TechTalks from event: Technical session talks from ICRA 2012

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Embodied Soft Robots

  • Design and Development of a Soft Robotic Octopus Arm Exploiting Embodied Intelligence Authors: Cianchetti, Matteo; Follador, Maurizio; Mazzolai, Barbara; Dario, Paolo; Laschi, Cecilia
    The octopus is a marine animal whose body has no rigid structures. It has eight arms mainly composed of muscles organized in a peculiar structure, named muscular hydrostat, that can change stiffness and that is used as a sort of a modifiable skeleton. Furthermore, the morphology of the arms and the mechanical characteristics of their tissues are such that the interaction with the environment, namely water, is exploited to simplify the control of movements. From these considerations, the octopus emerges as a paradigmatic example of embodied intelligence and a good model for soft robotics. In this paper the design and the development of an artificial muscular hydrostat are reported, underling the efforts in the design and development of new technologies for soft robotics, like materials, mechanisms, soft actuators. The first prototype of soft robot arm is presented, with experimental results that show its capability to perform the basic movements of the octopus arm (like elongation, shortening, and bending) and demonstrate how embodiment can be effective in the design of robots.
  • The Application of Embodiment Theory to the Design and Control of an Octopus-Like Robotic Arm Authors: Guglielmino, Emanuele; Zullo, Letizia; Cianchetti, Matteo; Follador, Maurizio; Branson, David; Caldwell, Darwin G.
    This paper examines the design and control of a robotic arm inspired by the anatomy and neurophysiology of Octopus vulgaris in light of embodiment theory. Embodiment in an animal is defined as the dynamic coupling between sensory-motor control, anatomy, materials, and the environment that allows for the animal to achieve effective behaviour. Octopuses in particular are highly embodied and dexterous animals: their arms are fully flexible, can bend in any direction, grasp objects and modulate stiffness along their length. In this paper the biomechanics and neurophysiology of octopus have been analysed to extract relevant information for use in the design and control of an embodied soft robotic arm. The embodied design requirements are firstly defined, and how the biology of the octopus meets these requirements presented. Next, a prototype continuum arm and control architecture based on octopus biology, and meeting the design criteria, are presented. Finally, experimental results are presented to show how the developed prototype arm is able to reproduce motions performed by live octopus for contraction, elongation, bending, and grasping.
  • Dynamic Continuum Arm Model for Use with Underwater Robotic Manipulators Inspired by Octopus Vulgaris Authors: Zheng, Tianjiang; Branson, David; Kang, Rongjie; Cianchetti, Matteo; Guglielmino, Emanuele; Follador, Maurizio; Medrano-Cerda, Gustavo; Godage, Isuru S.; Caldwell, Darwin G.
    Continuum structures with a very high or infinite number of degrees of freedom (DOF) are very interesting structures in nature. Mimicking this kind of structures artificially is challenging due to the high number of required DOF. This paper presents a kinematic and dynamic model for an underwater robotic manipulator inspired by Octopus vulgaris. Then, a prototype arm inspired by live octopus is presented and the model validated experimentally. Initial comparisons of simulated and experimental results show good agreement.
  • Hydrodynamic Analysis of Octopus-Like Robotic Arms Authors: Kazakidi, Asimina; Vavourakis, Vasileios; Pateromichelakis, Nikolaos; Ekaterinaris, John A.; Tsakiris, Dimitris
    We consider robotic analogues of the arms of the octopus, a cephalopod exhibiting a wide variety of dexterous movements and complex shapes, moving in an aquatic environment. Although an invertebrate, the octopus can vary the stiffness of its long arms and generate large forces, while also performing rapid motions within its aquatic environment. Previous studies of elongated robotic systems, moving in fluid environments, have mostly oversimplified the effects of flow and the generated hydrodynamic forces, in their dynamical models. The present paper uses computational fluid dynamic (CFD) analysis to perform high-fidelity numerical simulations of robotic prototypes emulating the morphology of octopus arms. The direction of the flow stream and the arm geometry (e.g., the presence of suckers), were among the parameters that were shown to affect significantly the flow field structure and the resulting hydrodynamic forces, which have a non-uniform distribution along the arm. The CFD results are supported by vortex visualization experiments in a water tank. The results of this investigation are being exploited for the design of soft-bodied robotic systems and the development of related motion control strategies.
  • Design and Performance of Nubbed Fluidizing Jamming Grippers Authors: Kapadia, Jaimeen; Yim, Mark
    Grippers have been shown using jamming of granular media grasp a large range of objects by pushing against them (with an activation force) to conform the gripper to the object’s shape before grasping them with the intent to make universal grippers. This paper presents two effective modifications to jamming gripper designs (adding small nubs and fluidizing the granular media) resulting in significantly larger holding forces (typically 60%) and increasing the range of object geometries. The paper presents the design and fabrication of these devices and explores the range of objects and conditions empirically. Experiments also show that the nubs enable the grasping of smaller objects in which the gripper can engage interlocking forces in the granular media.


