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European Signal Processing Conference (EUSIPCO)

作者： Sabina Zejnilovic João Gomes Bruno Sinopoli Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Institute for Systems and Robotics-Instituto Superior Técnico (ISR/IST) Technical University of Lisbon Portugal

We propose a collaborative, energy efficient method for diffusive source localization in wireless sensor networks. The algorithm is based on distributed and iterative maximum-likelihood (ML) estimation, which is very sensitive to initialization. As a part of the proposed method we present an approach for obtaining a “good enough” initial value for the ML recursion based on infinite time approximation and semidefinite programming. We also present an approach for determining the sensor node that initiates the estimation process. To improve the convergence rate of the algorithm, we consider the case where selected nodes collaborate with their neighbors. Simulation results are used to characterize the performance and energy efficiency of the algorithm. We also illustrate estimation accuracy/energy consumption trade-off by varying the communication radius of sensor nodes.

关键词： Tin Maximum likelihood estimation Energy consumption Collaboration Wireless sensor networks Approximation methods

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Distributed Nesterov-like gradient algorithms

Distributed Nesterov-like gradient algorithms

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IEEE Conference on Decision and Control

作者： Dušsan Jakovetić José M. F. Moura João Xavier Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Institute for Systems and Robotics (ISR) Instituto Superior Técnico (IST) Technical University of Lisbon Lisbon Portugal

ISBN: (纸本)9781467320658

In classical, centralized optimization, the Nesterov gradient algorithm reduces the number of iterations to produce an ε-accurate solution (in terms of the cost function) with respect to ordinary gradient from O(1/ε) to equation. This improvement is achieved on a class of convex functions with Lipschitz continuous first derivative, and it comes at a very small additional computational cost per iteration. In this paper, we consider distributed optimization, where nodes in the network cooperatively minimize the sum of their private costs subject to a global constraint. To solve this problem, recent literature proposes distributed (sub)gradient algorithms, that are attractive due to computationally inexpensive iterations, but that converge slowly-the ε error is achieved in O(1/ε 2 ) iterations. Here, building from the Nesterov gradient algorithm, we present a distributed, constant step size, Nesterov-like gradient algorithm that converges much faster than existing distributed (sub)gradient methods, with zero additional communications and very small additional computations per iteration k. We show that our algorithm converges to a solution neighborhood, such that, for a convex compact constraint set and optimized stepsize, the convergence time is O(1/ε). We achieve this on a class of convex, coercive, continuously differentiable private costs with Lipschitz first derivative. We derive our algorithm through a useful penalty, network's Laplacian matrix-based reformulation of the original problem (referred to as the clone problem) - the proposed method is precisely the Nesterov-gradient applied on the clone problem. Finally, we illustrate the performance of our algorithm on distributed learning of a classifier via logistic loss.

关键词： Cloning Convergence Vectors Logistics Cost function Approximation algorithms

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Convergence rate analysis of distributed gradient methods for smooth optimization

Convergence rate analysis of distributed gradient methods fo...

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Telecommunications Forum (TELFOR)

作者： Dušan Jakovetić João Xavier José M. F. Moura Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Institute for Systems and Robotics (ISR) Instituto Superior Técnico (IST) Technical University of Lisbon Lisboa Portugal

We derive the convergence rate of a distributed gradient algorithm for smooth optimization in networked systems. We assume that each agent in the network has a convex cost function f i (x), known only to agent i, and the goal is to minimize the sum Σ N i=1 fi(x) of all agents' costs; such problem formulation arises in various networked applications, like, e.g., distributed inference or source localization in sensor networks. With the distributed gradient algorithm under study, each agent, at each iteration k, performs a weighted average of its own and its neighbors' solution estimates, and performs a step in the direction of the negative of its local function's gradient. We establish a novel result that the distributed gradient algorithm has the convergence rate O(1/k 2/3 ), in terms of the cost function optimality gap, under the assumption of convex f i 's with Lipschitz continuous and bounded gradients.

关键词： Convergence Gradient methods Algorithm design and analysis Upper bound Cost function Educational institutions

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Reconstruction of 3D lines from a single axial catadioptric image using cross-ratio

Reconstruction of 3D lines from a single axial catadioptric ...

