N.E. Leonard – U. Pisa – 18-20 April 2007 Slide 1 Cooperative Control and Mobile Sensor Networks Application to Mobile Sensor Networks, Part I Naomi Ehrich.

Slides:

Advertisements

Similar presentations

N.E. Leonard – U. Pisa – April 2007 Slide 1 Cooperative Control and Mobile Sensor Networks Application to Mobile Sensor Networks, Part II Naomi Ehrich.

Advertisements

Adapting Ocean Surveys to the Observed Fields Characteristics Maria-João Rendas I3S, CNRS-UNSA.

COORDINATION and NETWORKING of GROUPS OF MOBILE AUTONOMOUS AGENTS.

Maryland Virtual High School An investigation into the factors contributing to hurricane intensity.

Robust and Efficient Control of an Induction Machine for an Electric Vehicle Arbin Ebrahim and Dr. Gregory Murphy University of Alabama.

Xu Chen Xiaowen Gong Lei Yang Junshan Zhang

Environmental Data Collection Using Autonomous Wave Gliders Qing Wang, Dick Lind, Kate Hermsdorfer Meteorology Department Kevin B. Smith, Joe Rice Physics.

7/25/20051 Network UAV C3 Stage 2 Program Preview Timothy X Brown, Brian Argrow University of Colorado at Boulder Interdisciplinary Telecommunications.

1 E190Q – Project Introduction Autonomous Robot Navigation Team Member 1 Name Team Member 2 Name.

N.E. Leonard – U. Pisa – April 2007 Slide 1 Cooperative Control and Mobile Sensor Networks Cooperative Control, Part I, A-C Naomi Ehrich Leonard.

Target Tracking Algorithm based on Minimal Contour in Wireless Sensor Networks Jaehoon Jeong, Taehyun Hwang, Tian He, and David Du Department of Computer.

Measuring the Earths Dimensions

N.E. Leonard – U. Pisa – April 2007 Slide 1 Cooperative Control and Mobile Sensor Networks Cooperative Control, Part II Naomi Ehrich Leonard Mechanical.

Centre for Autonomous Systems Petter ÖgrenCAS talk1 A Control Lyapunov Function Approach to Multi Agent Coordination P. Ögren, M. Egerstedt * and X. Hu.

PORT: A Price-Oriented Reliable Transport Protocol for Wireless Sensor Networks Yangfan Zhou, Michael. R. Lyu, Jiangchuan Liu † and Hui Wang The Chinese.

Path Planning for Multiple Marine Vehicles Andreas J. Häusler¹, Reza Ghabcheloo², Isaac Kaminer³ António M. Pascoal¹, A. Pedro Aguiar¹ ¹Instituto Superior.

Darcy Glenn 1, Holly Ibanez 2, Amelia Snow 3, Oscar Schofield 3 1 University of Vermont 2 Florida Institute of Technology 3 Rutgers University Designing.

Adaptive Sampling RTOC 8/15/03 AS Goals/Metrics Review Proposed Dorado Experiment Prepared by Ralf Bachmayer, Eddie Fiorelli, Pradeep Bhatta Princeton.

Centre for Autonomous Systems Petter ÖgrenOptSyst seminar 23/51 Formations and Obstacle Avoidance in Mobile Robot Control Petter Ögren Topics from a Doctoral.

N.E. Leonard – ASAP Progress Meeting – February 17-18, 2005 Slide 1/22 ASAP Progress Report Adaptive Sampling and Cooperative Control Naomi Ehrich Leonard.

An Adaptive Coordinated Medium Access Control for Wireless Sensor Networks Jing Ai, Jingfei Kong, Damla Turgut Networking and Mobile Computing (NetMoC)

Adaptive Sampling And Prediction Dynamical Systems Methods for Adaptive Sampling ASAP Kickoff Meeting June 28, 2004 Shawn C. Shadden (PI: Jerrold Marsden)

Boundary Detection Jue Wang and Runhe Zhang. May 17, 2004 UCLA EE206A In-class presentation 2 Outline Boundary detection using static nodes Boundary detection.

Cooperative Electronic Chaining using Small Unmanned Aircraft Cory Dixon & Eric W. Frew May 10, 2007.

City College of New York 1 John (Jizhong) Xiao Department of Electrical Engineering City College of New York Projects for Advanced.

Cooperative Q-Learning Lars Blackmore and Steve Block Expertness Based Cooperative Q-learning Ahmadabadi, M.N.; Asadpour, M IEEE Transactions on Systems,

Approach: Networked Aquatic Microbial Observing System Approach: Networked Aquatic Microbial Observing System Results: Data From Both Networked Sensors.