  • Decomposable Bundle Adjustment Using a Junction Tree Authors: Pinies, Pedro; Paz, Lina María; Heyden, Anders; Haner, Sebastian
    The Sparse Bundle Adjustment (SBA) algorithm is a widely used method to solve multi-view reconstruction problems in vision. The critical cost of SBA depends on the fill in of the reduced camera matrix whose pattern is known as the Secondary structure of the problem. In centered object applications where a large number of images are taken in a small area the camera matrix obtained when points are eliminated is dense. On the contrary, visual mapping systems where long trajectories are traversed yield sparse matrices. In this paper, we propose a Decomposable Bundle Adjustment (DBA) method which naturally adapts to the fill in pattern of the camera matrix improving the performance on visual mapping systems. The proposed algorithm is able to decompose the normal equations into small subsystems which are ordered in a junction tree structure. To solve the original system, local factorizations of the small dense matrices are passed between clusters in the tree. The DBA algorithm has been tested for simulated and real data experiments for different environment configurations showing good performance.
  • An Incremental Trust-Region Method for Robust Online Sparse Least-Squares Estimation Authors: Rosen, David; Kaess, Michael; Leonard, John
    Many online inference problems in computer vision and robotics are characterized by probability distributions whose factor graph representations are sparse and whose factors are all Gaussian functions of error residuals. Under these conditions, maximum likelihood estimation corresponds to solving a sequence of sparse least-squares minimization problems in which additional summands are added to the objective function over time. In this paper we present Robust Incremental least-Squares Estimation (RISE), an incrementalized version of the Powell's Dog-Leg trust-region method suitable for use in online sparse least-squares minimization. As a trust-region method, Powell's Dog-Leg enjoys excellent global convergence properties, and is known to be considerably faster than both Gauss-Newton and Levenberg-Marquardt when applied to sparse least-squares problems. Consequently, RISE maintains the speed of current state-of-the-art incremental sparse least-squares methods while providing superior robustness to objective function nonlinearities.
  • Weak Constraints Network Optimiser Authors: Berger, Cyrille
    We present a general framework to estimate the parameters of both a robot and landmarks in 3D. It relies on the use of a stochastic gradient descent method for the optimisation of the nodes in a graph of weak constraints where the landmarks and robot poses are the nodes. Then a belief propagation method combined with covariance intersection is used to estimate the uncertainties of the nodes. The first part of the article describes what is needed to define a constraint and a node models, how those models are used to update the parameters and the uncertainties of the nodes. The second part present the models used for robot poses and interest points, as well as simulation results.
  • Multi-Agent Deterministic Graph Mapping Via Robot Rendezvous Authors: Gong, Chaohui; Tully, Stephen; Kantor, George; Choset, Howie
    In this paper, we present a novel algorithm for deterministically mapping an undirected graph-like world with multiple synchronized agents. The application of this algorithm is the collective mapping of an indoor environment with multiple mobile robots while leveraging an embedded topological decomposition of the environment. Our algorithm relies on a group of agents that all depart from the same initial vertex in the graph and spread out to explore the graph. A centralized tree of graph hypotheses is maintained to consider loop-closure, which is deterministically verified when agents observe each other at a common vertex. To achieve efficient mapping, we introduce an active exploration method in which agents dynamically request rendezvous tasks from other available agents to validate graph hypotheses.


  • On the Number of Local Minima to the Point Feature Based SLAM Problem Authors: Huang, Shoudong; Wang, Heng; Frese, Udo; Dissanayake, Gamini
    Map joining is an efficient strategy for solving feature based SLAM problems. This paper demonstrates that joining of two 2D local maps, formulated as a nonlinear least squares problem has at most two local minima, when the associated uncertainties can be described using spherical covariance matrices. Necessary and sufficient condition for the existence of two minima is derived and it is shown that more than one minimum exists only when the quality of the local maps used for map joining is extremely poor. The analysis explains to some extent why a number of optimization based SLAM algorithms proposed in the recent literature that rely on local search strategies are successful in converging to the globally optimal solution from poor initial conditions, particularly when covariance matrices are spherical. It also demonstrates that the map joining problem has special properties that may be exploited to reliably obtain globally optimal solutions to the SLAM problem.
  • On the Comparison of Uncertainty Criteria for Active SLAM Authors: Carrillo, Henry; Reid, Ian; Castellanos, Jose A.
    In this paper, we consider the computation of the D-optimality criterion as a metric for the uncertainty of a SLAM system. Properties regarding the use of this uncertainty criterion in the active SLAM context are highlighted, and comparisons against the A-optimality criterion and entropy are presented. This paper shows that contrary to what has been previously reported, the D-optimality criterion is indeed capable of giving fruitful information as a metric for the uncertainty of a robot performing SLAM. Finally, through various experiments with simulated and real robots, we support our claims and show that the use of D-opt has desirable effects in various SLAM related tasks such as active mapping and exploration.
