1,643 research outputs found
A decentralized motion coordination strategy for dynamic target tracking
This paper presents a decentralized motion planning
algorithm for the distributed sensing of a noisy dynamical
process by multiple cooperating mobile sensor agents. This
problem is motivated by localization and tracking tasks of
dynamic targets. Our gradient-descent method is based on a
cost function that measures the overall quality of sensing. We
also investigate the role of imperfect communication between
sensor agents in this framework, and examine the trade-offs in
performance between sensing and communication. Simulations
illustrate the basic characteristics of the algorithms
Bounded Distributed Flocking Control of Nonholonomic Mobile Robots
There have been numerous studies on the problem of flocking control for
multiagent systems whose simplified models are presented in terms of point-mass
elements. Meanwhile, full dynamic models pose some challenging problems in
addressing the flocking control problem of mobile robots due to their
nonholonomic dynamic properties. Taking practical constraints into
consideration, we propose a novel approach to distributed flocking control of
nonholonomic mobile robots by bounded feedback. The flocking control objectives
consist of velocity consensus, collision avoidance, and cohesion maintenance
among mobile robots. A flocking control protocol which is based on the
information of neighbor mobile robots is constructed. The theoretical analysis
is conducted with the help of a Lyapunov-like function and graph theory.
Simulation results are shown to demonstrate the efficacy of the proposed
distributed flocking control scheme
GRASP News Volume 9, Number 1
A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory
Data fusion to improve trajectory tracking in a Cooperative Surveillance Multi-Agent Architecture
13 pages, 12 figures.In this paper we present a Cooperative Surveillance Multi-Agent System (CS-MAS) architecture extended to incorporate dynamic coalition formation. We illustrate specific coalition formation using fusion skills. In this case, the fusion process is divided into two layers: (i) a global layer in the fusion center, which initializes the coalitions and (ii) a local layer within coalitions, where a local fusion agent is dynamically instantiated. There are several types of autonomous agent: surveillanceâsensor agents, a fusion center agent, a local fusion agent, interface agents, record agents, planning agents, etc. Autonomous agents differ in their ability to carry out a specific surveillance task. A surveillanceâsensor agent controls and manages individual sensors (usually video cameras). It has different capabilities depending on its functional complexity and limitations related to sensor-specific aspects. In the work presented here we add a new autonomous agent, called the local fusion agent, to the CS-MAS architecture, addressing specific problems of on-line sensor alignment, registration, bias removal and data fusion. The local fusion agent is dynamically created by the fusion center agent and involves several surveillanceâsensor agents working in a coalition. We show how the inclusion of this new dynamic local fusion agent guarantees that, in a video-surveillance system, objects of interest are successfully tracked across the whole area, assuring continuity and seamless transitions.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505 /TIC/0255 and DPS2008-07029-C02-02.Publicad
\u3cem\u3eGRASP News\u3c/em\u3e: Volume 9, Number 1
The past year at the GRASP Lab has been an exciting and productive period. As always, innovation and technical advancement arising from past research has lead to unexpected questions and fertile areas for new research. New robots, new mobile platforms, new sensors and cameras, and new personnel have all contributed to the breathtaking pace of the change. Perhaps the most significant change is the trend towards multi-disciplinary projects, most notable the multi-agent project (see inside for details on this, and all the other new and on-going projects). This issue of GRASP News covers the developments for the year 1992 and the first quarter of 1993
Realization of reactive control for multi purpose mobile agents
Mobile robots are built for different purposes, have different physical size, shape, mechanics and electronics. They are required to work in real-time, realize more than one goal simultaneously, hence to communicate and cooperate with other agents. The approach proposed in this paper for mobile robot control is reactive and has layered structure that supports multi sensor perception. Potential field method is implemented for both obstacle avoidance and goal tracking. However imaginary forces of the obstacles and of the goal point are separately treated, and then resulting behaviors are fused with the help of the geometry. Proposed control is tested on simulations where
different scenarios are studied. Results have confirmed the high performance of the method
Weighted SPSA-based Consensus Algorithm for Distributed Cooperative Target Tracking
In this paper, a new algorithm for distributed multi-target tracking in a sensor network is proposed. The main feature of that algorithm, combining the SPSA techniques and iterative averaging ("consensus algorithm"), is the ability to solve distributed optimization problems in presence of signals with fully uncertain distribution; the only assumption is the signalâs boundedness. As an example, we consider the multi-target tracking problem, in which the unknown signals include measurement errors and unpredictable targetâs maneuvers; statistical properties of these signals are unknown. A special choice of weights in the algorithm enables its application to targets exhibiting different behaviors. An explicit estimate of the residualâs covariance matrix is obtained, which may be considered as a performance index of the algorithm. Theoretical results are illustrated by numerical simulations
A multi-agent architecture to support active fusion in a visual sensor network
8 pages, 12 figures.-- Contributed to: Second ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC'2008, Stanford, California, US, Sep 7-11, 2008).One of the main characteristics of a visual sensor network environment is the high amount of data generated. In addition, the application of some process, as for example tracking objects, generate a highly noisy output which may potentially produce an inconsistent system output. By inconsistent output we mean highly differences between tracking information provided by the visual sensors. A visual sensor network, with overlapped field of views, could exploit the redundancy between the field of view of each visual sensor to avoid inconsistencies and obtain more accurate results. In this paper, we present a visual sensor network system with overlapped field of views, modeled as a network of software agents. The communication of each software agent allows the use of feedback information in the visual sensors, called active fusion. Results of the software architecture to support active fusion scheme in an indoor scenario evaluation are presented.This work was supported in part by Projects MADRINET, TEC2005-07186-C03-02, SINPROB, TSI2005-07344-C02-02.Publicad
Consensus-based Distributed Algorithm for Multisensor-Multitarget Tracking under UnknownâbutâBounded Disturbances
We consider a dynamic network of sensors that cooperate to estimate parameters of multiple targets. Each sensor can observe parameters of a few targets, reconstructing the trajectories of the remaining targets via interactions with âneighbouringâ sensors. The multi-target tracking has to be provided in the face of uncertainties, which include unknown-but-bounded drift of parameters, noise in observations and distortions introduced by communication channels. To provide tracking in presence of these uncertainties, we employ a distributed algorithm, being an âoffspringâ of a consensus protocol and the stochastic gradient descent. The mathematical results on the algorithmâs convergence are illustrated by numerical simulations
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