QARTOD 2: Remote Currents Working Group Definitions: Level 1 – refers to radials Level 2 – refers to total vectors Level 3 – refers to higher level data.

Adaptive Sampling RTOC 8/26/03 AS Goals/Metrics Review Expanded Dorado Experiment Drifter chase experiment Prepared by Ralf Bachmayer, Pradeep Bhatta,

8/22/20061 Maintaining a Linked Network Chain Utilizing Decentralized Mobility Control AIAA GNC Conference & Exhibit Aug. 21, 2006 Cory Dixon and Eric.

Adaptive Sampling RTOC 8/21/03 AS Goals/Metrics Review Expanded Dorado Experiment Prepared by Ralf Bachmayer, Pradeep Bhatta Princeton University.

Adaptive Sampling RTOC 8/24/03 AS Goals/Metrics Review Expanded Dorado Experiment Prepared by Ralf Bachmayer, Pradeep Bhatta Princeton University.

Distributed Cooperative Control of Multiple Vehicle Formations Using Structural Potential Functions Reza Olfati-Saber Postdoctoral Scholar Control and.

N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 1/16 Adaptive Sampling and Prediction (ASAP) Additional Collaborators: Jim Bellingham (MBARI)

Problem Description: To develop an autonomous network for monitoring aquatic environment Problem Description: To develop an autonomous network for monitoring.

Robotic Sensor Networks: from theory to practice CSSE Annual Research Review Sameera Poduri.

ASAP Kickoff Meeting June 28-29, 2004 Adaptive Sampling Plans: Optimal Mobile Sensor Array Design Naomi Ehrich Leonard Mechanical and Aerospace Engineering.

2-5 Mar, 2015IHC1 Sensitivity of Ocean Sampling for Coupled COAMPS-TC Prediction Sue Chen 1, James Cummings 2, Jerome Schmidt 1, Peter Black 2, Elizabeth.

N.E. Leonard – Block Island Workshop on Swarming – June 3, 2009 Slide 1 Spatial Dynamics, Information Flow and Collective Behavior Naomi Ehrich Leonard.

A Sweeper Scheme for Localization and Mobility Prediction in Underwater Acoustic Sensor Networks K. T. DharanC. Srimathi*Soo-Hyun Park VIT University Vellore,

Deformable Models Segmentation methods until now (no knowledge of shape: Thresholding Edge based Region based Deformable models Knowledge of the shape.

Slide Adaptive Sampling and Prediction (ASAP) AOSN-II Undersea Persistent Surveillance (UPS) Autonomous Wide Aperture.

Algorithms for Robot-based Network Deployment, Repair, and Coverage Gaurav S. Sukhatme Center for Robotics and Embedded.

788.11J Presentation “Multi-AUV Control” Presented By Mukundan Sridharan.

UNDERWATER GLIDERS.

R. Bencatel (UM)Flight in Flow FieldsR. Bencatel (UM)April 22nd, Flight in Flow Fields Ricardo Bencatel Department of Aerospace Engineering University.

“Toward a GOOS glider programme: Tools and methods” General Assembly.

Importance to the Off-Shore Energy Industry James Done Chad Teer, Wikipedia NCAR Earth System Laboratory National Center for Atmospheric Research NCAR.

Integrated Engineering Course at Budapest University of Technology and Economics Presented by Peter Korondi.

Work Package 11 Task 11.1: Towards an optimum observational network Ute Schuster, University of Exeter, UK.

Design, Optimization, and Control for Multiscale Systems

Leverage the data characteristics of applications and computing to reduce the communication cost in WSNs. Design advanced algorithms and mechanisms to.

Cooperative Q-Learning Lars Blackmore and Steve Block Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents Tan, M Proceedings of the.

N.E. Leonard – MBARI – August 1, 2006 Slide 1/46 Cooperative Control and Mobile Sensor Networks in the Ocean Naomi Ehrich Leonard Mechanical & Aerospace.

MMAC: A Mobility- Adaptive, Collision-Free MAC Protocol for Wireless Sensor Networks Muneeb Ali, Tashfeen Suleman, and Zartash Afzal Uzmi IEEE Performance,

Iso-Contour Queries and Gradient Descent with Guaranteed Delivery in Sensor Networks Rik Sarkar, Xianjin Zhu, Jie Gao, Joseph S. B. Micchell, Leonidas.

