List of all recorded talks

  • Autonomous Detection of Volcanic Plumes on Outer Planetary Bodies Authors: Lin, Yucong; Bunte, Melissa; Saripalli, Srikanth; Greeley, Ronald
    We experimentally evaluated the efficacy of var- ious autonomous supervised classification techniques for de- tecting transient geophysical phenomena. We demonstrated methods of detecting volcanic plumes on the planetary satellites Io and Enceladus using spacecraft images from the Voyager, Galileo, New Horizons, and Cassini missions. We successfully detected 73-95% of known plumes in images from all four mission datasets. We increased the detection rate by using a training subset. Additionally, we showed that the same tech- niques are applicable to differentiating geologic features, such as plumes and mountains, which exhibit similar appearances in images.
  • Gravity-Independent Mobility and Drilling on Natural Rock Using Microspines Authors: Parness, Aaron; Frost, Matthew; Thatte, Nitish; King, Jonathan
    To grip rocks on the surfaces of asteroids and comets, and to grip the cliff faces and lava tubes of Mars, a 250 mm diameter omni-directional anchor is presented that utilizes a hierarchical array of claws with suspension flexures, called microspines, to create fast, strong attachment. Prototypes have been demonstrated on vesicular basalt and a‘a lava rock supporting forces in all directions away from the rock. Each anchor can support >160 N tangent, >150 N at 45, and >180 N normal to the surface of the rock. A two-actuator selectively-compliant ankle interfaces these anchors to the Lemur IIB robot for climbing trials. A rotary percussive drill was also integrated into the anchor, demonstrating self-contained rock coring regardless of gravitational orientation. As a harder-than-zero-g proof of concept, 20mm diameter boreholes were drilled 83 mm deep in vesicular basalt samples, retaining a 12 mm diameter rock core in 3-6 pieces while in an inverted configuration, literally drilling into the ceiling.
  • Cooperative Micromanipulation Using Optically Controlled Bubble Microrobots Authors: Ishii, Kelly Ann; Hu, Wenqi; Ohta, Aaron
    Optically actuated bubbles in oil were used as microrobots. Simulations of the thermocapillary fluid flow within the oil phase are used to illustrate the mechanisms driving the bubble actuation. Parallel manipulation of sub-millimeter objects including glass beads and hydrogel beads was demonstrated. These capabilities show the potential for using the bubble microrobots in biomedical or other microassembly applications.
  • A Modular Control System for Warehouse Automation - Algorithms and Simulations in USARSim Authors: Miklic, Damjan; Petrovic, Tamara; Coric, Mirko; Piskovic, Zvonimir; Bogdan, Stjepan
    In this paper, we present a control system for a fully autonomous material handling facility. The scenario we are considering is motivated by the 2011 IEEE Virtual Manufacturing Automation Challenge (VMAC). It consists of multiple autonomously guided vehicles (AGVs), transporting pallets of various goods between several input and output locations, through an unstructured warehouse environment. Only a map of the warehouse and a pallet delivery list are provided a priori. Pallets must be delivered to the output locations in the shortest time possible, while respecting the ordering of different pallet types specified by the delivery list. The presented control system handles all aspects of warehouse operation, from individual vehicle control to high-level mission planning and coordination. Delivery mission assignments are optimized using dynamic programming and simulated annealing techniques. Mission executions are coordinated using graph search methods and a modified version of the Banker's algorithm, to ensure safe, collision and deadlock-free system operation. System performance is evaluated on a virtual warehouse model, using the high fidelity USARSim simulator.
  • Wireless Swimming Microrobots: Design and Development of a 2 DoF Magnetic-Based System Authors: Palagi, Stefano; Lucarini, Gioia; Pensabene, Virginia; Levi, Alessandro; Mazzolai, Barbara; Menciassi, Arianna; Beccai, Lucia
    In this work, the design and development of an integrated platform for the steering of swimming microrobot is reported. The system consists of: a near-spherical soft and buoyant magnetic microrobot (with a diameter of about 500 µm) conceived for operation in liquid; a wireless magnetic steering system, including a compact magnetic field generator based on two pairs of Helmholtz and Maxwell coils; an electronic system for their driving; a control software; a joypad physical user interface; and, the micro-arena as working environment. The platform design fulfills the requirements for the “Mobility Task” of the 2011 NIST Mobile Microrobotics Challenge. The results obtained from preliminary validation experiments confirm that the microrobots can move in a fully controlled way, successfully accomplishing an intricate eight-shape path, as required, in the water filled micro-arena. In particular we achieved a maximum average speed of 0.71 mm/s and an exceptionally smooth motion.
