TechTalks from event: Technical session talks from ICRA 2012

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Micro and Nano Robots I

  • Polymer-Based Wireless Resonant Magnetic Microrobots Authors: Tung, Hsi-Wen; Frutiger, Dominic R.; Pane, Salvador; Nelson, Bradley J.
    We present a class of Wireless Resonant Magnetic Microactuator (WRMMA) that integrates a polymer spring/body structure with electroplated ferromagnetic masses. The new devices, which we call PolyMites as they are derived from our previous MagMites, are simpler, faster and cheaper to fabricate than the MagMite. Like their predecessor, they are capable of moving on planar surfaces in dry and wet environments. Their improved biocompatibility also extends their potential for biological applications. PolyMites are 500 μm in diameter and 55 μm in height. In air they have attained a speed of 13 mm/s, approximately 26 body lengths per second. PolyMites are capable of micromanipulation on a surface, which is demonstrated by pushing and releasing micro-objects such as polystyrene beads in water.
  • Three-Dimensional Control of Engineered Motile Cellular Microrobots Authors: Kim, Dal Hyung; Kim, Paul; Julius, Agung; Kim, MinJun
    We demonstrate three-dimensional control with the eukaryotic cell Tetrahymena pyriformis (T. pyriformis) using two sets of Helmholtz coils for xy-plane motion and a single electromagnet for vertical motion. T. pyriformis is modified to have artificial magnetotaxis with internalized magnetite. Since the magnetic fields exerted by electromagnets are relatively uniform in the working space, the magnetite exerts only torque, without translational force, which enabled us to guide the cell’s swimming direction while the swimming force is exerted only by the cell’s motile organelles. A stronger magnetic force was necessary to steer cells to the z¬-axis, and, as a result, a single electromagnet placed just below our sample area is utilized for vertical motion. To track the cell’s positions in the z-axis, intensity profiles of non-motile cells at varying distances from the focal plane are used. During vertical motion along the z-axis, the intensity difference from the background decreases while the cell size increases. Since the cell is pear-shaped, the eccentricity is high during planar motion, but lowers during vertical motion due to the change in orientation. The three-dimensional control of the live organism T. pyriformis as a cellular robot shows great potential to be utilized for practical applications in microscale tasks, such as target transport and cell therapy.
  • Towards MR-Navigable Nanorobotic Carriers for Drug Delivery into the Brain Authors: Tabatabaei, Seyed Nasrollah; Sonia, Duchemin; Giouard, Hélène; Martel, Sylvain
    Magnetic Resonance Navigation (MRN) relies on Magnetic Nanoparticles (MNPs) embedded in microcarriers or microrobots to allow the induction of a directional propelling force by 3D magnetic gradients. These magnetic gradients are superposed on a sufficiently high homogeneous magnetic field to achieve maximum propelling force through magnetization saturation of the MNP. As previously demonstrated by our group, such technique was successful at maintaining microcarriers along a planned trajectory in the blood vessels based on tracking information gathered using Magnetic Resonance Imaging (MRI) sequences from artifacts caused by the same MNPs. Besides propulsion and tracking, the same MNPs can be synthesized with characteristics that can allow for the diffusion of therapeutic cargo carried by these MR-navigable carriers through the Blood Brain Barrier (BBB) using localized hyperthermia without compromising the MRN capabilities. In the present study, an external heating apparatus was used to impose a regional heat shock on the skull of a living mouse model. The effect of heat on the permeability of the BBB was assessed using histological observation and tissue staining by Evans blue dye. Results show direct correlation between hyperthermia and BBB leakage as well as its recovery from thermal damage. Therefore, the proposed navigable agents could be suitable for controlled opening of the BBB by hyperthermia and selective brain drug delivery.