Problem Description: Locate and Track Dynamic Gradient Sources (Marine Microorganisms) Problem Description: Locate and Track Dynamic Gradient Sources (Marine.

City College of New York 1 John (Jizhong) Xiao Department of Electrical Engineering City College of New York Mobile Robot Control G3300:

Ocean Energy EGEE 101H Jeffrey Singer & Matthew Quillen.

Decentralized Control of Mobile Sensor Networks Kevin M. Lynch Laboratory for Intelligent Mechanical Systems Mechanical Engineering Department Northwestern.

Unmanned Mobile Sensor Net - Ben Snively Unmanned Underwater Gliders Survey and extensions to work from: COOPERATIVE CONTROL OF COLLECTIVE MOTION FOR OCEAN.

Doc.: a Submission September 2004 Z. Sahinoglu, Mitsubishi Electric research LabsSlide 1 A Hybrid TOA/RSS Based Location Estimation Zafer.

Adaptive Sampling for MB’03 – Preliminary Update for Discussion Naomi Ehrich Leonard Mechanical and Aerospace Engineering Princeton University and the.

Reliable Navigation of Mobile Sensors in Wireless Sensor Networks without Localization Service Qingjun Xiao, Bin Xiao, Jiaqing Luo and Guobin Liu Department.

Multidisciplinary Ocean Dynamics and Engineering Laboratory: Simulation, Estimation and Assimilation Systems Massachusetts Institute of Technology, Department.

Sensor Network with Mobile Access: Network-Based Control (An Experimental Testbed) Youngchul Sung Adaptive Communications and Signal Processing Group School.

E190Q – Project Introduction Autonomous Robot Navigation

Space and time scales in satellite oceanography

Modeling, Predictions and Adaptive Sampling Team

Phases of Matter Cornell Notes page 125.

Presentation transcript:

N.E. Leonard – U. Pisa – April 2007 Slide 1 Cooperative Control and Mobile Sensor Networks Application to Mobile Sensor Networks, Part I Naomi Ehrich Leonard Mechanical and Aerospace Engineering Princeton University and Electrical Systems and Automation University of Pisa

N.E. Leonard – U. Pisa – April 2007 Slide 2 Outline 1.Introduction to cooperative control and mobile sensor networks 2.Cooperative control, part I Virtual bodies and artificial potentials (VBAP) Virtual tensegrity structures Coordination of mechanical system networks Curvature control and level set tracking 3.Application to mobile sensor networks, part I Adaptive gradient climbing; curve tracking; contour plots; adaptation of sampling resolution 4.Cooperative control, part II Coupled oscillator dynamics and collective motion Consensus dynamics and collective motion with limited communication. Extensions and new directions: multi-scale, resonant patterns, oscillatory speeds. 5.Application to mobile sensor networks, part II Coverage and adaptive sampling 6.Implementation of glider coordinated control system. 7.Connections to collective motion of animal groups (time permitting)

N.E. Leonard – U. Pisa – April 2007 Slide 3 Reference 1.E. Fiorelli, N.E. Leonard, P. Bhatta, D. Paley, R. Bachmayer and D. Fratantoni, Multi-AUV control and adaptive sampling in Monterey Bay, IEEE J. Oceanic Engineering, 31:4, pages , 2006.

N.E. Leonard – U. Pisa – April 2007 Slide 4 Aug 6-7, 2003 Glider temperature profiles Aug 6-7, 2003 August 6 Coordinated Sea Trial

N.E. Leonard – U. Pisa – April 2007 Slide 5 Aug 16, 2003 August 16 Coordinated Glider Sea Trial Average inter-vehicle spacing

N.E. Leonard – U. Pisa – April 2007 Slide 6 Aug. 6, 2003 Results 5 m 30 m

N.E. Leonard – U. Pisa – April 2007 Slide 7 Aug. 16, 2003 Results 5 m 30 m

N.E. Leonard – U. Pisa – April 2007 Slide 8 Exploring Scalar Fields lon. lat Temperature at -20m near Monterey Bay With Fumin Zhang

N.E. Leonard – U. Pisa – April 2007 Slide 9 Exploring Scalar Fields time (hrs) temperature

N.E. Leonard – U. Pisa – April 2007 Slide 10 Front Definition Thermal Front Parameter (TFP) Thermal Front Warm / Cold Fronts Red Tides and Algal Blooms With Francois Lekien and Eddie Fiorelli

N.E. Leonard – U. Pisa – April 2007 Slide 11 Front Tracking Formation maintains optimal shape to estimate gradient, … Virtual beacon tracks the front