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A mathematical model for evaporation of water from a urea-water-solution (UWS) droplet and subsequent decomposition of urea, including a semi-detailed kinetic scheme for byproduct formation, was developed. The model provided a good description of experimental water evaporation rates from UWS droplets, but generally underestimated the urea decomposition rates. The depletion of 7-70 µm UWS droplets was investigated at ambient temperatures of 200-400 oC corresponding to urea-SCR conditions. The results indicate that the risk of deposition may be reduced by minimizing the droplet size and maximizing the exhaust temperature and reaction time. The present work can provide guidance for designing an efficient evaporation system, to facilitate a stable SCR operation with high NO reduction.

Background Assigning meaningful probabilities of SARS-CoV-2 infection risk presents a diagnostic challenge across the continuum of care. Objective The aim of this study was to develop and clinically validate an adaptable, personalized diagnostic model to assist clinicians in ruling in and ruling out COVID-19 in potential patients. We compared the diagnostic performance of probabilistic, graphical, and machine learning models against a previously published benchmark model. Methods We integrated patient symptoms and test data using machine learning and Bayesian inference to quantify individual patient risk of SARS-CoV-2 infection. We trained models with 100,000 simulated patient profiles based on 13 symptoms and estimated local prevalence, imaging, and molecular diagnostic performance from published reports. We tested these models with consecutive patients who presented with a COVID-19–compatible illness at the University of California San Diego Medical Center over the course of 14 days starting in March 2020. Results We included 55 consecutive patients with fever (n=43, 78%) or cough (n=42, 77%) presenting for ambulatory (n=11, 20%) or hospital care (n=44, 80%). In total, 51% (n=28) were female and 49% (n=27) were aged Conclusions Decision support models that incorporate symptoms and available test results can help providers diagnose SARS-CoV-2 infection in real-world settings.

Calibration of sensors is fundamental to robust performance for intelligent vehicles. In natural environments, disturbances can easily challenge calibration. One possibility is to use natural objects of known shape to recalibrate sensors. An approach based on recognition of traffic signs, such as stop signs, and use of them for recalibration of cameras is presented. The approach is based on detection, geometry estimation, calibration, and recursive updating. Results from natural environments are presented that clearly show convergence and improved performance., 7 pages, 7 figures, 1 table, Accepted to ICRA 2021

Recent advancements in statistical learning and computational abilities have enabled autonomous vehicle technology to develop at a much faster rate. While many of the architectures previously introduced are capable of operating under highly dynamic environments, many of these are constrained to smaller-scale deployments, require constant maintenance due to the associated scalability cost with high-definition (HD) maps, and involve tedious manual labeling. As an attempt to tackle this problem, we propose to fuse image and pre-built point cloud map information to perform automatic and accurate labeling of static landmarks such as roads, sidewalks, crosswalks, and lanes. The method performs semantic segmentation on 2D images, associates the semantic labels with point cloud maps to accurately localize them in the world, and leverages the confusion matrix formulation to construct a probabilistic semantic map in bird's eye view from semantic point clouds. Experiments from data collected in an urban environment show that this model is able to predict most road features and can be extended for automatically incorporating road features into HD maps with potential future work directions., 6 pages, 7 figures, IROS 2020

With the recent development of autonomous vehicle technology, there have been active efforts on the deployment of this technology at different scales that include urban and highway driving. While many of the prototypes showcased have been shown to operate under specific cases, little effort has been made to better understand their shortcomings and generalizability to new areas. Distance, uptime and number of manual disengagements performed during autonomous driving provide a high-level idea on the performance of an autonomous system but without proper data normalization, testing location information, and the number of vehicles involved in testing, the disengagement reports alone do not fully encompass system performance and robustness. Thus, in this study a complete set of metrics are applied for benchmarking autonomous vehicle systems in a variety of scenarios that can be extended for comparison with human drivers and other autonomous vehicle systems. These metrics have been used to benchmark UC San Diego's autonomous vehicle platforms during early deployments for micro-transit and autonomous mail delivery applications., 6 pages, 7 figures, IROS 2020

COVID-19 may drive sustained research in robotics to address risks of infectious diseases.

