TechTalks from event: Technical session talks from ICRA 2012

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Micro/Nanoscale Automation II

  • Gripper Synthesis for Indirect Manipulation of Cells Using Holographic Optical Tweezers Authors: Chowdhury, Sagar; Svec, Petr; Wang, Chenlu; Losert, Wolfgang; Gupta, Satyandra K.
    Optical Tweezers (OT) are used for highly accurate manipulations of biological cells. However, the direct exposure of cells to focused laser beam may negatively influence their biological functions. In order to overcome this problem, we generate multiple optical traps to grab and move a 3D ensemble of inert particles such as silica microspheres to act as a reconfigurable gripper for a manipulated cell. The relative positions of the microspheres are important in order for the gripper to be robust against external environmental forces and the exposure of high intensity laser on the cell to be minimized. In this paper, we present results of different gripper configurations, experimentally tested using our OT setup, that provide robust gripping as well as minimize laser intensity experienced by the cell. We developed a computational approach that allowed us to perform preliminary modeling and synthesis of the gripper configurations. The gripper synthesis is cast as a multi-objective optimization problem.
  • Robotic Pick-Place of Nanowires for Electromechanical Characterization Authors: Ye, Xutao; Zhang, Yong; Sun, Yu
    Pick-place of single nanowires inside scanning electron microscopes (SEM) is useful for prototyping functional devices and characterizing nanowires' properties. Nanowire pick-place has been typically performed via teleoperation, which is time-consuming and highly skill-dependent. This paper presents a robotic system capable of automated pick-place of single nanowires. Through SEM visual detection and vision-based motion control, the system transferred individual silicon nanowires from their growth substrate to a microelectromechanical systems (MEMS) device that characterized the nanowires' electromechanical properties. The performance of the nanorobotic pick-up and placement procedures was quantified by experiments. The system demonstrated automated nanowire pick-up and placement with a high reliability.
  • Automated High Throughput Scalable Green Nanomanufacturing for Naturally Occurring Nanoparticles Using English Ivy Authors: Xu, Zhonghua; Lenaghan, Scott; Gilmore, David; Xia, Lijin; Zhang, Mingjun
    The discovery of novel nanomaterials, such as nanoparticles and nanofibers, is crucial to the expansion of the nanotechnology field. Of even greater importance, is the identification of nanomaterials that exist in nature and have low environmental toxicity when compared to man-made nanomaterials. In 2008, our group first discovered that ivy secretes nanoparticles for surface affixing. It was further demonstrated that these nanoparticles could be used for biomedical applications. This paper proposes an automated framework for high throughput scalable green nanomanufacturing of these naturally occurring nanoparticles. Several parameters necessary to optimize the growth of the ivy, including temperature, humidity, and light level, were regulated using feedback controls. Since the contact of ivy rootlets with a substrate is necessary to initiate the secretion of ivy adhesive, an electromechanical system was designed to automatically stimulate the rootlets to start the nanoparticle secretion process. The contact of ivy rootlets with a surface was formulated as a linear viscoelastic model and a speed control law was proposed for the actuator of the automated system. The proposed framework was verified through prototype experiments, and demonstrated promise for high throughput production of ivy nanoparticles.