  • Diamagnetically Levitated Robots: An Approach to Massively Parallel Robotic Systems with Unusual Motion Properties Authors: Pelrine, Ron; Wong-Foy, Annjoe; McCoy, Brian; Holeman, Dennis; Mahoney, Rich; Myers, Greg; Herson, Jim; Low, Thomas
    Using large numbers of microrobots to build unique macrostructures has long been a vision in both popular and scientific media. This paper describes a new class of machines, DiaMagnetic Micro Manipulator (DM3) systems, for controlling many small robots. The robots are diamagnetically levitated with zero wear and zero hysteresis, and driven using conventional circuits. Unusual motion properties have been reported in testing these systems, including exceptional open loop repeatability of motion (200 nm rms) and relative speeds (37.5 cm/s or 217 body lengths/s) [1]. A system using 130 micro robots as small as 1.7 mm with densities up to 12.5 robots/cm2 has been demonstrated. This paper reports initial data on robot trajectories, and shows that open loop trajectory repeatabilities on the order of 0.8 micrometers rms or better are feasible in a levitated state compared with 15 micrometers rms repeatability in a non-levitated state with surface contact. These results suggest an encouraging path to complex microrobotic systems with broad capabilities.
  • Magnetic Micro Actuator with Neutral Buoyancy and 3D Fabrication of Cell Size Magnetized Structure Authors: Yasui, Masato; Ikeuchi, Masashi; Ikuta, Koji
    We have developed two technologies for 3D magnetic microstructures, with a wide size range between 5&#956;m to 2mm. The first technology enables us to obtain density controlled 3D magnetic microstructures. The size is approximately 500&#956;m. In this scale, controlling density is vital for magnetic micro actuators, because the effect of gravity is strong. To adjust density, we developed the world’s first “density controllable magnetically photocurable (DMPC) polymer.” The DMPC polymer is a mixture of hollow microcapsules (density, 0.03 g/cm<sup>3</sup>), magnetic particles, and photocurable polymer. We can obtain desired relative density between 0.5 to 1.7 by adjusting the concentration of microcapsules. In addition, we succeeded in 3D velocity control of a screw-type magnetic micro actuator with neutral buoyancy in water. The delay time was 32msec. In addition, the actuator possessed 6 DOF. The second technology realized a 5&#956;m magnetic micro actuator, which is a combination of a 3D transparent structure and 2D magnetic structure. Various photocurable polymers can be applied as the 2D structure in this process, although we used magnetically photocurable polymer in this report. Furthermore, we have succeeded in driving a ferromagnetic micro actuator, whose diameter is as small as 1&#956;m. These two fabrication processes will become key technologies in both medical and life sciences field, because they can supply a wide variety of 3D micro structures with small effort.

Multi-Robot Systems II

  • Distributed Value Functions for Multi-Robot Exploration Authors: Matignon, Laetitia; Jeanpierre, Laurent; Mouaddib, Abdel-Illah
    This paper addresses the problem of exploring an unknown area with a team of autonomous robots using decentralized decision making techniques. The localization aspect is not considered and it is assumed the robots share their positions and have access to a map updated with all explored areas. A key problem is then the coordination of decentralized decision processes: each individual robot must choose appropriate exploration goals so that the team simultaneously explores different locations of the environment. We formalize this problem as a Decentralized Markov Decision Process (Dec-MDP) solved as a set of individual MDPs, where interactions between MDPs are considered in a distributed value function. Thus each robot computes locally a strategy that minimizes the interactions between the robots and maximizes the space coverage of the team. Our technique has been implemented and evaluated in real-world and simulated experiments.
  • Steiner Traveler: Relay Deployment for Remote Sensing in Heterogeneous Multi-Robot Exploration Authors: Pei, Yuanteng; Mutka, Matt
    In the multi-robot exploration task of an unknown environment, human operators often need to control the robots remotely and obtain the sensed information by real-time bandwidth-consuming multimedia streams. The task has military and civilian applications, such as reconnaissance, search and rescue missions in earthquake, radioactive, and other dangerous or hostile regions. Due to the nature of such applications, infrastructure networks or pre-deployed relays are often not available to support the stream transmission. To address this issue, we present a novel exploration scheme called Bandwidth aware Exploration with a Steiner Traveler (BEST). BEST has a heterogeneous robot team with a fixed number of frontier nodes(FNs) to sense the area iteratively. In addition, a relay-deployment node (RDN) tracks the FNs movement and places relays when necessary to support the video/audio streams aggregation to the base station. Therefore, the main problem is to find a minimum path for the relay-deployment robot to travel and the positions to deploy necessary relays to support the stream aggregation in each movement iteration. This problem inherits characteristics of both the Steiner minimum tree and traveling salesman problems. We model the novel problem as the minimum velocity Flow constrained Steiner Traveler problem (FST). Extensive simulations show BEST improves exploration efficiency by 62% on average compared to the state-of-the-art homogeneous robot exploration strategies.
