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Institute for Robotics and Intelligent Machines (IRIM)

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Author: Howard, Ayanna M. × End date: 2009 × Start date: 2000 × search.filters.applied.f.resourcesubtype: Article ×

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Now showing 1 - 10 of 17
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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 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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    An Integrated Approach for Achieving Multi-Robot Task Formations
    (Georgia Institute of Technology, 2009-04) Viguria Jimenez, Luis Antidio ; Howard, Ayanna M.
    In this paper, a problem, called the initial formation problem, within the multirobot task allocation domain is addressed. This problem consists in deciding which robot should go to each of the positions of the formation in order to minimize an objective. Two different distributed algorithms that solve this problem are explained. The second algorithm presents a novel approach that uses cost means to model the cost distribution and improves the performance of the task allocation algorithm. Also, we present an approach that integrates distributed task allocation algorithms with a behavior-based architecture to control formations of robot teams. Finally, simulations and real experiments are used to analyze the formation behavior and provide performance metrics associated with implementation in realistic scenarios.
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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.
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    A Learning Approach to Enable Locomotion of Multiple Robotic Agents Operating in Natural Terrain Environments
    (Georgia Institute of Technology, 2008) Howard, Ayanna M. ; Parker, Lonnie T. ; Smith, Brian Stephen
    This paper presents a methodology that utilizes soft computing approaches to enable locomotion of multiple legged robotic agents operating in natural terrain environments. For individual robotic control, the locomotion strategy consists of a hybrid FSM-GA approach that couples leg orientation states with a genetic algorithm to learn necessary leg movement sequences. To achieve multi-agent formations, locomotion behavior is driven by using a trained neural network to extract relevant distance metrics necessary to realize desired robotic formations while operating in the field. These distance metrics are then fed into local controllers for realizing linear and rotational velocity values for each robotic agent. Details of the methodology are discussed, and experimental results with a team of mobile robots are presented.
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    Integrating Virtual and Human Instructors in Robotic Learning Environments
    (Georgia Institute of Technology, 2008) Howard, Ayanna M. ; Remy, Sekou
    This paper presents two different approaches for utilizing virtual environments to enable learning for both human and robotic students. In the first approach, we showcase a 3D interactive environment that allows a human user to learn how to interact with a virtual robot, before interaction with a physical robot. In the second approach, we present a method that utilizes a simulation environment to provide feedback to a human teacher during a training session in order to concurrently allow adaptation of the learning process for both the teacher and the robotic student. We provide details of the approaches in this paper and provide results of the learning outcomes for the two different scenarios.
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    A Systematic Approach to Predict Performance of Human-Automation Systems
    (Georgia Institute of Technology, 2007-07) Howard, Ayanna M.
    This paper discusses an approach for predicting system performance resulting from humans and robots performing repetitive tasks in a collaborative manner. The methodology uses a systematic approach that incorporates the various effects of workload on human performance, and estimates resulting performance attributes derived between teleoperated and autonomous control of robotic systems. Performance is determined by incorporating capabilities of the human and robotic agent based on accomplishment of functional operations and effect of cognitive stress due to continuous operation by the human agent. This paper provides an overview of the prediction system and discusses its implementation on a simulated rendezvous/docking task.
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    Components, Curriculum, and Community: Robots and Robotics in Undergraduate AI Education
    (Georgia Institute of Technology, 2006) Dodds, Zachary ; Greenwald, Lloyd ; Howard, Ayanna M. ; Tejada, Sheila ; Weinberg, Jerry B.
    This editorial introduction presents an overview of the robotic resources available to AI educators and provides context for the articles in this special issue. We set the stage by addressing the trade-offs among a number of established and emerging hardware and software platforms, curricular topics, and robot contests used to motivate and teach undergraduate AI.
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    Global and Regional Path Planners for Integrated Planning and Navigation
    (Georgia Institute of Technology, 2005-12) Howard, Ayanna M. ; Seraji, Homayoun ; Werger, Barry
    This paper presents a hierarchical strategy for field mobile robots that incorporates path planning at different ranges. At the top layer is a global path planner that utilizes gross terrain characteristics, such as hills and valleys, to determine globally safe paths through the rough terrain. This information is then passed via waypoints to a regional layer that plans appropriate navigation paths using regional terrain characteristics. The global and regional path planners share the same map information, but at different ranges. The motion recommendations from the regional layer are then combined with those of the reactive navigation layer to provide reactive control for the mobile robot. Details of the global and regional path planners are discussed, and simulation and experimental results are presented.
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    Multi-sensor terrain classification for safe spacecraft landing
    (Georgia Institute of Technology, 2004-10) Howard, Ayanna M. ; Seraji, Homayoun
    A novel multi-sensor information fusion methodology for intelligent terrain classification is presented. The focus of this research is to analyze safety characteristics of the terrain using imagery data obtained by on-board sensors during spacecraft descent. This information can be used to enable the spacecraft to land safely on a planetary surface. The focus of our approach is on robust terrain analysis and information fusion in which the terrain is analyzed using multiple sensors and the extracted terrain characteristics are combined to select safe landing sites for touchdown. The novelty of this method is the incorporation of the T-Hazard Map, a multi-valued map representing the risk associated with landing on a planetary surface. The fusion method is explained in detail in this paper and computer simulation results are presented to validate the approach.