  • Non-Vector Space Control for Nanomanipulations Based on Compressive Feedbacks Authors: Song, Bo; Zhao, Jianguo; Xi, Ning; Lai, King Wai Chiu; Yang, Ruiguo; Qu, Chengeng
    AFM based nanomanipulations have been successfully applied in various areas such as physics, biology and so forth in nano scale. Traditional nanomanipulations always have to approach the problems such as hysteresis, nonlinearity and thermal drift of the scanner, and the noise brought by the position sensor. In this research, a compressive feedbacks based non-vector space control approach is proposed for improving the accuracy of AFM based nanomanipulations. Instead of sensors, the local image was used as the feedback to a non-vector space controller to generate a closed-loop control for manipulation. In this paper, there are four research topics: First, local scan strategy was used to get a local image. Second, since the feedback is an image, a non-vector space controller was designed to deal with the difficulty in vector space such as calibration and coordinate transformation. Third, in order to further decrease the time spent on local scan, compressive sensing was introduced to this system. Finally, to overcome the disadvantage that compressive sensing costs time on reconstructing the original signal, we directly use the compressive data as the feedback. Both theoretical analysis and experimental results have shown that the system has a good performance on AFM tip motion control. Therefore, the non-vector space control method can make visual servoing easier, and the compressive feedback could make a high speed real-time control of nanomanipulation possible. In addition, thi
  • Nanotool Exchanger System Based on E-SEM Nanorobotic Manipulation System Authors: Nakajima, Masahiro; Kawamoto, Takuya; Kojima, Masaru; Fukuda, Toshio
    A novel nanotool exchanger system is proposed based on Environmental Scanning Electron Microscope (E-SEM) nanorobotic manipulation system. We proposed to use the E-SEM nanomanipulation system for the analysis of biological specimen using various “nanotools” to realize flexible and complex nano-scale stiffness measurement, adhesion force measurement, cutting, and injection. The E-SEM can use to observe the biological samples in nano-scale and real-time without any drying or dyeing processes. As previous works, we applied the system to manipulate biological specimens, such as Caenorhabditis elegans (C. elegans) and yeast cells. To maintain the livable condition of biological cells, it is important to reduce the exchange time of the nanotools. This is also important to improve the efficiency of biological specimen analysis using various nanotools without break the chamber pressure. This paper presents a novel nanotool exchanger system for exchanging different nanotools within the ESEM chamber. Through the nanotool exchanger system, the following advantages are mainly obtained, 1) it is not needed to open the sample chamber to exchange the nanotools and to evacuate the sample chamber pressure again, 2) it is not needed to operate manually to exchange nanotools, 3) it is possible to recover the nanotools by exchanging new one, 4) it is possible to use different tools continuously. Firstly, the design and fabrication are presented for the proposed nanotool adaptor, nanotool attachm
  • Controlled Positioning of Biological Cells Inside a Micropipette Authors: Zhang, Xuping; Leung, Clement; Lu, Zhe; Esfandiari, Navid; Casper, Robert; Sun, Yu
    Manipulating single cells with a micropipette is the oldest, yet still a widely used technique. This paper discusses the positioning of a single cell to a target position inside the micropipette after the cell is aspirated into the micropipette. Due to the small volume of a single cell (pico-liter) and nonlinear dynamics involved, this task has high skill requirements and is labor intensive in manual operation that is solely based on trial and error and has high failure rates. We present automated techniques in this paper for achieving this task. Computer vision algorithm was developed to track a single cell inside a micropipette for automated single-cell positioning. A closed-loop robust controller integrating the dynamics of cell motion was designed to accurately and efficiently position the cell to a target position inside the micropipette. The system achieved high success rates of 97% for cell tracking (n=100) and demonstrated its capability of accurately positioning a cell inside the micropipette within 8 seconds (vs. 25 seconds by highly skilled operators).

Visual Learning

  • 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.
  • Place Representation in Topological Maps Based on Bubble Space Authors: Erkent, Ozgur; Bozma, Isil
    Place representation is a key element in topological maps. This paper presents bubble space - a novel representation for "places" (nodes) in topological maps. The novelties of this model are two-fold: First, a mathematical formalism that defines bubble space is presented. This formalism extends previously proposed bubble memory to accommodate two new variables -- varying robot pose and multiple features. Each bubble surface preserves the local $S^2-$metric relations of the incoming sensory data from the robot's viewpoint. Secondly, for learning and recognition, bubble surfaces can be transformed into bubble descriptors that are compact and rotationally invariant, while being computable in an incremental manner. The proposed model is evaluated with support vector machine based decision making in two different settings: first with a mobile robot placed in a variety of locations and secondly using benchmark visual data.
