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Howard,
Ayanna M.
Howard,
Ayanna M.
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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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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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ItemAutomatic Formation Deployment of Decentralized Heterogeneous Multiple-Robot Networks with Limited Sensing Capabilities(Georgia Institute of Technology, 2009-05) Smith, Brian Stephen ; Wang, Jiuguang ; Egerstedt, Magnus B. ; Howard, Ayanna M.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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ItemAutomatic 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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ItemMulti-Robot Deployment and Coordination with Embedded Graph Grammars(Georgia Institute of Technology, 2009-01) Smith, Brian Stephen ; Howard, Ayanna M. ; McNew, John-Michael ; Egerstedt, Magnus B.This paper presents a framework for going from specifications to implementations of decentralized control strategies for multi-robot systems. In particular, we show how the use of Embedded Graph Grammars (EGGs) provides a tool for characterizing local interaction and control laws. This paper highlights some key implementation aspects of the EGG formalism, and develops and discusses experimental results for a hexapod-based multi-robot system, as well as a multi-robot system of wheeled robots.
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ItemMulti-robot deployment and coordination with Embedded Graph Grammars(Georgia Institute of Technology, 2009-01) Smith, Brian Stephen ; Howard, Ayanna M. ; McNew, John-Michael ; Wang, Jiuguang ; Egerstedt, Magnus B.
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ItemAutomatic Deployment and Formation Control of Decentralized Multi-Agent Networks(Georgia Institute of Technology, 2008-05) Smith, Brian Stephen ; Egerstedt, Magnus B. ; Howard, Ayanna M.Novel tools are needed to deploy multi-agent networks in applications that require a high degree of accuracy in the achievement and maintenance of geometric formations. This is the case when deploying distributed sensing devices across large spatial domains. Through so-called Embedded Graph Grammars (EGGs), this paper develops a method for automatically generating control programs that ensure that a multi-robot network is deployed according to the desired configuration. This paper presents a communication protocol needed for implementing and executing the control programs in an accurate and deadlock-free manner.
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ItemAutomatic Deployment and Formation Control of Decentralized Multi-Agent Networks(Georgia Institute of Technology, 2008-05) Smith, Brian Stephen ; Howard, Ayanna M. ; Egerstedt, Magnus B.Novel tools are needed to deploy multi-agent networks in applications that require a high degree of accuracy in the achievement and maintenance of geometric formations. This is the case when deploying distributed sensing devices across large spatial domains. Through so-called embedded graph grammars (EGGs), this paper develops a method for automatically generating control programs that ensure that a multi-robot network is deployed according to the desired configuration. This paper presents a communication protocol needed for implementing and executing the control programs in an accurate and deadlock-free manner.
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ItemA Single Camera Terrain Slope Estimation Technique for Natural Arctic Environments(Georgia Institute of Technology, 2008-05) Smith, Brian 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 reliable, fault tolerant, low-cost method of navigating must be developed. One aspect of an autonomous navigation system must be an assessment of the local terrain, including the slope of nearby regions. Presented here is a method of estimating the slope of the terrain in the robot's coordinate frame using only a single camera, which has been applied to both simulated arctic terrain and real images. The slope estimates are then converted into the global coordinate frame using information from a roll sensor, used as an input to a fuzzy logic navigation scheme, and tested in a simulated arctic environment.
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ItemA 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 StephenThis 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.