We discuss the process of building semantic maps, how to interactively label entities in them, and how to use them to enable context-aware navigation behaviors in human environments. We utilize planar surfaces, such as walls and tables, and static objects, such as door signs, as features for our semantic mapping approach. Users can interactively annotate these features by having the robot follow him/her, entering the label through a mobile app, and performing a pointing gesture toward the landmark of interest. Our gesture-based approach can reliably estimate which object is being pointed at, and detect ambiguous gestures with probabilistic modeling. Our person following method attempts to maximize future utility by search for future actions assuming constant velocity model for the human. We describe a method to extract metric goals from a semantic map landmark and to plan a human aware path that takes into account the personal spaces of people. Finally, we demonstrate context awareness for person following in two scenarios: interactive labeling and door passing.We believe that future navigation approaches and service robotics applications can be made more effective by further exploiting the structure of human environments.

Sulfur dioxide, formed in combustion of sulfur-rich fuels in diesel engines, may oxidize and react with water to form corrosive H2SO4. However, the SO2 may also be absorbed in the lube oil and consume CaCO3-containing reverse micelles. In this study, the CaCO3 + SO2 reaction was investigated in a batch reactor setup at temperatures and pressures similar to those on the cylinder liner in an engine. The conversion of CaCO3 and the formation of products were determined by Fourier Transform Infrared Spectroscopy (FTIR). CaSO3 was the main product, but CaSO4 was observed at extended residence times and increased temperature. The SO2-CaCO3 reaction exhibited only a small temperature dependence; the increase in the rate constant with temperature was partly off-set because the absorption of SO2 in the lube oil emulsion decreases at increased temperature. The reaction rate increased slightly with the initial water concentration due to increased SO2 absorbance. A mathematical model for the batch reactor was set up and kinetic parameters were determined by fitting predictions to the experimental data. The model was then used to predict the CaCO3 conversion in lube oil from SO2 for conditions relevant to a full-scale engine application. Simulations showed that consumption of CaCO3 from SO2 is insignificant in a two-stroke marine diesel engine application and that the H2SO4-CaCO3 reaction is far more important than the SO2-CaCO3 reaction.

A kinetic model for urea decomposition and byproduct formation was developed and validated against thermogravimetric analyses (TGA) of urea and urea-derived byproducts, including biuret and cyanuric acid, at heating rates up to 500 °C/min. The model provided a good description of urea decomposition at all investigated heating rates. The results indicate that an increase in heating rate causes a shift in production of cyanuric acid into ammelide and limits the overall byproduct formation. Additionally, it is shown that a long reaction time is necessary to fully decompose urea at temperatures corresponding to SCR applications.

In recent years, there has been a rapid increase in the number of service robots deployed for aiding people in their daily activities. Unfortunately, most of these robots require human input for training in order to do tasks in indoor environments. Successful domestic navigation often requires access to semantic information about the environment, which can be learned without human guidance. In this paper, we propose a set of DEDUCE - Diverse scEne Detection methods in Unseen Challenging Environments algorithms which incorporate deep fusion models derived from scene recognition systems and object detectors. The five methods described here have been evaluated on several popular recent image datasets, as well as real-world videos acquired through multiple mobile platforms. The final results show an improvement over the existing state-of-the-art visual place recognition systems., Paper accepted at IROS'19

This paper presents practical design and hardware implementation issues of self-configuring swarms of autonomous mobile robots. For the purpose, we develop a new low-cost position detection system that we call dual rotating infrared (DRIr) sensor. The DRIr sensor can provide robots with advanced sensing capabilities that give reliable information about the position and surface geometry of neighboring robots and obstacles. Special focus is placed on how to realize the observation and object identification of mobile robots through the use of DRIr sensors. We verify the functionality and performance of the DRIr sensors mounted on a commercial mobile robot. Experimental results show that a swarm of mobile robots equipped with the DRIr sensors can autonomously configure themselves into an area.