  • DP-FACT: Towards Topological Mapping and Scene Recognition with Color for Omnidirectional Camera Authors: Liu, Ming; Siegwart, Roland
    Topological mapping and scene recognition problems are still challenging, especially for online realtime vision-based applications. We develop a hierarchical probabilistic model to tackle them using color information. This work is stimulated by our previous work [1] which defined a lightweight descriptor using color and geometry information from segmented panoramic images. Our novel model uses a Dirichlet Process Mixture Model to combine color and geometry features which are extracted from omnidirectional images. The inference of the model is based on an approximation of conditional probabilities of observations given estimated models. It allows online inference of the mixture model in real-time (at 50Hz), which outperforms other existing approaches. A real experiment is carried out on a mobile robot equipped with an omnidirectional camera. The results show the competence against the state-of-art.
  • Acquiring Semantics Induced Topology in Urban Environments Authors: Singh, Gautam; Kosecka, Jana
    Methods for acquisition and maintenance of an environment model are central to a broad class of mobility and navigation problems. Towards this end, various metric, topological or hybrid models have been proposed. Due to recent advances in sensing and recognition, acquisition of semantic models of the environments have gained increased interest in the community. In this work, we will demonstrate a capability of using weak semantic models of the environment to induce different topological models, capturing the spatial semantics of the environment at different levels. In the first stage of the model acquisition, we propose to compute semantic layout of the street scenes imagery by recognizing and segmenting buildings, roads, sky, cars and trees. Given such semantic layout, we propose an informative feature characterizing the layout and train a classifier to recognize street intersections in challenging urban inner city scenes. We also show how the evidence of different semantic concepts can induce useful topological representation of the environment, which can aid navigation and localization tasks. To demonstrate the approach, we carry out experiments on a challenging dataset of omnidirectional inner city street views and report the performance of both semantic segmentation and intersection classification.
  • Large-scale Semantic Mapping and Reasoning with Heterogeneous Modalities Authors: Pronobis, Andrzej; Jensfelt, Patric
    This paper presents a probabilistic framework combining heterogeneous, uncertain, information such as object observations, shape, size, appearance of rooms and human input for semantic mapping. It abstracts multi-modal sensory information and integrates it with conceptual common-sense knowledge in a fully probabilistic fashion. It relies on the concept of spatial properties which make the semantic map more descriptive, and the system more scalable and better adapted for human interaction. A probabilistic graphical model, a chain-graph, is used to represent the conceptual information and perform spatial reasoning. Experimental results from online system tests in a large unstructured office environment highlight the system's ability to infer semantic room categories, predict existence of objects and values of other spatial properties as well as reason about unexplored space.

AI Reasoning Methods

  • An Adaptive Nonparametric Particle Filter for State Estimation Authors: Wang, Yali; Chaib-draa, Brahim
    Particle filter is one of the most widely applied stochastic sampling tools for state estimation problems in practice. However, the proposal distribution in the traditional particle filter is the transition probability based on state equation, which would heavily affect estimation performance in that the samples are blindly drawn without considering the current observation information. Additionally, the fixed particle number in the typical particle filter would lead to wasteful computation, especially when the posterior distribution greatly varies over time. In this paper, an advanced adaptive nonparametric particle filter is proposed by incorporating gaussian process based proposal distribution into KLD-Sampling particle filter framework so that the high-qualified particles with adaptively KLD based quantity are drawn from the learned proposal with observation information at each time step to improve the approximation accuracy and efficiency. Our state estimation experiments on univariate nonstationary growth model and two-link robot arm show that the adaptive nonparametric particle filter outperforms the existing approaches with smaller size of particles.
