Organizational Unit:
Mobile Robot Laboratory

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Author: Balch, Tucker × End date: 2003 × Start date: 2000 × search.filters.applied.f.resourcesubtype: Paper ×

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Now showing 1 - 5 of 5
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    Efficient Particle Filter-Based Tracking of Multiple Interacting Targets Using an MRF-based Motion Model
    (Georgia Institute of Technology, 2003) Balch, Tucker ; Dellaert, Frank ; Khan, Zia
    We describe a multiple hypothesis particle filter for tracking targets that will be influenced by the proximity and/or behavior of other targets. Our contribution is to show how a Markov random field motion prior, built on the fly at each time step, can model these interactions to enable more accurate tracking. We present results for a social insect tracking application, where we model the domain knowledge that two targets cannot occupy the same space, and targets will actively avoid collisions. We show that using this model improves track quality and efficiency. Unfortunately, the joint particle tracker we propose suffers from exponential complexity in the number of tracked targets. An approximation to the joint filter, however, consisting of multiple nearly independent particle filters can provide similar track quality at substantially lower computational cost.
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    Value-Based Communication Preservation for Mobile Robots
    (Georgia Institute of Technology, 2003) Balch, Tucker ; Powers, Matthew
    Value-Based Communication Preservation (VBCP) is a behavior-based, computationally efficient approach to maintaining line-of-sight RF communication between members of robot teams in the context of other tasks. The goal of VBCP is, at each time step, to reactively choose a direction in which to move that provides the best communication quality of service with the rest of the team. VBCP uses information about other robots, real-time quality of service measurements and an a priori map of the environment to approximate an optimal direction in an efficient manner. Here, VBCP maintains communication between members of a robotic team while traversing an urban environment in formation. Quantitative and qualitative results are demonstrated in simulation and physical robot teams.
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    Niche Selection for Foraging Tasks in Multi-Robot Teams Using Reinforcement Learning
    (Georgia Institute of Technology, 2003) Balch, Tucker ; Ulam, Patrick D.
    We present a means in which individual members of a multi-robot team may allocate themselves into specialist and generalist niches in a multi-foraging task where there may exist a cost for generalist strategies. Through the use of reinforcement learning, we show that the members can allocate themselves into effective distributions consistent with those distributions predicted by optimal foraging theory. These distributions are established without prior knowledge of the environment, without direct communication between team members, and with minimal state.
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    The Georgia Tech Yellow Jackets: A Marsupial Team for Urban Search and Rescue
    (Georgia Institute of Technology, 2002) Alegre, Fernando ; Balch, Tucker ; Berhault, Marc ; Dellaert, Frank ; Kaess, Michael ; McGuire, Robert ; Merrill, Ernest ; Moshkina, Lilia ; Ravichandran, Ram ; Walker, Daniel
    We describe our entry in the AAAI 2002 Urban Search and Rescue (USAR) competition, a marsupial team consisting of a larger wheeled robot and several small legged robots, carried around by the larger robot. This setup exploits complimentary strengths of each robot type in a challenging domain. We describe both the hardware and software architecture, and the on-board real-time mapping which forms the basis of accurate victim-localization crucial to the USAR domain. We also evaluate what challenges remain to be resolved in order to deploy search and rescue robots in realistic scenarios.
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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.
    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.