TechTalks from event: Technical session talks from ICRA 2012

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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.

Continuum Robots

  • Development of Linear Inchworm Drive Using Flexible Pneumatic Actuator for Active Scope Camera Authors: Wakana, Kazuhito; Ishikura, Michihisa; Konyo, Masashi; Tadokoro, Satoshi
    Active Scope Camera (ASC) using a linear inchworm drive, which can run on various road surfaces assumed in disaster sites, have been developed as a snake-like rescue robot. However, it is difficult for the linear inchworm drive to run in crooked narrow pathways, because its rigid body actuator reduces the flexibility of the scope camera and becomes immovable when the scope camera is curved. There are many crooked narrow pathways inside collapsed houses and under rubble. ASC's search range could be vastly expanded if ASC can run in such environments. In this paper, we developed a flexible linear actuator, which has the bellows structure and the hollow structure, for ASC in order to solve these problems. The actuator was able to generate large force more than 6 N from 60 kPa of applied pressure even if it was curved at 200 mm bending radius. Moreover, we developed a flexible linear inchworm drive using this actuator.The flexible linear inchworm drive keeps the running characteristics on the various road surfaces of the conventional linear inchworm drive. The minimum width of 80 deg crooked pathway that the flexible linear inchworm drive could run through was 60 mm, which was one-thirds narrower than that of the conventional inchworm drive.
  • Robotic Body Extension Based on Hot Melt Adhesives Authors: Brodbeck, Luzius; Wang, Liyu; Iida, Fumiya
    The capability of extending body structures is one of the most significant challenges in the robotics research and it has been partially explored in self-reconfigurable robotics. By using such a capability, a robot is able to adaptively change its structure from, for example, a wheel like body shape to a legged one to deal with complexity in the environment. Despite their expectations, the existing mechanisms for extending body structures are still highly complex and the flexibility in self-reconfiguration is still very limited. In order to account for the problems, this paper investigates a novel approach to robotic body extension by employing an unconventional material called Hot Melt Adhesives (HMAs). Because of its thermo-plastic and thermo-adhesive characteristics, this material can be used for additive fabrication based on a simple robotic manipulator while the established structures can be integrated into the robot’s own body to accomplish a task which could not have been achieved otherwise. This paper first investigates the HMA material properties and its handling techniques, then evaluates performances of the proposed robotic body extension approach through a case study of a “water scooping” task.
  • Design and Analysis of a Robust, Low-Cost, Highly Articulated Manipulator Enabled by Jamming of Granular Media Authors: Cheng, Nadia; Lobovsky, Maxim; Keating, Steven; Setapen, Adam; Gero, Katy Ilonka; Hosoi, Anette; Iagnemma, Karl
    Hyper-redundant manipulators can be fragile, expensive, and limited in their flexibility due to the distributed and bulky actuators that are typically used to achieve the precision and degrees of freedom (DOFs) required. Here, a manipulator is proposed that is robust, high-force, low-cost, and highly articulated without employing traditional actuators mounted at the manipulator joints. Rather, local tunable stiffness is coupled with off-board spooler motors and tension cables to achieve complex manipulator configurations. Tunable stiffness is achieved by reversible jamming of granular media, which—by applying a vacuum to enclosed grains—causes the grains to transition between solid-like states and liquid-like ones. Experimental studies were conducted to identify grains with high strength-to-weight performance. A prototype of the manipulator is presented with performance analysis, with emphasis on speed, strength, and articulation. This novel design for a manipulator—and use of jamming for robotic applications in general—could greatly benefit applications such as human-safe robotics and systems in which robots need to exhibit high flexibility to conform to their environments.
  • Path Planning for Belt Object Manipulation Authors: Wakamatsu, Hidefumi; Morinaga, Eiji; Arai, Eiji; Hirai, Shinichi
    A method to generate an appropriate path for manipulation of a belt object is proposed. It is important for automatic manipulation of a belt object such as a film/flexible circuit board to generate an appropriate path for a manipulator because such object is flexible in a certain direction but fragile in another direction and an inappropriate path which causes deformation in the fragile direction may lead to wiring disconnection. First, deformation of a rectangular belt object is modeled considering its bending and torsional deformation under the force of gravity. Next, a method to generate a path for belt object manipulation with quasi-static and non-excessive deformation is proposed. After that, deformation and loaded condition in a path generated by our proposed method and those in a common path based on linear interpolation are compared. Finally, the validity of our proposed method is verified by measuring the deformed shape of a polyethylene sheet during manipulation with the generated path.
  • Exact and Efficient Collision Detection for a Multi-Section Continuum Manipulator Authors: Li, Jinglin; Xiao, Jing
    Continuum manipulators, featuring “continuous backbone structures”, are promising for deft manipulation of a wide range of objects under uncertain conditions in less-structured and cluttered environments. A multi-section trunk/tentacle robot is such a continuum manipulator. With a continuum robot, manipulation means a continuous whole arm motion, where the arm is often bent into a continuously deforming concave shape. To approximate such an arm with a polygonal mesh for collision detection is expensive not only because a fine mesh is required to approximate concavity but also because each time the manipulator deforms, a new mesh has to be built for the new configuration. However, most generic collision detection algorithms apply to only polygonal meshes or objects of convex primitives. In this paper, we propose an efficient algorithm for Collision Detection between an Exact Continuum Manipulator (CD-ECoM) and its environments, which is applicable to any continuum manipulator featuring multiple constant-curvature sections. Our test results show that using this algorithm is both accurate and more efficient in both time and space to detect collisions than approximating the continuum manipulator as polygonal meshes and applying an existing generic collision detection algorithm. Our CD-ECoM algorithm is essential for path/trajectory planning of continuum manipulators.
  • Design and Architecture of the Unified Modular Snake Robot Authors: Wright, III, Cornell; Buchan, Austin D; Brown, H. Ben; Geist, Jason C.; Schwerin, Michael; Rollinson, David; Tesch, Matthew; Choset, Howie
    The design of a hyper-redundant serial-linkage snake robot is the focus of this paper. The snake, which consists of many fully enclosed actuators, incorporates a modular architecture. In our design, which we call the Unified Snake, we consider size, weight, power, and speed tradeoffs. Each module includes a motor and gear train, an SMA wire actuated bistable brake, custom electronics featuring several different sensors, and a custom intermodule connector. In addition to describing the Unified Snake modules, we also discuss the specialized head and tail modules on the robot and the software that coordinates the motion.