  • Online Semantic Exploration of Indoor Maps Authors: Liu, Ziyuan; Chen, Dong; v. Wichert, Georg
    In this paper we propose a method to extract an abstracted floor plan from typical grid maps using Bayesian reasoning. The result of this procedure is a probabilistic generative model of the environment defined over abstract concepts. It is well suited for higher-level reasoning and communication purposes. We demonstrate the effectiveness of the approach through real-world experiments.
  • Game Solving for Industrial Automation and Control Authors: Cheng, Chih-Hong; Buckl, Christian; Knoll, Alois; Geisinger, Michael
    An ongoing effort within the community of verification and program analysis is to raise the level of abstraction in programming by automatic synthesis. In this paper, we demonstrate how our synthesis engine GAVS+ achieves this goal by automatically creating control code for the FESTO modular production system. The overall approach is model-driven: we reinterpret planning domain definition language (PDDL) (as a design contract) to model two-player games played between control and environment, such that users can describe (i) basic abilities of hardware components, including sensors (as environment moves) and actuators (as control moves), (ii) topologies how components are interconnected, and (iii) desired specification under a restricted class of linear temporal logic. The model is processed by our game-based synthesis engine, and from which intermediate code is generated. By mapping each behavioral-level action to a sequence of low-level PLC control commands, we transform the intermediate code to an executable program. The efficiency of our engine enables to synthesize every scenario presented in this paper within seconds. When the specification evolves, this implies a huge time-gain compared to manual code modification.
  • Learning Relational Affordance Models for Robots in Multi-Object Manipulation Tasks Authors: Moldovan, Bogdan; Moreno, Plinio; van Otterlo, Martijn; Santos-Victor, José; De Raedt, Luc
    Affordances define the action possibilities on an object in the environment and in robotics they play a role in basic cognitive capabilities. Previous works have focused on affordance models for just one object even though in many scenarios they are defined by configurations of multiple objects that interact with each other. We employ recent advances in statistical relational learning to learn affordance models in such cases. Our models generalize over objects and can deal effectively with uncertainty. Two-object interaction models are learned from robotic interaction with the objects in the world and employed in situations with arbitrary numbers of objects. We illustrate these ideas with experimental results of an action recognition task where a robot manipulates objects on a shelf.
  • Abstract Planning for Reactive Robots Authors: Joshi, Saket; Schermerhorn, Paul; Khardon, Roni; Scheutz, Matthias
    Hybrid reactive-deliberative architectures in robotics combine reactive sub-policies for fast action execution with goal sequencing and deliberation. The need for replanning, however, presents a challenge for reactivity and hinders the potential for guarantees about the plan quality. In this paper, we argue that one can integrate abstract planning provided by symbolic dynamic programming in first order logic into a reactive robotic architecture, and that such an integration is in fact natural and has advantages over traditional approaches. In particular, it allows the integrated system to spend off-line time planning for a policy, and then use the policy reactively in open worlds, in situations with unexpected outcomes, and even in new environments, all by simply reacting to a state change executing a new action proposed by the policy. We demonstrate the viability of the approach by integrating the FODD-Planner with the robotic DIARC architecture showing how an appropriate interface can be defined and that this integration can yield robust goal-based action execution on robots in open worlds.
  • Searching Objects in Large-Scale Indoor Environments: A Decision-Theoretic Approach Authors: Kunze, Lars; Beetz, Michael; Saito, Manabu; Azuma, Haseru; Okada, Kei; Inaba, Masayuki
    Many of today's mobile robots are supposed to perform everyday manipulation tasks autonomously. However, in large-scale environments, a task-related object might be out of the robot's reach. Hence, the robot first has to search for the object in its environment before it can perform the task. In this paper, we present a decision-theoretic approach for searching objects in large-scale environments using probabilistic environment models and utilities associated with object locations. We demonstrate the feasibility of our approach by integrating it into a robot system and by conducting experiments where the robot is supposed to search different objects with various strategies in the context of fetch-and-delivery tasks within a multi-level building.