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International Conference on Pattern Recognition

作者： Luis Perdigoto Helder Araujo Institute for Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal ESTG Polytechnic Institute of Leiria Leiria Portugal

ISBN: (纸本)9781467322164

It has been shown, in previous work, that the 3D position of a line can be reconstructed from a single image in vision systems that do not possess a single viewpoint. We present a new method that, in a non-central axial catadioptric system, can achieve line spacial reconstruction from 3 or more image points, given the distance ratio of 3 points in the line (a fair assumption in, for example, structured environment with repetitive architectural features). We use cross-ratio as an invariant to constrain the line localization and perform the reconstruction from a set of image points through non-linear optimization. Experimental results are presented.

关键词： Image reconstruction Mirrors Cameras Geometry Vectors Noise Calibration

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Behavioral modeling and testing of a CMOS-MEMS parametric resonator governed by the nonlinear Mathieu equation

Behavioral modeling and testing of a CMOS-MEMS parametric re...

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IEEE International Conference on Micro Electro Mechanical systems

作者： Congzhong Guo Gary K. Fedder Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Department of Electrical and Computer Engineering Institute for Complex Engineered Systems The Robotics Institute Carnegie Mellon University Pittsburgh PA USA

Modeling and simulation of complex phenomena in environments that emulate end applications demonstrate the effectiveness of MEMS composable design methodologies. A CMOS-MEMS nonlinear resonator featuring parametric excitation driven by an oscillating voltage applied across the non-interdigitated comb fingers has been targeted as a demonstration vehicle. This paper reports the schematic-based parameterized behavioral modeling and vibration testbed of parametric resonators governed by the nonlinear Mathieu equation. The linear and cubic stiffness of the electrostatic force and the folded flexure are modeled by Verilog-A MEMS behavioral models. The transition frequencies and the jump amplitudes are characterized optically by sweeping the frequency bi-directionally. The observed parametric resonance, occurring near excitation frequency of twice the resonant frequency, is verified by perturbation solution of Mathieu equation, and validated by circuit-level behavioral model simulation matching to 0.6%. The dynamic behavior and its dependence on system parameters are also analyzed.

关键词： Mathematical model Resonant frequency Fingers Analytical models Frequency measurement Electrostatics Force

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ADMM For Consensus On Colored Networks

ADMM For Consensus On Colored Networks

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IEEE Annual Conference on Decision and Control

作者： Joao F. C. Mota Joao M. F. Xavier Pedro M. Q. Aguiar Markus Puschel Department of Electrical and Computer Engineering Carnegie Mellon University Institute of Systems and Robotics Instituto Superior Tecnico Technical University of Lisbon Department of Computer Science

ISBN: (纸本)9781467320658

We propose a novel distributed algorithm for one of the most fundamental problems in networks: the average consensus. We view the average consensus as an optimization problem, which allows us to use recent techniques and results from the optimization area. Based on the assumption that a coloring scheme of the network is available, we derive a decentralized, asynchronous, and communication-efficient algorithm that is based on the Alternating Direction Method of Multipliers (ADMM). Our simulations with other state-of-the-art consensus algorithms show that the proposed algorithm is the one exhibiting the most stable performance across several network models.

关键词： distributed algorithms Network network model simulating

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Evaluation of Brain-computer Interfaces in Accessing computer and other Devices by People with Severe Motor Impairments

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Procedia computer Science 2012年 14卷 283-292页

作者： Gabriel Pires Urbano Nunes Miguel Castelo-Branco Institute for Systems and Robotics (ISR) University of Coimbra 3030-290 Coimbra Portugal Department of Electrical Engineering Polytechnic Institute of Tomar 2300-313 Tomar Portugal Biomedical Institute for Research in Light and Image (IBILI) University of Coimbra 3000-548 Coimbra Portugal

A brain-computer interface (BCI) translates brain signals into commands that can be used to control computer applications or external devices. BCI provides a non-muscular communication channel and therefore it assumes a crucial importance for individuals with motor functions severely affected. The evaluation of BCI by individuals with severe disabilities is of utmost importance to understand the BCI feasibility as an assistive technology. This paper summarizes some of the results achieved in our research lab, with different BCIs tested by individuals with severe motor disabilities, focusing on some practical aspects of BCI evaluation, and on the target population.

