effectively approximated by a tree, and to design robust geometric features, which are informative for separation and characterization of different categories. These two choices notably improve the efficiency and performance of the semantic parsing tasks. We carry out the experiments on a NYU V2 dataset and achieve superior or comparable performance and the fraction of computational cost.César Cadena and Jana Kǒseckahttp://www.spme.ws/201

Experiments in behaviour composition

Die vorliegende Arbeit befasst sich mit der unspezifischen Peroxygenase (UPO), einer ausschließlich von Pilzen produzierten Oxidoreduktase. Die Dissertation besteht aus drei wesentlichen Teilen: 1.) Untersuchungen zum Substratspektrum zweier ausgewählter UPOs, 2.) Untersuchungen zur Inaktivierung von UPOs, und 3.) die Etablierung einer Kaskadenreaktion von UPOs und pilzlichen Oxidasen zur Oxidation von Hydroxymethylfurfural (HMF) zu 2,5-Furandicarbonsäure (FDCA). 1) Oxidation ausgewählter organischer Schadstoffe Die Mehrheit der getesteten organischen Schadstoffe (35 von 44 chemischen Verbindungen, vgl. Tabelle 6), inklusive verschiedener Xenobiotika wie chlorierte Benzole und deren Derivate, halogenierte Biphenylether, Nitroaromaten, PAK und Phthalate, wurden durch lediglich zwei UPOs (AaeUPO und MroUPO) oxidativ umgewandelt und damit aktiviert. Die von den UPOs katalysierten Reaktionen waren durch drei Substrat-Eigenschaften limitiert: 1.) sterische Hemmung, u.a. infolge einer hohen Zahl an Substituenten (z.B. Hexachlorbenzol) oder aufgrund der grundsätzliche Sperrigkeit (Größe) des Substrat-Moleküls (z.B. 6-Ring-PAK wie Benzo[g,h,i]perylen), 2.) Inaktivierung des aromatischen Ringes durch elektronenziehende Gruppen (z.B. im Nitrobenzol) und 3.) geringe Bioverfügbarkeit und Wasserlöslichkeit. Während die Verhinderung der Oxidation durch geringe Löslichkeit und sterische Hemmung bereits von Peter (2013) und Poraj-Kobielska (2013) beschrieben wurden, stellt die Limitation durch Substrat-Deaktivierung, wie im Fall des Nitrobenzols, einen neuen Befund. 2) Inaktivierung von UPOs Für alle getesteten UPOs konnte eine intrinsische Katalase-Aktivität nachgewiesen werden, wobei deren Ausmaß unter den getesteten Enzymen stark variierte. So unterschieden sich die katalytischen Effizienzen der Katalase-Aktivität von AaeUPO und rCciUPO um eine Größenordnung, während die der MroUPO sich dazwischen einordnete. Im Rahmen der Untersuchungen konnte für die AaeUPO die Bildung eines reaktionsträgen Intermediats, der UPO-Compound III (cpd-3), nach Exposition gegenüber hohen H2O2-Konzentrationen, gezeigt werden. Außerdem wurde Biliverdin als Abbauprodukt der H2O2-katalysierten Oxidation des UPO-Häms detektiert (inaktivierende Spontan-Oxidation). Diese Verbindung entstand höchstwahrscheinlich durch die Reaktion des Häms mit Hydroxyl-Radikalen (•OH), die wiederum ein Ergebnis der Reaktion der UPO-cpd-3 und H2O2 waren. Als Quintessenz dieser Untersuchungen kann zusammenfassend festgestellt werden, dass es für den schonenden Einsatz und die optimale Performance der UPOs notwendig ist, ein „optimales Verhältnis“ bezüglich der Konzentrationen des Zielsubstrates, des UPO-Proteins und des Peroxids einzustellen. Dieses Verhältnis muss für jede UPO-Substrat-Kombination experimentell empirisch ermittelt werden, wobei als Faustregel gilt: Je niedriger die lokale/stationäre Cosubstrat-Konzentration (H2O2) im Reaktionsmedium ist, desto geringer wird die schädigende Wirkung auf die UPO ausfallen. 3) HMF-Oxidation durch UPOs und pilzliche Oxidasen in einer Kaskaden-Reaktion Obwohl die AaeUPO prinzipiell in der Lage ist, HMF und nahezu jedes seiner primären und sekundären Derivate (mit Ausnahme von 5-Hydroxymethyl-2-furosäure) zu oxidieren, verläuft die vollständige Oxidation nur wenig effizient; insbesondere der letzte Schritt von der 5-formyl-2-furosäure (FFCA) zur FDCA ist reaktionslimitierend. Unter einfachen Reaktionsbedingungen (einmalige Zugabe von H2O2) würde das Peroxid von der AaeUPO unproduktiv verbraucht und das Enzym größtenteils inaktiviert, ohne dass dabei substanzielle Mengen an FDCA gebildet würden. Erst durch die Kombination einer UPO mit geeigneten Oxidasen (Arylalkoholoxidase - AAO und/oder Galactoseoxidase - GAO) konnte die FDCA-Ausbeute substantiell erhöht werden (7,9 mM in 10 mL). Hierbei sind zusammenfassend folgende positive Effekte hervorzuheben: 1.) Oxidasen stellen geeignetes H2O2 für die UPOs bereit, 2.) nur die GAO kann auch intermediär gebildetes HMFCA oxidieren und 3.) durch die schonende Bereitstellung von H2O2 für die UPO verringert sich die lokale Konzentration des Oxidationsmittels soweit, dass eine Schädigung des Enzyms vermieden wird (bei gleichzeitiger substantieller FFCA-Oxidation). Auf dieser Grundlage konnte eine dreistufige enzymatische Synthese von FDCA aus HMF realisiert werden