45,495 research outputs found
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
Planar Cooperative Extremum Seeking with Guaranteed Convergence Using A Three-Robot Formation
In this paper, a combined formation acquisition and cooperative extremum
seeking control scheme is proposed for a team of three robots moving on a
plane. The extremum seeking task is to find the maximizer of an unknown
two-dimensional function on the plane. The function represents the signal
strength field due to a source located at maximizer, and is assumed to be
locally concave around maximizer and monotonically decreasing in distance to
the source location. Taylor expansions of the field function at the location of
a particular lead robot and the maximizer are used together with a gradient
estimator based on signal strength measurements of the robots to design and
analyze the proposed control scheme. The proposed scheme is proven to
exponentially and simultaneously (i) acquire the specified geometric formation
and (ii) drive the lead robot to a specified neighborhood disk around
maximizer, whose radius depends on the specified desired formation size as well
as the norm bounds of the Hessian of the field function. The performance of the
proposed control scheme is evaluated using a set of simulation experiments.Comment: Presented at the 2018 IEEE Conference on Decision and Control (CDC),
Miami Beach, FL, US
Distributed Source Seeking without Global Position Information
International audienceWe present a distributed control law to steer a group of autonomous communicating sensors towards the source of a diffusion process. The graph describing the communication links between sensors has a time-invariant topology, and each sensor is able to measure (in addition to the quantity of interest) only the relative bearing angle with respect to its neighbour, but has no absolute position information and does not know any relative distance. Using multiple sensors is useful in wide environments (e.g., under the sea), or when the function describing the diffusion process is slowly changing in space, so that a single sensor may have to travel long distances before having a good gradient estimation. Our approach is based on a twofold control law, which is able to bring and keep the set of sensors on a circular equispaced formation, and to steer the circular formation towards the source via a gradient-ascent technique. The effectiveness of the proposed algorithm is both theoretically proven and supported by simulation results
Distributed Information-based Source Seeking
In this paper, we design an information-based multi-robot source seeking
algorithm where a group of mobile sensors localizes and moves close to a single
source using only local range-based measurements. In the algorithm, the mobile
sensors perform source identification/localization to estimate the source
location; meanwhile, they move to new locations to maximize the Fisher
information about the source contained in the sensor measurements. In doing so,
they improve the source location estimate and move closer to the source. Our
algorithm is superior in convergence speed compared with traditional field
climbing algorithms, is flexible in the measurement model and the choice of
information metric, and is robust to measurement model errors. Moreover, we
provide a fully distributed version of our algorithm, where each sensor decides
its own actions and only shares information with its neighbors through a sparse
communication network. We perform intensive simulation experiments to test our
algorithms on large-scale systems and physical experiments on small ground
vehicles with light sensors, demonstrating success in seeking a light source
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