关键词： Brain-computer interface (BCI) P300 motor disabilities assistive technology

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Distributed ADMM for Model Predictive Control and Congestion Control

Distributed ADMM for Model Predictive Control and Congestion...

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IEEE Annual Conference on Decision and Control

ISBN: (纸本)9781467320658

Many problems in control can be modeled as an optimization problem over a network of nodes. Solving them with distributed algorithms provides advantages over centralized solutions, such as privacy and the ability to process data locally. In this paper, we solve optimization problems in networks where each node requires only partial knowledge of the problem's solution. We explore this feature to design a decentralized algorithm that allows a significant reduction in the total number of communications. Our algorithm is based on the Alternating Direction of Multipliers (ADMM), and we apply it to distributed Model Predictive Control (MPC) and TCP/IP congestion control. Simulation results show that the proposed algorithm requires less communications than previous work for the same solution accuracy.

关键词： distributed model predictive control Communication Research PROBLEM SOLUTION Model predictive control Predictive control distributed algorithms Privacy nonlinear model predictive control Batch data processing control simulation Solutions Standing Orders Ganglia

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Herb 2.0: Lessons learned from developing a mobile manipulator for the home

Herb 2.0: Lessons learned from developing a mobile manipulat...

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作者： Srinivasa, Siddhartha S. Berenson, Dmitry Cakmak, Maya Collet, Alvaro Dogar, Mehmet R. Dragan, Anca D. Knepper, Ross A. Niemueller, Tim Strabala, Kyle Vande Weghe, Mike Ziegler, Julius Robotics Institute Carnegie Mellon University Pittsburgh PA 15213 United States Electrical Engineering and Computer Science Department University of California Berkeley Berkeley 94703 United States School of Interactive Computing Georgia Institute of Technology Atlanta GA 30332 United States Knowledge-based Systems Group RWTH Aachen University Aachen 52062 Germany Department of Measurement and Control Systems Karlsruhe Institute of Technology Karlsruhe D-76128 Germany

We present the hardware design, software architecture, and core algorithms of Herb 2.0, a bimanual mobile manipulator developed at the Personal robotics Lab at Carnegie Mellon University, Pittsburgh, PA. We have developed Herb 2.0 to perform useful tasks for and with people in human environments. We exploit two key paradigms in human environments: that they have structure that a robot can learn, adapt and exploit, and that they demand general-purpose capability in robotic systems. In this paper, we reveal some of the structure present in everyday environments that we have been able to harness for manipulation and interaction, comment on the particular challenges on working in human spaces, and describe some of the lessons we learned from extensively testing our integrated platform in kitchen and office environments. © 2012 IEEE.

关键词： Motion planning

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Saturated Output Feedback Control of a Quadrotor Aircraft

Saturated Output Feedback Control of a Quadrotor Aircraft

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American Control Conference

作者： D. Cabecinhas R. Cunha C. Silvestre Department of Electrical Engineering and Computer Science and Institute for Robotics and Systems in Engineering and Science (LARSyS) Instituto Superior Tecnico Universidade Tecnica de Lisboa 1049-001 Lisboa Portugal

ISBN: (数字)9781457710964

ISBN: (纸本)9781457710957

This paper addresses the problem of steering a quadrotor vehicle along a prescribed trajectory. The problem is formulated so as to enforce bounds on the thrust actuation and present robustness against possible disturbances resulting from errors in the estimation of the aircraft state. The proposed solution consists of a nonlinear state feedback controller for thrust and torque actuation that i) guarantees asymptotic stability of the closed-loop system in the presence of exponentially decaying observer errors, ii) ensures global regulation of the trajectory tracking errors, and iii) defines the thrust actuation as a saturated function of the position and linear velocity errors. Simulation results are presented to assess the performance and robustness of the proposed controller.

关键词： Trajectory Vehicles Observers Lyapunov methods Convergence Vectors Robustness

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