  • Minimal Persistence Control on Dynamic Directed Graphs for Multi-Robot Formation Authors: Wang, Hua; Guo, Yi
    Given a multi-robot system, in order to preserve its geometric shape in a formation, the minimal persistence control addresses questions: (1) what pairwise communication connections have to be prescribed to minimize communication channels, and (2) which orientations of communication links are to be placed between robots. In this paper, we propose a minimal persistence control problem on multi-robot systems with underlying graphs, being directed and dynamically switching. We develop distributed algorithms based on the rank of the rigidity matrix and the pebble game method. The feasibility of the proposed methods is validated by simulations on the robotic simulator Webots and experiments on e-puck robot platform.
  • Distributed Formation Control of Unicycle Robots Authors: Sadowska, Anna; Kostic, Dragan; van de Wouw, Nathan; Huijberts, Henri; Nijmeijer, Hendrik
    In this paper, we consider the problem of distributed formation control for a group of unicycle robots. We propose a control algorithm that solves the formation control problem in that it ensures that robots create a desired time– varying formation shape while the formation as a whole follows a prescribed trajectory. Moreover, we show that it is also possible to obtain coordination of robots in the formation, regardless of the trajectory tracking of the formation. We illustrate the behavior of a group of robots controlled by the formation control algorithm proposed in this paper in a simulation study.
  • Multi-Level Formation Roadmaps for Collision-Free Dynamic Shape Changes with Non-holonomic Teams Authors: Krontiris, Athanasios; Louis, Sushil; Bekris, Kostas E.
    Teams of robots can utilize formations to accomplish a task, such as maximizing the observability of an environment while maintaining connectivity. In a cluttered space, however, it might be necessary to automatically change formation to avoid obstacles. This work proposes a path planning approach for non-holonomic robots, where a team dynamically switches formations to reach a goal without collisions. The method introduces a multi-level graph, which can be constructed offline. Each level corresponds to a different formation and edges between levels allow for formation transitions. All edges satisfy curvature bounds and clearance requirements from obstacles. During the online phase, the method returns a path for a virtual leader, as well as the points along the path where the team should switch formations. Individual agents can compute their controls using kinematic formation controllers that operate in curvilinear coordinates. The approach guarantees that it is feasible for the agents to follow the trajectory returned. Simulations show that the online cost of the approach is small. The method returns solutions that maximize the maintenance of a desired formation while allowing the team to rearrange its configuration in the presence of obstacles.
  • An Unscented Model Predictive Control Approach to the Formation Control of Nonholonomic Mobile Robots Authors: Farrokhsiar, Morteza; Najjaran, Homayoun
    Formation control of nonholonomic robots in dynamic unstructured environments is a challenging task yet to be met. This paper presents the unscented model predictive control (UMPC) approach to tackle the formation control of multiple nonholonomic robots in unstructured environments. In unscented predictive control, the uncertainty propagation in the nonholonomic nonlinear motion model is approximated using the unscented transform. The collision avoidance constraints have been introduced as the chance constraints to model predictive control. The UMPC approach enables us to find a closed form of the collision avoidance probabilistic constraints. The desired pose of each robot in the formation is introduced through the local objective function of UMPC of each robot. The simulation results indicate the effective and robust performance of UMPC in unstructured environment in the presence of action disturbance and communication signal noise.

Grasping: Learning and Estimation

  • End-To-End Dexterous Manipulation with Deliberate Interactive Estimation Authors: Hudson, Nicolas; Howard, Tom; Ma, Jeremy; Jain, Abhinandan; Bajracharya, Max; Myint, Steven; Matthies, Larry; Backes, Paul; Hebert, Paul; Fuchs, Thomas; Burdick, Joel
    This paper presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation (ARM) program. The developed autonomy system uses robot, object, and environment models to identify and localize objects, and well as plan and execute required manipulation tasks. Deliberate interaction with objects and the environment increases system knowledge about the combined robot and environmental state, enabling high precision tasks such as key insertion to be performed in a consistent framework. This approach has been demonstrated across a wide range of manipulation tasks, and is the leading manipulation approach in independent DARPA testing.
