Hysteresis in the tendons driving continuum robots is frequently regarded as a nuisance and a problem that is best avoided. Some prior work seeks to ameliorate the effects of hysteresis through the selection of materials. Others propose models of hysteresis to compensate for their effects. In this work, we present an empirically validated model of hysteresis in tendon-driven continuum robots. We demonstrate that hysteresis contributes to the stability of these robots by mitigating undesirable tensions in robot's backbone. As a result, a model-based approach to hysteresis can be used not just for compensation of a nuisance, but to enhance the utility of continuum robots in safety critical applications such as medical robots.

This paper addresses the problem of choosing a sparse subset of measurements for quick calibration parameter estimation. A standard solution to this is selecting a measurement only if its utility—the difference between posterior (with the measurement) and prior information (without the measurement)—exceeds some threshold. Theoretically, utility, a function of the parameter estimate, should be evaluated at the estimate obtained with all measurements selected so far, hence necessitating a recalibration with each new measurement. However, we hypothesize that utility is insensitive to changes in the parameter estimate for many systems of interest, suggesting that evaluating utility at some initial parameter guess would yield equivalent results in practice. We provide evidence supporting this hypothesis for extrinsic calibration of multiple inertial measurement units (IMUs), showing the reduction in calibration time by two orders of magnitude by forgoing recalibration for each measurement.

In this paper, we confirm the hypothesis that the magnetic field inside a building can vary significantly as a function of height. We collected data with twenty magnetometers spaced evenly from knee height to head height and mounted to a ground robot, which we drove through two different buildings on the campus of the University of Illinois at Urbana-Champaign. We applied Gaussian process regression to build a map of the magnetic field at each height. We compared these maps and saw pairwise differences of more than 1 μT in up to 20% of each test environment, a threshold that we argue would prevent meeting the requirements of common indoor positioning applications. These results call into question the planar assumption that is commonly made when deriving methods of indoor positioning that are based on the use of magnetic fields.

In this paper, we present a publicly available dataset for the evaluation of indoor positioning algorithms that use magnetic anomalies. Our dataset contains IMU and magnetometer measurements along with ground truth position measurements that have centimeter-level accuracy. To produce this dataset, we collected over 13 hours of data (51 kilometers of total distance traveled) from three different buildings, with sensors both handheld and mounted on a wheeled robot, in environments with and without changes in the placement of objects that affect magnetometer measurements ("live loads”). We conclude the paper with a discussion of why these characteristics of our dataset are important when evaluating positioning algorithms.

We present a passive mechanism for quadrotor vehicles and other hover-capable aerial robots based on the cam-follower. This mechanism has two mating parts, one attached to the quadrotor and the other attached to a payload. These two parts are joined by a toggle switch-push to connect, push to disconnect-that is easy to activate with the quadrotor by varying thrust. We discuss the design parameters and provide an inertial model for our mechanism. With hardware experiments, we demonstrate the use of this passive mechanism to autonomously place a wireless camera in several different locations on the underside of a steel beam. Our mechanism is open source and can be easily fabricated with a 3D printer.

BACKGROUND: As large genomics and phenotypic datasets are becoming more common, it is increasingly difficult for most researchers to access, manage, and analyze them. One possible approach is to provide the research community with several petabyte-scale cloud-based computing platforms containing these data, along with tools and resources to analyze it. METHODS: Bionimbus is an open source cloud-computing platform that is based primarily upon OpenStack, which manages on-demand virtual machines that provide the required computational resources, and GlusterFS, which is a high-performance clustered file system. Bionimbus also includes Tukey, which is a portal, and associated middleware that provides a single entry point and a single sign on for the various Bionimbus resources; and Yates, which automates the installation, configuration, and maintenance of the software infrastructure required. RESULTS: Bionimbus is used by a variety of projects to process genomics and phenotypic data. For example, it is used by an acute myeloid leukemia resequencing project at the University of Chicago. The project requires several computational pipelines, including pipelines for quality control, alignment, variant calling, and annotation. For each sample, the alignment step requires eight CPUs for about 12 h. BAM file sizes ranged from 5 GB to 10 GB for each sample. CONCLUSIONS: Most members of the research community have difficulty downloading large genomics datasets and obtaining sufficient storage and computer resources to manage and analyze the data. Cloud computing platforms, such as Bionimbus, with data commons that contain large genomics datasets, are one choice for broadening access to research data in genomics.

National Center for Data Mining at UICIn our SC06 BWC entry, we will transfer SDSS (Sloan Digital Sky Survey) Data Release 5 (DR5) between the SC06 show floor in Tampa and one of the NCDM labs on the UIC campus. We will use SECTOR, our newly developed distributed data space management system, to transfer DR5 in parallel between two Linux clusters in Tampa and Chicago, respectively. SECTOR transparently manages the file locating and data moving, while it employs UDT for actual data transfer. The data transfer will be from disk to disk over a 10Gb/s shared, router link between SC06 and UIC, via StarLight. We expect to reach 5Gb/s disk-to-disk data transfer rate between the two sites.

No abstract available.