  • Continuous-Time Batch Estimation Using Temporal Basis Functions Authors: Furgale, Paul Timothy; Barfoot, Timothy; Sibley, Gabe
    Roboticists often formulate estimation problems in discrete time for the practical reason of keeping the state size tractable. However, the discrete-time approach does not scale well for use with high-rate sensors, such as inertial measurement units or sweeping laser imaging sensors. The difficulty lies in the fact that a pose variable is typically included for every time at which a measurement is acquired, rendering the dimension of the state impractically large for large numbers of measurements. This issue is exacerbated for the simultaneous localization and mapping (SLAM) problem, which further augments the state to include landmark variables. To address this tractability issue, we propose to move the full maximum likelihood estimation (MLE) problem into continuous time and use temporal basis functions to keep the state size manageable. We present a full probabilistic derivation of the continuous-time estimation problem, derive an estimator based on the assumption that the densities and processes involved are Gaussian, and show how coefficients of a relatively small number of basis functions can form the state to be estimated, making the solution efficient. Our derivation is presented in steps of increasingly specific assumptions, opening the door to the development of other novel continuous-time estimation algorithms using different assumptions. Results from a self-calibration experiment involving a camera and a high-rate IMU are provided to validate the approach.
  • SLAM with Single Cluster PHD Filters Authors: Lee, Chee Sing; Clark, Daniel; Salvi, Joaquim
    Recent work by Mullane, Vo, and Adams has re-examined the probabilistic foundations of feature-based Simultaneous Localization and Mapping (SLAM), casting the problem in terms of filtering with random finite sets. Algorithms were developed based on Probability Hypothesis Density (PHD) filtering techniques that provided superior performance to leading feature-based SLAM algorithms in challenging mea- surement scenarios with high false alarm rates, high missed detection rates, and high levels of measurement noise. We investigate this approach further by considering a hierarchical point process, or single-cluster multi-object, model, where we consider the state to consist of a map of landmarks conditioned on a vehicle state. Using Finite Set Statistics, we are able to find tractable formulae to approximate the joint vehicle-landmark state based on a single Poisson multi-object assumption on the predicted density. We describe the single-cluster PHD filter and the practical implementation developed based on a particle-system representation of the vehicle state and a Gaussian mixture approximation of the map for each particle. Synthetic simulation results are presented to compare the novel algorithm against the previous PHD filter SLAM algorithm. Results presented indicate a superior performance in vehicle and map landmark localization, and comparable performance in landmark cardinality estimation.
  • Simultaneous Localization and Scene Reconstruction with Monocular Camera Authors: Huang, Kuo- Chen; Tseng, Shih-Huan; Mou, Wei-Hao; Fu, Li-Chen
    In this paper, we propose an online scene recon- struction algorithm with monocular camera since there are many advantages on modeling and visualization of an environ- ment with physical scene reconstruction instead of resorting to sparse 3D points. The goal of this algorithm is to simultaneously track the camera position and map the 3D environment, which is close to the spirit of visual SLAM. There’re plenty of visual SLAM algorithms in the current literature which can provide a high accuracy performance, but many of them rely on stereo cameras. It’s true that we’ll face many more challenges to accomplish this task with monocular camera. However, the advantages of cheaper and easier deployable hardware setting have made monocular approach more attractive. Specifically, we apply a maximum a posteriori Bayesian approach with optimization technique to simultaneously track the camera and build a dense point cloud. We also propose a feature expansion method to expand the density of points, and then online reconstruct the scene with a delayed approach. Furthermore, we utilize the reconstructed model to accomplish visual localization task without extracting the features. Finally, a number of experiments have been conducted to validate our proposed approach, and promising performance can be observed.
  • Rhythm-based Adaptive Localization in Incomplete RFID Landmark Environments Authors: Kodaka, Kenri; Ogata, Tetsuya; Sugano, Shigeki
    This paper proposes a novel hybrid-structured model for the adaptive localization of robots combining a stochastic localization model and a rhythmic action model, for avoiding vacant spaces of landmarks efficiently. In regularly arranged landmark environments, robots may not be able to detect any landmarks for a long time during a straight-like movement. Consequently, locally diverse and smooth movement patterns need to be generated to keep the position estimation stable. Conventional approaches aiming at the probabilistic optimization cannot rapidly generate the detailed movement pattern due to a huge computational cost; therefore a simple but diverse movement structure needs to be introduced as an alternative option. We solve this problem by combining a particle filter as the stochastic localization module and the dynamical action model generating a zig-zagging motion. The validation experiments, where virtual-line-tracing tasks are exhibited on a floor-installed RFID environment, show that introducing the proposed rhythm pattern can improve a minimum error boundary and a velocity performance for arbitrary tolerance errors can be improved by the rhythm amplitude adaptation fed back by the localization deviation.