  • Template-Based Learning of Grasp Selection Authors: Herzog, Alexander; Pastor, Peter; Kalakrishnan, Mrinal; Righetti, Ludovic; Asfour, Tamim; Schaal, Stefan
    The ability to grasp unknown objects is an important skill for personal robots, which has been addressed by many present and past research projects. A crucial aspect of grasping is choosing an appropriate grasp configuration, i.e. the 6d pose of the hand relative to the object and its finger configuration. Finding feasible grasp configurations for novel objects, however, is challenging because of the huge variety in shape and size of these objects and the specific kinematics of the robotic hand in use. In this paper, we introduce a new grasp selection algorithm able to find object grasp poses based on previously demonstrated grasps. Assuming that objects with similar shapes can be grasped in a similar way, we associate to each demonstrated grasp a grasp template. The template is a local shape descriptor for a possible grasp pose and is constructed using 3d information from depth sensors. For each new object to grasp, the algorithm then finds the best grasp candidate in the library of templates. The grasp selection is also able to improve over time using the information of previous grasp attempts to adapt the ranking of the templates. We tested the algorithm on two different platforms, the Willow Garage PR2 and the Barrett WAM arm which have very different hands. Our results show that the algorithm is able to find good grasp configurations for a large set of objects from a relatively small set of demonstrations, and does indeed improve its performance over time.
  • Learning Hardware Agnostic Grasps for a Universal Jamming Gripper Authors: Jiang, Yun; Amend, John; Lipson, Hod; Saxena, Ashutosh
    Grasping has been studied from various perspectives including planning, control, and learning. In this paper, we take a learning approach to predict successful grasps for a universal jamming gripper. A jamming gripper is comprised of a flexible membrane filled with granular material, and it can quickly harden or soften to grip objects of varying shape by modulating the air pressure within the membrane. Although this gripper is easy to control, it is difficult to develop a physical model of its gripping mechanism because it undergoes significant deformation during use. Thus, many grasping approaches based on physical models (such as based on form- and force-closure) would be challenging to apply to a jamming gripper. Here we instead use a supervised learning algorithm and design both visual and shape features for capturing the property of good grasps. We show that given a RGB image and a point cloud of the target object, our algorithm can predict successful grasps for the jamming gripper without requiring a physical model. It can therefore be applied to both a parallel plate gripper and a jamming gripper without modification. We demonstrate that our learning algorithm enables both grippers to pick up a wide variety of objects, and through robotic experiments we are able to define the type of objects each gripper is best suited for handling.
  • Learning Grasp Stability Authors: Dang, Hao; Allen, Peter
    We deal with the problem of blind grasping where we use tactile feedback to predict the stability of a robotic grasp given no visual or geometric information about the object being grasped. We first simulated tactile feedback using a soft finger contact model in GraspIt! and computed tactile contacts of thousands of grasps with a robotic hand using the Columbia Grasp Database. We used the K-means clustering method to learn a contact dictionary from the tactile contacts, which is a codebook that models the contact space. The feature vector for a grasp is a histogram computed based on the distribution of its contacts over the contact space defined by the dictionary. An SVM is then trained to predict the stability of a robotic grasp given this feature vector. Experiments indicate that this model which requires low-dimension feature input is useful in predicting the stability of a grasp.
  • Learning to Slide a Magnetic Card through a Card Reader Authors: Sukhoy, Vladimir; Georgiev, Veselin; Wegter, Todd; Sweidan, Ramy; Stoytchev, Alexander
    This paper describes a set of experiments in which an upper-torso humanoid robot learned to slide a card through a card reader. The small size and the flexibility of the card presented a number of manipulation challenges for the robot. First, because most of the card is occluded by the card reader and the robot's hand during the sliding process, visual feedback is useless for this task. Second, because the card bends easily, it is difficult to distinguish between bending and hitting an obstacle in order to correct the sliding trajectory. To solve these manipulation challenges this paper proposes a method for constraint detection that uses only proprioceptive data. The method uses dynamic joint torque thresholds that are calibrated using the robot's movements in free space. The experimental results show that using this method the robot can detect when the movement of the card is constrained and modify the sliding trajectory in real time, which makes solving this task possible.