  • Toward Fluidic Microrobots Using Electrowetting Authors: Schaler, Ethan; Tellers, Mary; Gerratt, Aaron P.; Penskiy, Ivan; Bergbreiter, Sarah
    This paper describes the performance of a fluidic microrobot using Electrowetting on Dielectric (EWOD). A system to control the fluidic microrobot was designed, constructed and deployed in the NIST Mobile Microrobotics Challenge at ICRA 2011. The microrobots (0.1 M KCl and 550 μm diameter) demonstrated the ability to perform controlled maneuvers in 2-D while transporting hydrophilic objects. The EWOD system is composed of a DIP-mounted die produced via standard microfabrication techniques and containing the control electrodes / competition arena, and a transparent ITO cover slip for grounding. Key advantages of this platform include a scalable design for batch EWOD system fabrication, potential simultaneous control of multiple microrobots, and an easily portable, compact system design.
  • A Textured Object Recognition Pipeline for Color and Depth Image Data Authors: Tang, Jie; Miller, Stephen; Singh, Arjun; Abbeel, Pieter
    We present an object recognition system which leverages the additional sensing and calibration information available in a robotics setting together with large amounts of training data to build high fidelity object models for a dataset of textured household objects. We then demonstrate how these models can be used for highly accurate detection and pose estimation in an end-to-end robotic perception system incorporating simultaneous segmentation, object classification, and pose fitting. The system can handle occlusions, illumination changes, multiple objects, and multiple instances of the same object. The system placed first in the ICRA 2011 Solutions in Perception instance recognition challenge. We believe the presented paradigm of building rich 3D models at training time and including depth information at test time is a promising direction for practical robotic perception systems.
  • The Jacobs Robotics Approach to Object Recognition and Localization in the Context of the ICRA'11 Solutions in Perception Challenge Authors: Vaskevicius, Narunas; Pathak, Kaustubh; Ichim, Alexandru-Eugen; Birk, Andreas
    In this paper, we give an overview of the Jacobs Robotics entry to the ICRA'11 Solutions in Perception Challenge. We present our multi-pronged strategy for object recognition and localization based on the integrated geometric and visual information available from the Kinect sensor. Firstly, the range image is over-segmented using an edge-detection algorithm and regions of interest are extracted based on a simple shape-analysis per segment. Then, these selected regions of the scene are matched with known objects using visual features and their distribution in 3D space. Finally, generated hypotheses about the positions of the objects are tested by back-projecting learned 3D models to the scene using estimated transformations and sensor model.
  • Semi-Parametric Models for Visual Odometry Authors: Guizilini, Vitor; Ramos, Fabio
    This paper introduces a novel framework for estimating the motion of a robotic car from image information (a.k.a. visual odometry). Most current monocular visual odometry algorithms rely on a calibrated camera model and recover relative rotation and translation by tracking image features and applying geometrical constraints. This approach has some drawbacks: translation is recovered up to a scale, it requires camera calibration, and uncertainty estimates are not directly obtained. We propose an alternative approach that involves the use of semi-parametric statistical models as means to recover scale, infer camera parameters and provide uncertainty estimates given a training dataset. As opposed to conventional non-parametric machine learning procedures, where standard models for egomotion would be neglected, we present a novel framework in which the existing parametric models and powerful non-parametric Bayesian learning procedures are combined. We devise a multiple output Gaussian Process procedure, named Coupled GP, that uses a parametric model as the mean function and a non-stationary covariance function to map image features directly into vehicle motion. Additionally, this procedure is also able to infer joint uncertainty estimates for rotation and translation. Experiments performed using data collected from a single camera under challenging conditions show that this technique outperforms traditional methods in trajectories of several kilometers.
  • Efficient On-Line Data Summarization Using Extremum Summaries Authors: Girdhar, Yogesh; Dudek, Gregory
    We are interested in the task of online summarization of the data observed by a mobile robot, with the goal that these summaries could be then be used for applications such as surveillance, identifying samples to be collected by a planetary rover, and site inspections to detect anomalies. In this paper, we pose the summarization problem as an instance of the well known k-center problem, where the goal is to identify k observations so that the maximum distance of any observation from a summary sample is minimized. We focus on the online version of the summarization problem, which requires that the decision to add an incoming observation to the summary be made instantaneously. Moreover, we add the constraint that only a finite number of observed samples can be saved at any time, which allows for applications where the selection of a sample is linked to a physical action such as rock sample collection by a planetary rover. We show that the proposed online algorithm has performance comparable to the offline algorithm when used with real world data.