12,448 research outputs found
Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels
In this paper we propose and analyze a distributed algorithm for achieving
globally optimal decisions, either estimation or detection, through a
self-synchronization mechanism among linearly coupled integrators initialized
with local measurements. We model the interaction among the nodes as a directed
graph with weights (possibly) dependent on the radio channels and we pose
special attention to the effect of the propagation delay occurring in the
exchange of data among sensors, as a function of the network geometry. We
derive necessary and sufficient conditions for the proposed system to reach a
consensus on globally optimal decision statistics. One of the major results
proved in this work is that a consensus is reached with exponential convergence
speed for any bounded delay condition if and only if the directed graph is
quasi-strongly connected. We provide a closed form expression for the global
consensus, showing that the effect of delays is, in general, the introduction
of a bias in the final decision. Finally, we exploit our closed form expression
to devise a double-step consensus mechanism able to provide an unbiased
estimate with minimum extra complexity, without the need to know or estimate
the channel parameters.Comment: To be published on IEEE Transactions on Signal Processin
On the convergence rate of distributed gradient methods for finite-sum optimization under communication delays
Motivated by applications in machine learning and statistics, we study
distributed optimization problems over a network of processors, where the goal
is to optimize a global objective composed of a sum of local functions. In
these problems, due to the large scale of the data sets, the data and
computation must be distributed over processors resulting in the need for
distributed algorithms. In this paper, we consider a popular distributed
gradient-based consensus algorithm, which only requires local computation and
communication. An important problem in this area is to analyze the convergence
rate of such algorithms in the presence of communication delays that are
inevitable in distributed systems. We prove the convergence of the
gradient-based consensus algorithm in the presence of uniform, but possibly
arbitrarily large, communication delays between the processors. Moreover, we
obtain an upper bound on the rate of convergence of the algorithm as a function
of the network size, topology, and the inter-processor communication delays
Cooperative Adaptive Control for Cloud-Based Robotics
This paper studies collaboration through the cloud in the context of
cooperative adaptive control for robot manipulators. We first consider the case
of multiple robots manipulating a common object through synchronous centralized
update laws to identify unknown inertial parameters. Through this development,
we introduce a notion of Collective Sufficient Richness, wherein parameter
convergence can be enabled through teamwork in the group. The introduction of
this property and the analysis of stable adaptive controllers that benefit from
it constitute the main new contributions of this work. Building on this
original example, we then consider decentralized update laws, time-varying
network topologies, and the influence of communication delays on this process.
Perhaps surprisingly, these nonidealized networked conditions inherit the same
benefits of convergence being determined through collective effects for the
group. Simple simulations of a planar manipulator identifying an unknown load
are provided to illustrate the central idea and benefits of Collective
Sufficient Richness.Comment: ICRA 201
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