&lt;title&gt;Robotic perimeter detection system&lt;/title&gt;

As part of a project for the Defense Advanced Research Projects Agency, Sandia National Laboratones' Intelligent Systems & Robotics Center is developing and testing the feasibility of using a cooperative team of robotic sentry vehicles to guard a perimeter, perform a surround task, and travel extended distances. This paper describes our most recent activities. In particular, this paper highlights the development of a Differential Global Positioning System (DGPS) Ieapfiog capability that allows two or more vehicles to alternate sending DGPS corrections. Using this leapfrog technique, this paper shows that a group of autonomous vehicles can travel 22.68 kilometers with a root mean square positioning error of only 5 meters.

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“…As one vehicle attends an alarm, the other vehicles adjust their position around the perimeter to better prepare for another alarm [14].…”

Section: Disclaimer )mentioning

confidence: 99%

Cooperative Sentry Vehicles And Differential GPS Leapfrog

Feddema

Lewis

LaFarge

2000

Distributed Autonomous Robotic Systems 4

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“…In one approach by Feddema et al [1], a three-step process is presented that creates locally optimal distributed controls for multiple robot vehicles. The approach was tested in simulation and hardware that included distribution of multiple robots [1][2][3][4][5][6], along: lines/curves, unconstrained/constrained 2-D planes, elliptical curves, and convergence on the source of a plume in a plane and a 3-D volume. The approach is based on the optimization of a global performance index that includes only nearest-neighbor information.…”

Section: Introductionmentioning

confidence: 99%

Collective Plume Tracing: A Minimal Information Approach to Collective Control

Robinett

Wilson

2007

2007 American Control Conference

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A team of simple robots are used to trace a chemical plume to its source in order to find buried landmines. The goal of this paper is to analyze and design "emergent" behaviors to enable the team of simple robots to perform plume tracing with the assistance of information theory. The first step in the design process is to define a fundamental tradeoff for collective systems between processing, memory, and communications for each robot in order to execute the desired collective behaviors. The baseline problem is to determine the minimum values for processing, memory, and communications simultaneously. This will enable the designer to determine the required information flow and how effectively the information resources are being utilized in collective systems. The solution to this baseline problem is an 8-bit processor, no memory, and three words to communicate. The details of this solution are described in this paper. The second step is to extend the previous kinematic solution to a more general Hamiltonian based solution to develop a Fisher Information Equivalency. The Fisher Information Equivalency leads directly to necessary and sufficient conditions for stability and control of nonlinear collective systems via physical and information exergy flows. In particular, the creation of a "virtual/information potential" by the team of robots via the decentralized distributed networked sensors produces a direct relationship between proportional feedback control, stored exergy in the system, and Fisher Information. This nonlinear stability formulation through Fisher Information directly provides an optimization problem to "tune" the performance of the team of robots as a function of required information resources.

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“…The goal of robotic perimeter surveillance is to use a cooperative team of robotic sentry vehicles to investigate alarms from miniature intrusion detection sensors (MIDS) [39]. In our tests, we used four different types of MIDS including magnetometer, seismic, passive infrared, and beam-break (or active) infrared.…”

Section: Perimeter Surveillancementioning

confidence: 99%

“…To simplify the problem, assume that the and position can be controlled independently, and assume that vehicle dynamics of the two closest vehicles are (37) If and , then the control can be written as (38) where . The resulting stability test matrix is (39) which is an M matrix if . When , the two vehicles stabilize at and where and are the initial positions of the two vehicles.…”

Section: Surround Taskmentioning

confidence: 99%

Decentralized control of cooperative robotic vehicles: theory and application

Feddema

Lewis

Schoenwald

2002

IEEE Trans. Robot. Automat.

228

118

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Abstract-This paper describes how decentralized control theory can be used to analyze the control of multiple cooperative robotic vehicles. Models of cooperation are discussed and related to the input/output reachability, structural observability, and controllability of the entire system. Whereas decentralized control research in the past has concentrated on using decentralized controllers to partition complex physically interconnected systems, this work uses decentralized methods to connect otherwise independent nontouching robotic vehicles so that they behave in a stable, coordinated fashion. A vector Liapunov method is used to prove stability of two examples: the controlled motion of multiple vehicles along a line and the controlled motion of multiple vehicles in formation. Also presented are three applications of this theory: controlling a formation, guarding a perimeter, and surrounding a facility.

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<title>Robotic perimeter detection system</title>

Cited by 11 publications

References 0 publications

Cooperative Sentry Vehicles And Differential GPS Leapfrog

Cooperative Sentry Vehicles And Differential GPS Leapfrog

Collective Plume Tracing: A Minimal Information Approach to Collective Control

Decentralized control of cooperative robotic vehicles: theory and application

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