1,721,043 research outputs found
Maintaining Limited-Range Connectivity among Second-Order Agents
No full textIn this paper we consider ad-hoc networks of robotic agents with double integrator dynamics. For such networks, the connectivity maintenance problems are: (i) do there exist control inputs for each agent to maintain network connectivity, and (ii) given desired controls for each agent, can one compute the closest connectivity-maintaining controls in a distributed fashion? The proposed solution is based on three contributions. First, we define and characterize admissible sets for double integrators to remain inside disks. Second, we establish an existence theorem for the connectivity maintenance problem by introducing a novel state-dependent graph, called the double-integrator disk graph. Specifically, we show that one can always maintain connectivity by maintaining a spanning tree of this new graph, but one will not always maintain connectivity of a particular agent pair that happens to be connected at one instant of time. Finally, we design a distributed "flow-control" algorithm for distributed computation of connectivity-maintaining controls
QUANTIZED COORDINATION ALGORITHMS FOR RENDEZVOUS AND DEPLOYMENT
No full textIn this paper we study motion coordination problems for groups of robots that exchange information through a rate-constrained communication network. For one-dimensional rendezvous and deployment problems with communication path graphs, we propose an integrated control and communication scheme combining a logarithmic coder/decoder with linear coordination
algorithms. We show that the closed-loop performance is comparable to the one achievable in the
quantization-free model: the time complexity is unchanged and the exponential convergence factor
degrades smoothly as the quantization accuracy becomes coarser
Finite-Field Consensus
No full textThis work studies consensus networks over finite fields, where agents process and communicate values from the set of integers f0; : : : ; p 1g, for some prime number p, and operations are performed modulo p. For consensus networks over finite fields we provide necessary and sufficient conditions on the network topology and weights to ensure convergence. For instance we show that, differently from the case of consensus networks over the field of real numbers, consensus networks over finite fields converge in finite time, and that properties of the agents interaction graph are not sufficient to ensure finitefield consensus. Finally, we discuss the application of finite-field consensus to distributed averaging in sensor network
Network Abstract Linear Programming with Application to Cooperative Target Localization
No full textWe identify a novel class of distributed optimization problems, namely a networked version of abstract linear programming. For such problems we propose distributed algorithms for networks with various connectivity and/or memory constraints. Finally, we show how a suitable target localization problem can be tackled through appropriate linear programs
Quantized average consensus via dynamic coding/decoding schemes
No full textIn the average consensus a set of linear systems has to be driven to the same final state which
corresponds to the average of their initial states. This mathematical problem can be seen as the
simplest example of coordination task. In fact it can be used to model both the control of multiple
autonomous vehicles which all have to be driven to the centroid of the initial positions, and to model
the decentralized estimation of a quantity from multiple measure coming from distributed sensors.
This contribution presents a consensus strategy in which the systems can exchange information
among themselves according to a fixed strongly connected digital communication network. Beside
the decentralized computational aspects induced by the choice of the communication network, we
here have also to face the quantization effects due to the digital links. We here present and discuss two
different encoding/decoding strategies with theoretical and simulation results on their performance
Gossip coverage control for robotic networks: dynamical systems on the space of partitions
No full textFinite-time influence systems and the wisdom of crowd effect
No full textRecent contributions have studied how an influence system may affect the wisdom of crowd phenomenon. In the so-called naïve learning setting, a crowd of individuals holds opinions that are statistically independent estimates of an unknown parameter; the crowd is wise when the average opinion converges to the true parameter in the limit of infinitely many individuals. Unfortunately, even starting from wise initial opinions, a crowd subject to certain influence systems may lose its wisdom. It is of great interest to characterize when an influence system preserves the crowd wisdom effect. In this paper we introduce and characterize numerous wisdom preservation properties of the basic French-DeGroot influence system model. Instead of requiring complete convergence to consensus as in the previous naïve learning model by Golub and Jackson, we study finite-time executions of the French-DeGroot influence process and establish in this novel context the notion of prominent families (as a group of individuals with outsize influence). Surprisingly, finite-time wisdom preservation of the influence system is strictly distinct from its infinite-time version. We provide a comprehensive treatment of various finite-time wisdom preservation notions, counterexamples to meaningful conjectures, and a complete characterization of equal-neighbor influence systems
On visibility maintenance via controlled invariance for leader-follower Dubins-like vehicles
No full textThe paper studies the visibility maintenance problem (VMP) for a leader-follower pair of robots modelled as first-order dynamic systems and proposes an original solution based on the notion of controlled invariance. The nonlinear model describing the relative dynamics of the vehicles is interpreted as linear uncertain system, with the leader robot acting as an external disturbance. The VMP can then be reformulated as a linear constrained regulation problem with additive disturbances (DLCRP). New positive D-invariance conditions for linear uncertain systems with parametric disturbance matrix are introduced and used to solve the VMP when box bounds on the state, control input and disturbance are considered. The proposed design procedure can be easily adapted to provide the control with UBB disturbances rejection capabilities. As an extension, the paper addresses the VMP on a circle. Simulation experiments show the effectiveness of the proposed designs
Contraction Analysis of Hopfield Neural Networks with Hebbian Learning
No full textMotivated by advances in neuroscience and machine learning, this paper is concerned with the modeling and analysis of Hopfield neural networks with dynamic recurrent connections undergoing Hebbian learning. To capture the synaptic sparsity of neural circuits, we propose a low dimensional formulation for the model and then characterize its key dynamical properties. First, we give a biologically-inspired forward invariance result. Then, we give sufficient conditions for the non-Euclidean contractivity of the model. Our contraction analysis leads to stability and robustness of time-varying trajectories - for networks with both excitatory and inhibitory synapses governed by both Hebbian and anti-Hebbian rules. Our proposed contractivity test is based upon biologically meaningful quantities, e.g., neural and synaptic decay rate, maximum out-degree, and the maximum synaptic strength. Finally, we show that the model satisfies Dale's principle. The effectiveness of our results is illustrated via a numerical example
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