This paper proposes an adaptive CPG based controller for a lower limb prosthesis consisting of online trajectory generation and interlimb coordination. The adaptive CPG can produce multidimensional rhythmic patterns and modulate their frequency by tuning relevant parameters in an autonomously way adapting to a changing periodicity of external signals. Also, to increase the stability of the prosthesis, a spring-damper component is attached between the hip and ankle joints, allowing the absorption of impulsive ground reaction forces at landing. We verify the validity of the proposed controller with a simulated humanoid robot through the investigation of the self-coordination between the healthy and robotic legs.

This paper proposes a CPG-based control architecture using a frequency-adaptive oscillator for undulatory locomotion of snake-like robots. The control architecture consists of a network of neural oscillators that generates desired oscillatory output signals with specific phase lags. A key feature of the proposed architecture is a self-adaptation process that modulates the parameters of the CPG to adapt the motion of the robot to varying coefficients of body-ground friction. This process is based on the frequency-adaptation rule of the oscillator that is designed to learn the periodicity of sensory feedback signals. It has an important meaning of establishing a closed-loop CPG much more robust against environmental and/or system parameter changes. We verify the validity of the proposed locomotion control system employing a simulated snake-like robot moving over terrains with different friction coefficients with a constant velocity.

We address the problem of dispersing a large number of autonomous mobile robots toward building wireless ad hoc sensor networks performing environmental monitoring and control. For the purpose, we propose the adaptive triangular mesh generation algorithm that enables robots to generate triangular meshes of various sizes adapting to changing environmental conditions. A locally interacting, geometric technique allows robots to generate each triangular mesh with their two neighbor robots. Specifically, we have assumed that robots are not allowed to have the identifier, any pre-determined leaders or common coordinate systems, and any explicit communication. Under such minimal conditions, the positions of the robots were shown to converge to the desired distribution, which was mathematically proven and also verified through extensive simulations. Our preliminary results indicate that the proposed algorithm can be applied to the problem regarding the coverage of an area of interest by a swarm of mobile sensors.

© The Author(s) 2015. In this paper we present algorithms and experiments for multi-scale assembly of complex structures by multi-robot teams. We also focus on tasks where successful completion requires multiple types of assembly operations with a range of precision requirements. We develop a hierarchical planning approach to multi-scale perception in support of multi-scale manipulation, in which the resolution of the perception operation is matched with the required resolution for the manipulation operation. We demonstrate these techniques in the context of a multi-step task where robots assemble large box-like objects, inspired by the assembly of an airplane wing. The robots begin by transporting a wing panel, a coarse manipulation operation that requires a wide field of view, and gradually shifts to a narrower field of view but with more accurate sensors for part alignment and fastener insertion. Within this framework we also provide for failure detection and recovery: upon losing track of a feature, the robots retract to using wider field of view systems to re-localize. Finally, we contribute collaborative manipulation algorithms for assembling complex large objects. First, the team of robots coordinates to transport large assembly parts which are too heavy for a single robot to carry. Second, the fasteners and parts are co-localized for robust insertion and fastening. We implement these ideas using four KUKA youBot robots and present experiments where our robots successfully complete all 80 of the attempted fastener insertion operations.

This chapter describes the systematics and evolution of Pasteurellaceae with emphasis on new information generated since the 3rd edition of The Prokaryotes which only included chapters dealing with Haemophilus, Actinobacillus, and Pasteurella. A major source of new information for the current chapter has been provided by whole genome sequences now available for many taxa of the family. Some 100 species and species-like taxa have been documented and 18 genera of Pasteurellaceae reported so far. Members of the family include specialized commensals, potential pathogens, or pathogens of vertebrates and mainly survive poorly in other habitats including the external environment. The pathogenic members are of major importance to animal production and human health. Members of Pasteurellaceae have relatively small genomes, probably as a result of adaption to a special habitat. The most important species in veterinary microbiology include Pasteurella multocida, Actinobacillus pleuropneumoniae, [Haemophilus] parasuis, Mannheimia haemolytica, Bibersteinia trehalosi, and Avibacterium paragallinarum, while Haemophilus influenzae and Aggregatibacter actinomycetemcomitans represent the most important species as to human disease. Traditional isolation techniques are still used in both human and veterinary clinical diagnostic laboratories although genetically based diagnostic methods have replaced traditional biochemical/physiological methods for characterization and identification. For all species, MALDI-TOF can now be used as a diagnostic tool. As control and if MALDI-TOF equipment is not at hand, PCR-based specific detection is possible for Pasteurella multocida, Actinobacillus pleuropneumoniae, [Haemophilus] parasuis, Mannheimia haemolytica, Avibacterium paragallinarum, Gallibacterium anatis, Haemophilus influenzae, and Aggregatibacter actinomycetemcomitans. A lot of work has been directed towards identification of virulence factors and understanding host microbe interactions involved in disease.

