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.

Motion Path Planning I

  • Navigation Functions for Everywhere Partially Sufficiently Curved Worlds Authors: Filippidis, Ioannis; Kyriakopoulos, Kostas
    We extend Navigation Functions (NF) to worlds of more general geometry and topology. This is achieved without the need for diffeomorphisms, by direct definition in the geometrically complicated configuration space. Every obstacle boundary point should be partially sufficiently curved. This requires that at least one principal normal curvature be sufficient. A normal curvature is termed sufficient when the tangent sphere with diameter the associated curvature radius is a subset of the obstacle. Examples include ellipses with bounded eccentricity, tori, cylinders, one-sheet hyperboloids and others. Our proof establishes the existence of appropriate tuning for this purpose. Direct application to geometrically complicated cases is illustrated through nontrivial simulations.
  • Trajectory Tracking among Landmarks and Binary Sensor-Beams Authors: Tovar, Benjamin; Murphey, Todd
    We study a trajectory tracking problem for a mobile robot moving in the plane using combinatorial observations from the state. These combinatorial observations come from crossing binary detection beams. A binary detection beam is a sensing abstraction arising from physical sensor beams or virtual beams that are derived from several sensing modalities, such as actual detection beams in the environment, changes in the angular order of landmarks around the robot, or recognizable markings in the plane. We solve the filtering problem from a geometric perspective and present its relation to linear recursive filters in control theory. Subsequently, we develop the acceleration control of the robot to track a given input trajectory, with a finite control set consisting on moving toward landmarks naturally modeling the robot as a switched dynamical system. We present experiments using an e-puck differential-drive robot, in which a useful estimate of the state for tracking is produced regardless of nontrivial uncertainty.
  • A Singularity-Free Path Planner for Closed-Chain Manipulators Authors: Bohigas, Oriol; Henderson, Michael E.; Ros, Lluis; Porta, Josep M
    This paper provides an algorithm for computing singularity-free paths on non-redundant closed-chain manipulators. Given two non-singular configurations of the manipulator, the method attempts to connect them through a configuration space path that maintains a minimum clearance with respect to the singularity locus at all points. The method is resolution-complete, in the sense that it always returns a path if one exists at a given resolution, or returns "failure'' otherwise. The path is computed by defining a new manifold that maintains a one-to-one correspondence with the singularity-free configuration space of the manipulator, and then using a higher-dimensional continuation technique to explore this manifold systematically from one configuration, until the second configuration is found. Examples are included that demonstrate the performance of the method on illustrative situations.
  • Comparison of Constrained Geometric Approximation Strategies for Planar Information States Authors: Song, Yang; O'Kane, Jason
    This paper describes and analyzes a new technique for reasoning about uncertainty called constrained geometric approximation (CGA). We build upon recent work that has developed methods to explicitly represent a robot's knowledge as an element, called an information state, in an appropriately defined information space. The intuition of our new approach is to constrain the I-state to remain in a structured subset of the I-space, and to enforce that constraint using appropriate overapproximation methods. The result is a collection of algorithms that enable mobile robots with extreme limitations in both sensing and computation to maintain simple but provably meaningful representations of the incomplete information available to them. We present a simulated implementation of this technique for a sensor-based navigation task, along with experimental results for this task showing that CGA, compared to a high-fidelity representation of the un-approximated I-state, achieves a similar success rate at a small fraction of the computational cost.
  • Voxel-Based Motion Bounding and Workspace Estimation for Robotic Manipulators Authors: Anderson-Sprecher, Peter; Simmons, Reid
    Identification of regions in space that a robotic manipulator can reach in a given amount of time is important for many applications, such as safety monitoring of industrial manipulators and trajectory and task planning. However, due to the high-dimensional configuration space of many robots, reasoning about possible physical motion is often intractable. In this paper, we propose a novel method for creating a <i>reachability grid</i>, a voxel-based representation that estimates the minimum time needed for a manipulator to reach any physical location within its workspace. We use up to second-degree constraints on joint motion to model motion limits for each joint independently, followed by successive voxel approximations to map these limits on to the robot’s physical workspace. Results using a simulated manipulator indicate that our method can produce accurate reachability grids in real-time, even for robots with many degrees of freedom. Furthermore, errors are almost exclusively biased towards producing more optimistic reachability estimates, which is a desirable characteristic for many applications.
  • Branch and Bound for Informative Path Planning Authors: Binney, Jonathan; Sukhatme, Gaurav
    We present an optimal algorithm for informative path planning (IPP), using a branch and bound method inspired by feature selection algorithms. The algorithm uses the monotonicity of the objective function to give an objective function-dependent speedup versus brute force search. We present results which suggest that when maximizing variance reduction in a Gaussian process model, the speedup is significant.