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Howard,
Ayanna M.
Howard,
Ayanna M.
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1 - 10 of 85
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ItemUpper 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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ItemAssistive 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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ItemTowards Visual Arctic Terrain Assessment(Georgia Institute of Technology, 2009-07) Williams, Stephen ; Howard, Ayanna M.Many important scientific studies, particularly those involving climate change, require weather measurements from the ice sheets in Greenland and Antarctica. Due to the harsh and dangerous conditions of such environments, it would be advantageous to deploy a group of autonomous, mobile weather sensors, rather than accepting the expense and risk of human presence. For such a sensor network to be viable, a method of navigating, and thus a method of terrain assessment, must be developed that is tailored for arctic hazards. An extension to a previous arctic terrain assessment method is presented, which is able to produce dense terrain slope estimates from a single camera. To validate this methodology, a set of prototype arctic rovers have been designed, constructed, and fielded on a glacier in Alaska.
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ItemProbabilistic 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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ItemAutomatic Generation of Persistent Formations for Multi-Agent Networks Under Range Constraints(Georgia Institute of Technology, 2009-06) Smith, Brian Stephen ; Egerstedt, Magnus B. ; Howard, Ayanna M.In this paper we present a collection of graph-based 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 inter-agent 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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ItemImproving the performance of ANN training with an unsupervised filtering method(Georgia Institute of Technology, 2009-06) Remy, Sekou ; Park, Chung Hyuk ; Howard, Ayanna M.Learning control strategies from examples has been identified as an important capability for many robotic systems. In this work we show how the learning process can be aided by autonomously filtering the training set provided to improve key properties of the learning process. Demonstrated with data gathered for manipulation tasks, the results herein show the improved performance when autonomous filtering is applied. The filtration method, with no prior knowledge of the task was able to partition the training sets into sets almost equal to expertly labeled sets. In the case where the filter did not produce the same groupings as the expert user, the method still permitted a controller to be trained which demonstrated a success rate of 92%.
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ItemAutomatic 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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ItemMobility Reconfiguration for Terrain Exploration using Human Inspired Perception(Georgia Institute of Technology, 2009-05) Brooks, Douglas Antwonne ; Howard, Ayanna M.The ability of robotic units to autonomously navigate various terrains is critical to the advancement of robotic operation in natural environments. Next generation robots will need to adapt to their environment in order to accomplish tasks that are either too hazardous, too time consuming, or physically impossible for human-beings. Such tasks may include accurate and rapid explorations of various planets or potentially dangerous areas on Earth. Furthermore, because terrain variability typically increases as the distance that a rover traverses increases, it will be beneficial for robotic units to adapt to their surroundings. As a result, this research investigates a navigation control methodology for a multi-modal locomotive robot based upon passive perception. Surface estimation for robot reconfigurability is implemented using a region growing method, which characterizes the traversability of the terrain, in conjunction with passive perception regarding motion. A mathematical approach is then implemented that inherits human psychological aspects to direct necessary navigation behavior to control robot mobility. Physical experimentations in a simulated Mars yard are presented to validate the methodology.
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ItemA Probabilistic Model for the Performance Analysis of a Distributed Task Allocation Algorithm(Georgia Institute of Technology, 2009-05) Viguria Jimenez, Luis Antidio ; Howard, Ayanna M.In this paper we extend our previous work where the mean of the global cost was used as a performance metric for distributed task allocation algorithms. In this case, we move a step forward and calculate the variance of the global cost. This second parameter gives us a better understanding of the distributed algorithm performance, i.e., we can estimate how much the algorithm behavior diverts from its mean. The normal distribution, computed from the theoretical mean and variance, is shown to be suitable for modeling the global cost. This approximation enables us to compare our algorithm theoretically in different cases.
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ItemPlaying with Toys: Towards Autonomous Robot Manipulation for Therapeutic Play.(Georgia Institute of Technology, 2009-05) Trevor, Alexander J. B. ; Park, Hae Won ; Howard, Ayanna M. ; Kemp, Charles C.When young children play, they often manipulate toys that have been specifically designed to accommodate and stimulate their perceptual-motor skills. Robotic playmates capable of physically manipulating toys have the potential to engage children in therapeutic play and augment the beneficial interactions provided by overtaxed care givers and costly therapists. To date, assistive robots for children have almost exclusively focused on social interactions and teleoperative control. Within this paper we present progress towards the creation of robots that can engage children in manipulative play. First, we present results from a survey of popular toys for children under the age of 2 which indicates that these toys share simplified appearance properties and are designed to support a relatively small set of coarse manipulation behaviors. We then present a robotic control system that autonomously manipulates several toys by taking advantage of this consistent structure. Finally, we show results from an integrated robotic system that imitates visually observed toy playing activities and is suggestive of opportunities for robots that play with toys.