This paper describes the robot vision track that has been proposed to the ImageCLEF@ICPR2010 participants. The track addressed the problem of visual place classification. Participants were asked to classify rooms and areas of an office environment on the basis of image sequences captured by a stereo camera mounted on a mobile robot, under varying illumination conditions. The algorithms proposed by the participants had to answer the question "where are you?" (I am in the kitchen, in the corridor, etc) when presented with a test sequence imaging rooms seen during training (from different viewpoints and under different conditions), or additional rooms that were not imaged in the training sequence. The participants were asked to solve the problem separately for each test image (obligatory task). Additionally, results could also be reported for algorithms exploiting the temporal continuity of the image sequences (optional task). A total of eight groups participated to the challenge, with 25 runs submitted to the obligatory task, and 5 submitted to the optional task. The best result in the obligatory task was obtained by the Computer Vision and Geometry Laboratory, ETHZ, Switzerland, with an overall score of 3824.0. The best result in the optional task was obtained by the Intelligent Systems and Data Mining Group, University of Castilla-La Mancha, Albacete, Spain, with an overall score of 3881.0.

In this paper we describe a method for bridging internet time delays in a teleoperation scenario. In the scenario, the sizes of the time delays is not only stochastic, but it is also large compared to the task execution time. The method proposed uses minimum jerk motion models to predict the input from the user a time into the future that is equivalent to, he one-way communication delay. We present results from a teleoperated ball-catching experiment with real internet delays, where we show that the proposed method makes a significant improvement over traditional methods for teleoperation over intercontinental distances. Copyright 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.QC 20110509

As mass-market video game controllers have become more advanced, there has been a recent increase in interest for using these as intuitive and inexpensive control devices. In this paper we examine position control for a robot using a wiimote game controller. We show that human motion models can be used to achieve better precision than traditional tracking approaches, sufficient for simpler tasks. We also present an experiment that shows that very intuitive control can be achieved, as novice subjects can control a robot arm through simple tasks after just a few minutes of practice and minimal instructions.

The paper presents an HRI architecture for human-augmented mapping, which has been implemented and tested on an autonomous mobile robotic platform. Through interaction with a human, the robot can augment its autonomously acquired metric map with qualitative information about locations and objects in the environment. The system implements various interaction strategies observed in independently performed Wizard-of-Oz studies. The paper discusses an ontology-based approach to multi-layered conceptual spatial mapping that provides a common ground for human-robot dialogue. This is achieved by combining acquired knowledge with innate conceptual commonsense knowledge in order to infer new knowledge. The architecture bridges the gap between the rich semantic representations of the meaning expressed by verbal utterances on the one hand and the robot's internal sensor-based world representation on the other. It is thus possible to establish references to spatial areas in a situated dialogue between a human and a robot about their environment. The resulting conceptual descriptions represent qualitative knowledge about locations in the environment that can serve as a basis for achieving a notion of situational awareness.