Robust and Adaptive Control of Robotic Systems

  • A Nonlinear PI and Backstepping Based Controller for Tractor-Steerable Trailer Influenced by Slip Authors: Huynh, Van; Smith, Ryan N.; Kwok, Ngai Ming; Katupitiya, Jayantha
    Autonomous guidance of agricultural vehicles is vital as mechanized farming production becomes more prevalent. It is crucial that tractor-trailers are guided with accuracy in both lateral and longitudinal directions, whilst being affected by large disturbance forces, or slips, owing to uncertain and undulating terrain. Successful research has been concentrated on trajectory control which can provide longitudinal and lateral accuracy if the vehicle moves without sliding, and the trailer is passive. In this paper, the problem of robust trajectory tracking along straight and circular paths of a tractor-steerable trailer is addressed. By utilizing a robust combination of backstepping and nonlinear PI control, a robust, nonlinear controller is proposed. For vehicles subjected to sliding, the proposed controller makes the lateral deviations and the orientation errors of the tractor and trailer converge to a neighborhood near the origin. Simulation results are presented to illustrate that the suggested controller ensures precise trajectory tracking in the presence of slip.
  • Dual-Space Adaptive Control of Redundantly Actuated Parallel Manipulators for Extremely Fast Operations with Load Changes Authors: Sartori Natal, Guilherme; Chemori, Ahmed; Pierrot, François
    This paper deals with the dual-space adaptive control of R4 redundantly actuated parallel manipulator for applications with very high accelerations. This controller is compared experimentally with a dual-space feedforward controller (which may have good performances for specific cases, but has crucial losses of performance when there is any operational change (such as a change of load)), for a pick-and-place task with accelerations of 30G (without payload)and 20G (with a payload of 200g). The objective of this paper is to show that the proposed dual-space adaptive controller not only keeps a very good performance independently of the operational case, but also has a better performance than the dual-space feedforward controller even when this last one is best configured to the given case.
  • Learning Tracking Control with Forward Models Authors: Bocsi, Botond; Hennig, Philipp; Csató, Lehel; Peters, Jan
    Performing task-space tracking control on redundant robot manipulators is a difficult problem. When the physical model of the robot is too complex or not available, standard methods fail and machine learning algorithms can have advantages. We propose an adaptive learning algorithm for tracking control of underactuated or non-rigid robots where the physical model of the robot is unavailable. The control method is based on the fact that forward models are relatively straightforward to learn and local inversions can be obtained via local optimization. We use sparse online Gaussian process inference to obtain a flexible probabilistic forward model and second order optimization to find the inverse mapping. Physical experiments indicate that this approach can outperform state-of-the-art tracking control algorithms in this context.
  • Predictive Gaze Stabilization During Periodic Locomotion Based on Adaptive Frequency Oscillators Authors: Gay, Sébastien; Santos-Victor, José; Ijspeert, Auke
    In this paper we present an approach to the problem of stabilizating the gaze of legged robots using Adaptive Frequency Oscillators to learn the frequency, phase and amplitude of the optical flow and generate compensatory commands during robot locomotion. Assuming periodic and nearly sine shaped motion of the head of the robot, the system successfully stabilizes the gaze of the robot, whether the robot itself is moving, or an external object is moving relative to the robot. We present experiments in simulation and, for object tracking, with a real robotics setup, the Hoap 3, showing that the system can be successfully applied to gaze stabilization during locomotion, even when the feedback loop is very slow and noisy.
  • Learning-Based Model Predictive Control on a Quadrotor: Onboard Implementation and Experimental Results Authors: Bouffard, Patrick; Aswani, Anil; Tomlin, Claire
    In this paper, we present details of the real time implementation onboard a quadrotor helicopter of learning-based model predictive control (LBMPC). LBMPC rigorously combines statistical learning with control engineering, while providing levels of guarantees about safety, robustness, and convergence. Experimental results show that LBMPC can learn physically based updates to an initial model, and how as a result LBMPC improves transient response performance. We demonstrate robustness to mis-learning. Finally, we show the use of LBMPC in an integrated robotic task demonstration---The quadrotor is used to catch a ball thrown with an a priori unknown trajectory.