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Institute for Robotics and Intelligent Machines (IRIM)

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End date: 2009 × search.filters.applied.f.resourcesubtype: Proceedings ×

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    Pulling Open Novel Doors and Drawers with Equilibrium Point Control
    (Georgia Institute of Technology, 2009-12) Jain, Advait ; Kemp, Charles C.
    A large variety of doors and drawers can be found within human environments. Humans regularly operate these mechanisms without difficulty, even if they have not previously interacted with a particular door or drawer. In this paper, we empirically demonstrate that equilibrium point control can enable a humanoid robot to pull open a variety of doors and drawers without detailed prior models, and infer their kinematics in the process. Our implementation uses a 7 DoF anthropomorphic arm with series elastic actuators (SEAs) at each joint, a hook as an end effector, and low mechanical impedance. For our control scheme, each SEA applies a gravity compensating torque plus a torque from a simulated, torsional, viscoelastic spring. Each virtual spring has constant stiffness and damping, and a variable equilibrium angle. These equilibrium angles form a joint space equilibrium point (JEP), which has a corresponding Cartesian space equilibrium point (CEP) for the arm's end effector. We present two controllers that generate a CEP at each time step (ca. 100 ms) and use inverse kinematics to command the arm with the corresponding JEP. One controller produces a linear CEP trajectory and the other alters its CEP trajectory based on real-time estimates of the mechanism's kinematics. We also present results from empirical evaluations of their performance (108 trials). In these trials, both controllers were robust with respect to variations in the mechanism, the pose of the base, the stiffness of the arm, and the way the handle was hooked. We also tested the more successful controller with 12 distinct mechanisms. In these tests, it was able to open 11 of the 12 mechanisms in a single trial, and successfully categorized the 11 mechanisms as having a rotary or prismatic joint, and opening to the right or left. Additionally, in the 7 out of 8 trials with rotary joints, the robot accurately estimated the location of the axis of rotation.
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    Upper Limb Rehabilitation and Evaluation of Children Using a Humanoid Robot
    (Georgia Institute of Technology, 2009-11) Brooks, Douglas Antwonne ; Howard, Ayanna M.
    This paper discusses a preliminary approach to matching child movements with robotic movements for the purpose of evaluating child upper limb rehabilitation exercises. Utilizing existing algorithms termed Motion History Imaging and Dynamic Time Warping for determining areas of movement and video frame mapping respectively, we are able to determine whether or not a patient is consistently performing accurate rehabilitation exercises. The overall goal of this research is to fuse play and rehabilitation techniques using a robotic design to induce child-robot interaction that will be entertaining as well as effective for the child.
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    Individual Muscle Control using an Exoskeleton Robot for Muscle Function Testing
    (Georgia Institute of Technology, 2009-10) Ueda, Jun ; Hyderabadwala, Moiz ; Ding, Ming ; Ogasawara, Tsukasa ; Krishnamoorthy, Vijaya ; Shinohara, Minoru
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    Effective robot task learning by focusing on task-relevant objects
    (Georgia Institute of Technology, 2009-10) Lee, Kyu Hwa ; Lee, Jinhan ; Thomaz, Andrea L. ; Bobick, Aaron F.
    In a robot learning from demonstration framework involving environments with many objects, one of the key problems is to decide which objects are relevant to a given task. In this paper, we analyze this problem and propose a biologically-inspired computational model that enables the robot to focus on the task-relevant objects. To filter out incompatible task models, we compute a task relevance value (TRV) for each object, which shows a human demonstrator's implicit indication of the relevance to the task. By combining an intentional action representation with `motionese', our model exhibits recognition capabilities compatible with the way that humans demonstrate. We evaluate the system on demonstrations from five different human subjects, showing its ability to correctly focus on the appropriate objects in these demonstrations.
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    RF vision: RFID receive signal strength indicator (RSSI) images for sensor fusion and mobile manipulation
    (Georgia Institute of Technology, 2009-10) Deyle, Travis ; Nguyen, Hai ; Reynolds, Matt S. ; Kemp, Charles C.
    In this work we present a set of integrated methods that enable an RFID-enabled mobile manipulator to approach and grasp an object to which a self-adhesive passive (battery-free) UHF RFID tag has been affixed. Our primary contribution is a new mode of perception that produces images of the spatial distribution of received signal strength indication (RSSI) for each of the tagged objects in an environment. The intensity of each pixel in the 'RSSI image' is the measured RF signal strength for a particular tag in the corresponding direction. We construct these RSSI images by panning and tilting an RFID reader antenna while measuring the RSSI value at each bearing. Additionally, we present a framework for estimating a tagged object's 3D location using fused ID-specific features derived from an RSSI image, a camera image, and a laser range finder scan. We evaluate these methods using a robot with actuated, long-range RFID antennas and finger-mounted short-range antennas. The robot first scans its environment to discover which tagged objects are within range, creates a user interface, orients toward the user-selected object using RF signal strength, estimates the 3D location of the object using an RSSI image with sensor fusion, approaches and grasps the object, and uses its finger-mounted antennas to confirm that the desired object has been grasped. In our tests, the sensor fusion system with an RSSI image correctly located the requested object in 17 out of 18 trials (94.4%), an 11.1% improvement over the system's performance when not using an RSSI image. The robot correctly oriented to the requested object in 8 out of 9 trials (88.9%), and in 3 out of 3 trials the entire system successfully grasped the object selected by the user.
