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Mobile Robot Laboratory

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Now showing 1 - 10 of 187
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    Transferring Embodied Concepts Between Perceptually Heterogeneous Robots
    (Georgia Institute of Technology, 2009) Kira, Zsolt ; Georgia Institute of Technology. College of Computing ; Georgia Institute of Technology. Mobile Robot Laboratory
    This paper explores methods and representations that allow two perceptually heterogeneous robots, each of which represents concepts via grounded properties, to transfer knowledge despite their differences. This is an important issue, as it will be increasingly important for robots to communicate and effectively share knowledge to speed up learning as they become more ubiquitous.We use Gӓrdenfors’ conceptual spaces to represent objects as a fuzzy combination of properties such as color and texture, where properties themselves are represented as Gaussian Mixture Models in a metric space. We then use confusion matrices that are built using instances from each robot, obtained in a shared context, in order to learn mappings between the properties of each robot. These mappings are then used to transfer a concept from one robot to another, where the receiving robot was not previously trained on instances of the objects. We show in a 3D simulation environment that these models can be successfully learned and concepts can be transferred between a ground robot and an aerial quadrotor robot.
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    Noise Maps for Acoustically Sensitive Navigation
    (Georgia Institute of Technology, 2004) Martinson, Eric ; Arkin, Ronald C. ; Georgia Institute of Technology. College of Computing
    More and more robotic applications are equipping robots with microphones to improve the sensory information available to them. However, in most applications the auditory task is very low-level, only processing data and providing auditory event information to higher-level navigation routines. If the robot, and therefore the microphone, ends up in a bad acoustic location, then the results from that sensor will remain noisy and potentially useless for accomplishing the required task. To solve this problem, there are at least two possible solutions. The first is to provide bigger and more complex filters, which is the traditional signal processing approach. An alternative solution is to move the robot in concert with providing better audition. In this work, the second approach is followed by introducing noise maps as a tool for acoustically sensitive navigation. A noise map is a guide to noise in the environment, pinpointing locations which would most likely interfere with auditory sensing. A traditional noise map, in an acoustic sense, is a graphical display of the average sound pressure level at any given location. An area with high sound pressure level corresponds to high ambient noise that could interfere with an auditory application. Such maps can be either created by hand, or by allowing the robot to first explore the environment. Converted into a potential field, a noise map then becomes a useful tool for reducing the interference from ambient noise. Preliminary results with a real robot on the creation and use of noise maps are presented.
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    A Reactive Robot Architecture With Planning on Demand
    (Georgia Institute of Technology, 2003) Koenig, Sven ; Ranganathan, Ananth ; Georgia Institute of Technology. College of Computing
    In this paper, we describe a reactive robot architecture that uses fast re-planning methods to avoid the shortcomings of reactive navigation, such as getting stuck in box canyons or in front of small openings. Our robot architecture differs from others in that it gives planning progressively greater control of the robot if reactive navigation continues to fail, until planning controls the robot directly. Our first experiments on a Nomad robot and in simulation demonstrate that our robot architecture promises to simplify the programming of reactive robot architectures greatly and results in robust navigation, smooth trajectories, and reasonably good navigation performance.
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    Auditory Evidence Grids
    (Georgia Institute of Technology, 2006) Martinson, Eric ; Schultz, Alan ; Georgia Institute of Technology ; Naval Research Laboratory (U.S.)
    Sound source localization on a mobile robot can be a difficult task due to a variety of problems inherent to a real environment, including robot ego-noise, echoes, and the transient nature of ambient noise. As a result, source localization data are often very noisy and unreliable. In this work, we overcome some of these problems by combining the localization evidence over a variety of robot poses using an evidence grid. The result is a representation that localizes the pertinent objects well over time, can be used to filter poor localization results, and may also be useful for global re-localization from sound localization results.
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    Distributed Sensor Fusion for Object Position Estimation by Multi-Robot Systems
    (Georgia Institute of Technology, 2001) Balch, Tucker ; Martin, Martin C. ; Stroupe, Ashley W. ; Carnegie-Mellon University. Robotics Institute
    We present a method for representing, communicating and fusing distributed, noisy and uncertain observations of an object by multiple robots. The approach relies on re-parameterization of the canonical two-dimensional Gaussian distribution that corresponds more naturally to the observation space of a robot. The approach enables two or more observers to achieve greater effective sensor coverage of the environment and improved accuracy in object position estimation. We demonstrate empirically that, when using our approach, more observers achieve more accurate estimations of an object’s position. The method is tested in three application areas, including object location, object tracking, and ball position estimation for robotic soccer. Quantitative evaluations of the technique in use on mobile robots are provided.