In this paper we present ongoing work on how to incorporate human motion models into the design of a high performance teleoperation platform. A short description of human motion models used for ball-catching is followed by a more detailed study of a teleoperation platform on which to conduct experiments. Also, a pilot study using minimum jerk theory to explain user input behavior in teleoperated catching is presented. NOTICE: this is the author’s version of a work that was accepted for publication in Neural Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neural Networks, VOL 21, ISSUE 4, May 2008 DOI: 10.1016/j.neunet.2008.04.002QC 20110530

In this paper we present a design study and technical specifications of a high performance robotic manipulator to be used for ball catching experiments using commercial off-the-shelf (COTS) components. Early evaluation shows that very good performance can be achieved using standardized PowerCube actuator modules from Amtec and a standard workstation using CAN bus communication. Implementation issues of low-level control and software platform are also described, as well as early experimental evaluation of the system.

In this paper, a new feature representation for simultaneous localization and mapping (SLAM) is discussed. The representation addresses feature symmetries and constraints explicitly to make the basic model numerically robust. In previous SLAM work, complete initialization of features is typically performed prior to introduction of a new feature into the map. This results in delayed use of new data. To allow early use of sensory data, the new feature representation addresses the use of features that initially have been partially observed. This is achieved by explicitly modelling the subspace of a feature that has been observed. In addition to accounting for the special properties of each feature type, the commonalities can be exploited in the new representation to create a feature framework that allows for interchanging of SLAM algorithms, sensor and features. Experimental results are presented using a low-cost Web-cam, a laser range scanner, and combinations thereof. QC 20160315

The present paper examines minimum jerk models for human kinematics as a tool to predict user input in teleoperation with significant dynamics. Predictions of user input can be a powerful tool to bridge time-delays and to trigger autonomous sub-sequences. In this paper an example implementation is presented, along with the results of a pilot experiment in which a virtual reality simulation of a teleoperated ball-catching scenario is used to test the predictive power of the model. The results show that delays up to 100 ms can potentially be bridged with this approach. Copyright 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.QC 20110908

Staphylococci are part of the normal microflora of humans and animals and some are potential pathogens that have become resistant to almost all known antibiotics. Despite the widespread reports of penicillin resistance in cat and dog staphylococci, the mechanism underlying penicillin resistance has not been examined. This study was aimed at investigating the molecular basis of resistance to penicillin in cat and dog staphylococcal isolates that showed phenotypic resistance to beta-lactam antibiotics. An 861 bp fragment of the structural blaZ gene which codes for beta-lactamase production in staphylococci was amplified by polymerase chain reaction (PCR) and the products were sequenced. Sequenced fragments were analysed by protein signature typing and sequences were compared to published blaZ sequences of human and bovine staphylococcal strains held in a public database. Four known protein signature types (1, 3, 5 and 6) and one new type (12) were identified in this study. When sequences were compared with published blaZ sequences, gene phylogenetic analysis revealed three major groups. The four variants of beta-lactamases types (A, B, C and D) belonged to each major group except for types A and D which were both in group II. These findings confirm that the blaZ gene is responsible for beta-lactamase production leading to subsequent resistance to beta-lactam antibiotics in feline and canine staphylococci and that the gene shows similar diversity and relatedness as found with blaZ sequences obtained from human and bovine staphylococci.

A teleoperation system for controlling a robot with fast dynamics over the Internet has been constructed. It employs a predictive control structure with an accurate dynamic model of the robot to overcome problems caused by varying delays. The operator interface uses a stereo virtual reality display of the robot cell, and a haptic device for force feed-back including virtual obstacle avoidance forces. Copyright 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.QC 20111006

In this paper we combine a graphical approach for simultaneous localization and mapping, SLAM, with a feature representation that addresses symmetries and constraints in the feature coordinates, the measurement subspace, M-space. The graphical method has the advantages of delayed linearizations and soft commitment to feature measurement matching. It also allows large maps to be built up as a network of small local patches, star nodes. This local map net is then easier to work with. The formation of the star nodes is explicitly stable and invariant with all the symmetries of the original measurements. All linearization errors are kept small by using a local frame. The construction of this invariant star is made clearer by the M-space feature representation. The M-space allows the symmetries and constraints of the measurements to be explicitly represented. We present results using both vision and laser sensors. QC 20111010

