Person:
Howard, Ayanna M.

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https://hdl.handle.net/1853/72195
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    Visualize Your Robot with Your Eyes Closed: A Multi-modal Interactive Approach Using Environmental Feedback
    (Georgia Institute of Technology, 2011-05) Park, Chung Hyuk ; Howard, Ayanna M.
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    An Integrated Sensing Approach for Entry, Descent, and Landing of a Robotic Spacecraft
    (Georgia Institute of Technology, 2011-01) Howard, Ayanna M. ; Jones, Brandon M. ; Serrano, Navid
    We present an integrated sensing approach for enabling autonomous landing of a robotic spacecraft on a hazardous terrain surface; this approach is active during the spacecraft descent profile. The methodology incorporates an image transformation algorithm to interpret temporal imagery land data, perform real-time detection and avoidance of terrain hazards that may impede safe landing, and increase the accuracy of landing at a desired site of interest using landmark localization techniques. By integrating a linguistic rule-based engine with linear algebra and computer vision techniques, the approach suitably addresses inherent uncertainty in the hazard assessment process while ensuring computational simplicity for real-time implementation during spacecraft descent. The proposed approach is able to identify new hazards as they emerge and also remember the locations of past hazards that might impede spacecraft landing. We provide details of the methodology in this paper and present simulation results of the approach applied to a representative Mars landing descent profile.
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    Developing Monocular Visual Odometry and Pose Estimation for Arctic Environments
    (Georgia Institute of Technology, 2010-03) Williams, Stephen ; Howard, Ayanna M.
    Arctic regions present one of the harshest environments on Earth for people or mobile robots, yet many important scientific studies, particularly those involving climate change, require measurements from these areas. For the successful deployment of mobile sensors in the Arctic, a high-quality localization system is required. Although a global positioning system can provide coarse positioning (within several meters), it cannot provide any orientation information. A single-camera-pose-estimation system is presented, based on visual odometry techniques, which is capable of operating in the feature-poor environments of the Arctic. To validate the system, a prototype rover was developed and fielded on a glacier in Alaska. The resulting pose estimates compare favorably to values obtained by hand registering the same video sequence. Although pose errors do accumulate over time, these errors are typical of a standard odometry system but obtained in an environment where standard odometry is not practical.
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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.