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    Using the CONDENSATION Algorithm for Robust, Vision-Based Mobile Robot Localization
    (Georgia Institute of Technology, 1999) Burgard, Wolfram ; Dellaert, Frank ; Fox, Dieter ; Thrun, Sebastian ; Carnegie-Mellon University. Computer Science Dept. ; Universität Bonn. Institut für Informatik III
    To navigate reliably in indoor environments, a mobile robot must know where it is. This includes both the ability of globally localizing the robot from scratch, as well as tracking the robot’s position once its location is known. Vision has long been advertised as providing a solution to these problems, but we still lack efficient solutions in unmodified environments. Many existing approaches require modification of the environment to function properly, and those that work within unmodified environments seldomly address the problem of global localization. In this paper we present a novel, vision-based localization method based on the CONDENSATION algorithm [17, 18], a Bayesian filtering method that uses a sampling-based density representation. We show how the CONDENSATION algorithm can be used in a novel way to track the position of the camera platform rather than tracking an object in the scene. In addition, it can also be used to globally localize the camera platform, given a visual map of the environment. Based on these two observations, we present a vision-based robot localization method that provides a solution to a difficult and open problem in the mobile robotics community. As evidence for the viability of our approach, we show both global localization and tracking results in the context of a state of the art robotics application.
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    Integrated Control for Mobile Manipulation for Intelligent Materials Handling
    (Georgia Institute of Technology, 1992) Arkin, Ronald C. ; Arya, S. ; Book, Wayne J. ; Cameron, Jonathan M. ; Gardner, Warren F. ; Lawton, Daryl T. ; MacKenzie, Douglas Christopher ; Ramanathan, V. ; Son, C. ; Vachtsevanos, George J. ; Ward, Keith Ronald ; Georgia Institute of Technology
    An integrated control system architecture for mobile manipulators is presented. This architecture incorporates a hybrid reactive/hierarchical structure and partitions the task into macro- and micro-manipulation components. Computer vision and other sensor modalities provide the input necessary to cope with materials handling tasks in a partially modeled and dynamic world.
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    Performance Verification for Behavior-Based Robot Missions
    (Georgia Institute of Technology, 2015-06) Lyons, Damian M. ; Arkin, Ronald C. ; Jiang, Shu ; Liu, Tsung-Ming ; Nirmal, Paramesh ; Georgia Institute of Technology. College of Computing ; Georgia Institute of Technology. School of Interactive Computing ; Georgia Institute of Technology. Mobile Robot Laboratory ; Georgia Institute of Technology. Institute for Robotics and Intelligent Machines
    Certain robot missions need to perform predictably in a physical environment that may have significant uncertainty. One approach is to leverage automatic software verification techniques to establish a performance guarantee. The addition of an environment model and uncertainty in both program and environment, however, means the state-space of a model-checking solution to the problem can be prohibitively large. An approach based on behavior-based controllers in a process-algebra framework that avoids state-space combinatorics is presented here. In this approach, verification of the robot program in the uncertain environment is reduced to a filtering problem for a Bayesian Network. Validation results are presented for the verification of a multiple-waypoint and an autonomous exploration robot mission.
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    Intrinsic Localization and Mapping With 2 Applications: Diffusion Mapping and Marco Polo Localization
    (Georgia Institute of Technology, 2003) Alegre, Fernando ; Dellaert, Frank ; Martinson, Eric Beowulf ; Georgia Institute of Technology. College of Computing
    We investigate Intrinsic Localization and Mapping (ILM) for teams of mobile robots, a multi-robot variant of SLAM where the robots themselves are used as landmarks. We develop what is essentially a straightforward application of Bayesian estimation to the problem, and present two complimentary views on the associated optimization problem that provide insight into the problem and allows one to devise initialization strategies, indispensable in practice. We also provide a discussion of the degrees of freedom and ambiguities in the solution. Finally, we introduce two applications of ILM that bring out its potential: Diffusion Mapping and Marco Polo localization.
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    Io, Ganymede and Callisto - a Multiagent Robot Trash-Collecting Team
    (Georgia Institute of Technology, 1995) Balch, Tucker ; Boone, Gary Noel ; Collins, Tom ; Forbes, Harold ; MacKenzie, Douglas Christopher ; Santamaria, Juan Carlos ; Georgia Institute of Technology. College of Computing ; Georgia Institute of Technology. Mobile Robot Laboratory
    Georgia Tech won the Office Cleanup Event at the 1994 AAAI Mobile Robot Competition with a multi-robot cooperating team. This paper describes the design and implementation of these reactive trash-collecting robots, including details of multiagent cooperation, color vision for the detection of perceptual object classes, temporal sequencing of behaviors for task completion, and a language for specifying motor schema-based robot behaviors.