Organizational Unit:
Institute for Robotics and Intelligent Machines (IRIM)

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Publication Search Results

Now showing 1 - 10 of 25
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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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    Probabilistic Analysis of Market-Based Algorithms for Initial Robotic Formations
    (Georgia Institute of Technology, 2009-06) Viguria Jimenez, Luis Antidio ; Howard, Ayanna M.
    In this paper, we present a probabilistic analysis approach for analyzing market-based algorithms applied to the initial formation problem. These algorithms determine an assignment scheme for associating individual robots with goal positions necessary to achieve a desired formation while minimizing an objective function. The main contribution of this paper is a method that calculates the expected value of the objective function, which allows us to estimate and compare theoretically the performance of two task allocation algorithms. This probabilistic analysis is applied in different runtime scenarios. We validate our approach through both simulations and experiments with real robots.
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    Automatic Formation Deployment of Decentralized Heterogeneous Multiple-Robot Networks with Limited Sensing Capabilities
    (Georgia Institute of Technology, 2009-05) Smith, Brian Stephen ; Wang, Jiuguang ; Howard, Ayanna M. ; Egerstedt, Magnus B.
    Heterogeneous multi-robot networks require novel tools for applications that require achieving and maintaining formations. This is the case for distributing sensing devices with heterogeneous mobile sensor networks. Here, we consider a heterogeneous multi-robot network of mobile robots. The robots have a limited range in which they can estimate the relative position of other network members. The network is also heterogeneous in that only a subset of robots have localization ability. We develop a method for automatically configuring the heterogeneous network to deploy a desired formation at a desired location. This method guarantees that network members without localization are deployed to the correct location in the environment for the sensor placement
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    Improvements To Satellite-Based Albedo Measurements Using In Situ Robotic Surveying Techniques
    (Georgia Institute of Technology, 2009-04) Parker, Lonnie T. ; English, Brittney A. ; Chavis, Marcus A. ; Howard, Ayanna M.
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    3-D Simulations for Testing and Validating Robotic-Driven Applications for Exploring Lunar Pole
    (Georgia Institute of Technology, 2009-04) Williams, Stephen ; Remy, Sekou ; Howard, Ayanna M.
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    Development of a Mobile Arctic Sensor Node for Earth-Science Data Collection Applications
    (Georgia Institute of Technology, 2009-04) Williams, Stephen ; Hurst, Michael ; Howard, Ayanna M.
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    Automatic Generation of Persistent Formations for Multi-Agent Networks Under Range Constraints
    (Georgia Institute of Technology, 2009-04) Smith, Brian Stephen ; Howard, Ayanna M. ; Egerstedt, Magnus B.
    In this paper we present a collection of graphbased methods for determining if a team of mobile robots, subjected to sensor and communication range constraints, can persistently achieve a specified formation. What we mean by this is that the formation, once achieved, will be preserved by the direct maintenance of the smallest subset of all possible pairwise interagent distances. In this context, formations are defined by sets of points separated by distances corresponding to desired inter-agent distances. Further, we provide graph operations to describe agent interactions that implement a given formation, as well as an algorithm that, given a persistent formation, automatically generates a sequence of such operations. Experimental results are presented that illustrate the operation of the proposed methods on real robot platforms.
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    Predicting the Robot Learning Curve based on Properties of Human Interaction
    (Georgia Institute of Technology, 2009-03) Remy, Sekou ; Howard, Ayanna M.
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    Multi-robot deployment and coordination with Embedded Graph Grammars
    (Georgia Institute of Technology, 2009-01) Smith, Brian Stephen ; Howard, Ayanna M. ; McNew, John-Michael ; Wang, Jiuguang ; Egerstedt, Magnus B.
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    Learning Approaches Applied to Human-Robot Interaction for Space Missions
    (Georgia Institute of Technology, 2008) Remy, Sekou ; Howard, Ayanna M.
    Advances in space science and technology have enabled humanity to reach a stage where we are able to send manned and unmanned vehicles to explore nearby planets. However, given key differences between terrestrial and space environments such as differences in atmospheric content and pressure, acceleration due to gravity among many others between our planet and those we wish to explore, it is not always easy or feasible to expect all mission related tasks to be accomplished by astronauts alone. The presence of robots that specialize in different tasks would greatly enhance our capabilities and enable better overall performance. In this paper we discuss a methodology for building a robotic system that can learn to perform tasks via interactive learning. This learning functionality extends the ability for a robot agent to operate with similar competence as their human teacher- whether astronaut, mission designer, or engineer. We provide details on our approach and give representative examples of applying the different methods in relevant task scenarios.