Path Optimization for Nonholonomic Systems: Application to Reactive Obstacle Avoidance and Path Planning.- From Dynamic Programming to RRTs: Algorithmic Design of Feasible Trajectories.- Control of Nonprehensile Manipulation.- Motion Planning and Control Problems for Underactuated Robots.- Motion Description Languages for Multi-Modal Control in Robotics.- Polynomial Design of Dynamics-based Information Processing System.- Actuation Methods For Human-Centered Robotics and Associated Control Challenges.- Control of a Flexible Manipulator with Noncollocated Feedback: Time Domain Passivity Approach.- Cartesian Compliant Control Strategies for Light-Weight, Flexible Joint Robots.- Toward the Control of Self-Assembling Systems.- Towards Abstraction and Control for Large Groups of Robots.- Omnidirectional Sensing for Robot Control.- A Passivity Approach to Vision-based Dynamic Control of Robots with Nonlinear Observer.- Visual Servoing Along Epipoles.- Toward Geometric Visual Servoing.- Vision-Based Online Trajectory Generation and Its Application to Catching.- Stability Analysis of Invariant Visual Servoing and Robustness to Parametric Uncertainties.

The visual robustness of biological systems is in part due to their ability to actively integrate (fuse) information from a number of visual cues. In addition to active integration, the perception-action nature of biological vision demands event-driven behavioral composition. Providing mechanical vision systems with similar capabilities therefore requires tools and techniques for cue integration and behavioral composition. In this paper, we address two issues. First, we present a unified approach for handling both active integration and behavioral composition. The approach combines a theoretical framework that handles uncertainty using a voting scheme with a set of behaviors that are committed to achieving a specific goal through common effort and a well-known process composition model. Secondly, we address the issue of integration in the active vision activity of smooth pursuit. We have experimented with the fusion of four smooth pursuit techniques (blob tracking, edge tracking, template matching and image differencing). We discuss each technique, highlighting their strengths and weaknesses, and then show that fusing the techniques according to our formal framework improves system tracking behavior.

The paper presents a viewpoint planning strategy which automatically guides a movable camera from an arbitrary position to a position where the optical axis is perpendicular to a plane spanned by any two intersecting edges on a polyhedral object, i.e., a junction. In related work it is common to use the changing length of edge segments to control such alignment, but we demonstrate the use of the apparent angle between the edge segments of the junction. Based on the control on the apparent angle we achieve robustness as well as independence of distance to the object and focal length. The strategy is able to determine the true angle of a junction with an accuracy of approximately 1/spl deg/, and align with an accuracy of approximately 6/spl deg/.

Through adaptation of theoretical techniques from control theory and computer science it becomes possible to provide well posed expert systems for the control of mobile robots. In contrast to other expert systems for mobile robotics the adapted framework enables structured design and verification of implemented systems. This in turn results in a modular system, and a system with a predictable performance. The authors describe how a behaviour based system can be controlled by a rule based expert system, where the interaction between behaviours is described in terms of a process algebra, where different processes are modelled as discrete event systems, and compositions are controlled by a supervisory control-structure. To verify the described framework experimental results obtained with a prototype system are also outlined.

When visual behaviors are combined to provide a specific functionality needed for a task, the combination is often based on heuristic rules. In this article we show that by adopting the Discrete-Event Systems (DES) formalism for describing the interaction between visual behaviors it is possible to provide systems that have well-defined properties in terms of observability and controllability. The method is in particular suited for describing the coupling between action and perception. An introduction to the use of DES is provided and it is demonstrated how DES are used for modeling behaviors and controlling a mobile robot equipped with a binocular camera head and some additional sensors.

We present a view planning approach to determining angles between two-line junctions on an object. Using view planning we demonstrate an ability to determine such angles without reconstruction of 3D properties, and without using known camera displacement. The core in our approach is a two-step view planning strategy based on two simple motion patterns. The view planning is controlled using local extrema in the apparent (viewed) angle. We demonstrate the approach on real images and experiments show an accuracy in determined angle which is better than /spl plusmn/1 degrees.