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    Assistive Formation Maintenance for Human-Led Multi-Robot Systems
    (Georgia Institute of Technology, 2009-10) Parker, Lonnie T. ; Howard, Ayanna M.
    In ground-based military maneuvers, group formations require flexibility when traversing from one point to the next. For a human-led team of semi-autonomous agents, a certain level of awareness demonstrated by the agents regarding the quality of the formation is preferable. Through the use of a Multi-Robot System (MRS), this work combines leader-follower principles augmented by an assistive formation maintenance (AFM) method to improve formation keeping and demonstrate a formation-in-motion concept. This is achieved using the Robot Mean Task Allocation method (RTMA), a strategy used to allocate formation positions to each unit within a continuously mobile MRS. The end goal is to provide a military application that allows a soldier to efficiently tele-operate a semi-autonomous MRS capable of holding formation amidst a cluttered environment. Baseline simulation is performed in Player/Stage to show the applicability of our developed model and its potential for expansive research.
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    PPS-Tags: Physical, Perceptual and Semantic Tags for Autonomous Mobile Manipulation
    (Georgia Institute of Technology, 2009-10) Nguyen, Hai ; Deyle, Travis ; Reynolds, Matt S. ; Kemp, Charles C.
    For many promising application areas, autonomous mobile manipulators do not yet exhibit sufficiently robust performance. We propose the use of tags applied to task-relevant locations in human environments in order to help autonomous mobile manipulators physically interact with the location, perceive the location, and understand the location’s semantics. We call these tags physical, perceptual and semantic tags (PPS-tags). We present three examples of PPS-tags, each of which combines compliant and colorful material with a UHF RFID tag. The RFID tag provides a unique identifier that indexes into a semantic database that holds information such as the following: what actions can be performed at the location, how can these actions be performed, and what state changes should be observed upon task success? We also present performance results for our robot operating on a PPS-tagged light switch, rocker light switch, lamp, drawer, and trash can. We tested the robot performing the available actions from 4 distinct locations with each of these 5 tagged devices. For the light switch, rocker light switch, lamp, and trash can, the robot succeeded in all trials (24/24). The robot failed to open the drawer when starting from an oblique angle, and thus succeeded in 6 out of 8 trials. We also tested the ability of the robot to detect failure in unusual circumstances, such as the lamp being unplugged and the drawer being stuck.
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    Modeling of biodynamic feedthrough in backhoe operation
    (Georgia Institute of Technology, 2009-10) Humphreys, Heather C. ; Book, Wayne J. ; Huggins, James D.
    An advanced backhoe user interface has been developed which uses coordinated control with haptic feedback. Results indicate that the coordinated control provides more intuitive operation that is easy to learn, and the haptic feedback also relays meaningful information back to the user in the form of force signals from digging forces and system limitations. However, results show that the current system has significant problems with biodynamic feedthrough, where the motion of the controlled device excites motion of the operator, resulting in undesirable forces applied to the input device and control performance degradation. This unwanted input is difficult to decouple from the intentional operator input in experiments. This research presents an investigation on the effects of biodynamic feedthrough on this particular backhoe control system, using system identification to empirically define models to represent each component. These models are used for a preliminary simulation study on potential methods for biodynamic feedthrough compensation.
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    Modeling & Characterizing Stochastic Actuator Arrays
    (Georgia Institute of Technology, 2009-10) MacNair, David L. ; Ueda, Jun
    If the modal response for a single degree of freedom flexible system is known, a command generation architecture can be determined which schedules on/off actuator effort such that the resulting motion will have zero vibration. If the system possesses redundant on/off actuation, the number of possible zero vibration commands increases. Of particular interest is the command that has the minimum number of actuator changes in state. This paper presents how to determine this command and applies it in simulation to a flexible actuator inspired by human muscle.
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    GroupSAC: Efficient Consensus in the Presence of Groupings
    (Georgia Institute of Technology, 2009-09) Ni, Kai ; Jin, Hailin ; Dellaert, Frank
    We present a novel variant of the RANSAC algorithm that is much more efficient, in particular when dealing with problems with low inlier ratios. Our algorithm assumes that there exists some grouping in the data, based on which we introduce a new binomial mixture model rather than the simple binomial model as used in RANSAC. We prove that in the new model it is more efficient to sample data from a smaller numbers of groups and groups with more tentative correspondences, which leads to a new sampling procedure that uses progressive numbers of groups. We demonstrate our algorithm on two classical geometric vision problems: wide-baseline matching and camera resectioning. The experiments show that the algorithm serves as a general framework that works well with three possible grouping strategies investigated in this paper, including a novel optical flow based clustering approach. The results show that our algorithm is able to achieve a significant performance gain compared to the standard RANSAC and PROSAC.