Organizational Unit:
Institute for Robotics and Intelligent Machines (IRIM)
Institute for Robotics and Intelligent Machines (IRIM)
Permanent Link
Research Organization Registry ID
Description
Previous Names
Parent Organization
Parent Organization
Includes Organization(s)
Organizational Unit
Organizational Unit
ArchiveSpace Name Record
Publication Search Results
Now showing
1 - 5 of 5
-
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
-
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.
-
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.
-
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.
-
ItemAutomatic Generation of Persistent Formations for Multi-agent Networks under Range Constraints(Georgia Institute of Technology, 2007-10) Smith, Brian Stephen ; Howard, Ayanna M. ; Egerstedt, Magnus B.We present graph-based methods for determining if a mobile robot network with a defined sensor and communication range can persistently achieve a specified formation, which implies that the formation, once achieved, will be preserved by the direct maintenance of a subset of inter-agent distances. Here, formations are defined by a set of points whose inter-point distances correspond 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.