6,703 research outputs found
Recursive Bayesian Initialization of Localization Based on Ranging and Dead Reckoning
The initialization of the state estimation in a localization scenario based
on ranging and dead reckoning is studied. Specifically, we start with a
cooperative localization setup and consider the problem of recursively arriving
at a uni-modal state estimate with sufficiently low covariance such that
covariance based filters can be used to estimate an agent's state subsequently.
A number of simplifications/assumptions are made such that the estimation
problem can be seen as that of estimating the initial agent state given a
deterministic surrounding and dead reckoning. This problem is solved by means
of a particle filter and it is described how continual states and covariance
estimates are derived from the solution. Finally, simulations are used to
illustrate the characteristics of the method and experimental data are briefly
presented
Cooperative localization for mobile agents: a recursive decentralized algorithm based on Kalman filter decoupling
We consider cooperative localization technique for mobile agents with
communication and computation capabilities. We start by provide and overview of
different decentralization strategies in the literature, with special focus on
how these algorithms maintain an account of intrinsic correlations between
state estimate of team members. Then, we present a novel decentralized
cooperative localization algorithm that is a decentralized implementation of a
centralized Extended Kalman Filter for cooperative localization. In this
algorithm, instead of propagating cross-covariance terms, each agent propagates
new intermediate local variables that can be used in an update stage to create
the required propagated cross-covariance terms. Whenever there is a relative
measurement in the network, the algorithm declares the agent making this
measurement as the interim master. By acquiring information from the interim
landmark, the agent the relative measurement is taken from, the interim master
can calculate and broadcast a set of intermediate variables which each robot
can then use to update its estimates to match that of a centralized Extended
Kalman Filter for cooperative localization. Once an update is done, no further
communication is needed until the next relative measurement
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Distributed Algorithms for Stochastic Source Seeking With Mobile Robot Networks
Autonomous robot networks are an effective tool for monitoring large-scale environmental fields. This paper proposes distributed control strategies for localizing the source of a noisy signal, which could represent a physical quantity of interest such as magnetic force, heat, radio signal, or chemical concentration. We develop algorithms specific to two scenarios: one in which the sensors have a precise model of the signal formation process and one in which a signal model is not available. In the model-free scenario, a team of sensors is used to follow a stochastic gradient of the signal field. Our approach is distributed, robust to deformations in the group geometry, does not necessitate global localization, and is guaranteed to lead the sensors to a neighborhood of a local maximum of the field. In the model-based scenario, the sensors follow a stochastic gradient of the mutual information (MI) between their expected measurements and the expected source location in a distributed manner. The performance is demonstrated in simulation using a robot sensor network to localize the source of a wireless radio signal
Quality-Aware Broadcasting Strategies for Position Estimation in VANETs
The dissemination of vehicle position data all over the network is a
fundamental task in Vehicular Ad Hoc Network (VANET) operations, as
applications often need to know the position of other vehicles over a large
area. In such cases, inter-vehicular communications should be exploited to
satisfy application requirements, although congestion control mechanisms are
required to minimize the packet collision probability. In this work, we face
the issue of achieving accurate vehicle position estimation and prediction in a
VANET scenario. State of the art solutions to the problem try to broadcast the
positioning information periodically, so that vehicles can ensure that the
information their neighbors have about them is never older than the
inter-transmission period. However, the rate of decay of the information is not
deterministic in complex urban scenarios: the movements and maneuvers of
vehicles can often be erratic and unpredictable, making old positioning
information inaccurate or downright misleading. To address this problem, we
propose to use the Quality of Information (QoI) as the decision factor for
broadcasting. We implement a threshold-based strategy to distribute position
information whenever the positioning error passes a reference value, thereby
shifting the objective of the network to limiting the actual positioning error
and guaranteeing quality across the VANET. The threshold-based strategy can
reduce the network load by avoiding the transmission of redundant messages, as
well as improving the overall positioning accuracy by more than 20% in
realistic urban scenarios.Comment: 8 pages, 7 figures, 2 tables, accepted for presentation at European
Wireless 201
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