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

Slides:



Advertisements
Similar presentations
Is There Light at the Ends of the Tunnel? Wireless Sensor Networks for Adaptive Lighting in Road Tunnels IPSN 2011 Sean.
Advertisements

By: Evan Olson and Vishal Doshi. Introduction Autonomous Vehicles allow expanded capabilities over manned data collection Relatively low cost, versatile.
The Design and Evaluation of a Mobile Sensor/Actuator Network for Autonomous Animal Control Presented by Nashwa Ahmed.
Dario Pompili, Tommaso Melodia {dario,
A discussion on.  Path Planning  Autonomous Underwater Vehicles  Adaptive Sampling  Mixed Integer Linear programming.
Berkeley Delay/Disruption-Tolerant Networking Architecture & Applications Kevin Fall w/help from A. Maffei Intel Research, Berkeley & WHOI
A Survey of Artificial Intelligence Applications in Water-based Autonomous Vehicles Daniel D. Smith CSC 7444 December 8, 2008.
Sample Size Estimation and Re- Estimation Break-out Session.
IMagic Senior Project Emre AYDIN Asil Kaan BOZCUOĞLU Onur ÖZBEK Egemen VARDAR Onur YÜRÜTEN Project Supervisor: Asst. Prof. Uluç Saranlı.
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.
Centre for Autonomous Systems Petter ÖgrenCAS talk1 A Control Lyapunov Function Approach to Multi Agent Coordination P. Ögren, M. Egerstedt * and X. Hu.
Soul Envoy Final Year Project 22nd April 2006 By Zhu Jinhao.
PORT: A Price-Oriented Reliable Transport Protocol for Wireless Sensor Networks Yangfan Zhou, Michael. R. Lyu, Jiangchuan Liu † and Hui Wang The Chinese.
Ocean Technology Test Bed Colin Bradley, University of Victoria John Roston, McGill University NEPTUNE Canada VENUS.
Temporally and Spatially Deconflicted Path Planning for Multiple Marine Vehicles A. Häusler 1, R. Ghabcheloo 2, A. Pascoal 1, A. Aguiar 1 I. Kaminer 3,
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.
N.E. Leonard – ASAP Progress Meeting – February 17-18, 2005 Slide 1/22 ASAP Progress Report Adaptive Sampling and Cooperative Control Naomi Ehrich Leonard.
Designing a Glider Network to Monitor Rapid Climate Change: Evaluation of Heat Transport Amelia Snow 1, Scott Glenn 1, Darcy Glenn 2, Holly Ibanez 3, John.
EEC 693/793 Wireless Sensor Networks A Short Name of your Paper’s Title Student Name.
Underwater Gliders at NRC-IOT Ralf Bachmayer NRC-Institute for Ocean Technology St. John’s, NL, Canada Ralf Bachmayer NRC-Institute for Ocean Technology.
MIT Computer Science & Artificial Intelligence Laboratory MIT Dept. of Mechanical Engineering Cooperative Navigation for Groups of Autonomous Underwater.
P. Ögren (KTH) N. Leonard (Princeton University)
MIT Computer Science & Artificial Intelligence Laboratory MIT Dept. of Mechanical Engineering.
Adaptive Sampling RTOC 8/24/03 AS Goals/Metrics Review Expanded Dorado Experiment Prepared by Ralf Bachmayer, Pradeep Bhatta Princeton University.
Creating a real world of opportunity through a virtual trading estate Dr Simon Brown Director for Enterprise Teaching and Learning.
ASAP/AESOP INTEGRATION OBSERVATIONS ASAP Aircraft –Heat/Momentum fluxes to AESOP –Front mapping for AESOP for float deployment Dedicated P3 AXBT wide area.
ASAP Kickoff Meeting June 28-29, 2004 Adaptive Sampling Plans: Optimal Mobile Sensor Array Design Naomi Ehrich Leonard Mechanical and Aerospace Engineering.
Vanderbilt University Vibro-Acoustics Laboratory Distributed Control with Networked Embedded Systems Objectives Implementation of distributed, cooperative.
Real-time Video Streaming from Mobile Underwater Sensors 1 Seongwon Han (UCLA) Roy Chen (UCLA) Youngtae Noh (Cisco Systems Inc.) Mario Gerla (UCLA)
INTEGRATED PROGRAMME IN AERONAUTICAL ENGINEERING Coordinated Control, Integrated Control and Condition Monitoring in Uninhabited Air-Vehicles Ian Postlethwaite,
UnderWater Acoustic Sensor Networks (UW-ASN) -Xiong Junjie
Slide Adaptive Sampling and Prediction (ASAP) AOSN-II Undersea Persistent Surveillance (UPS) Autonomous Wide Aperture.
Utilizing Remote Sensing, AUV’s and Acoustic Biotelemetry to Create Dynamic Single Species Distribution Models the Mid-Atlantic Matthew Breece, Matt Oliver,
CeNCOOS Glider Activities Francisco Chavez Senior Scientist Monterey Bay Aquarium Research Institute.
UNDERWATER GLIDERS.
N.E. Leonard – U. Pisa – April 2007 Slide 1 Cooperative Control and Mobile Sensor Networks Application to Mobile Sensor Networks, Part I Naomi Ehrich.
Daniel E. Frye A GENERAL VIEW OF GATEWAY PLATFORMS Daniel E. Frye Woods Hole Oceanographic Institution.
Robust localization algorithms for an autonomous campus tour guide Richard Thrapp Christian Westbrook Devika Subramanian Rice University Presented at ICRA.
Determination of Number of Probe Vehicles Required for Reliable Travel Time Measurement in Urban Network.
Symbiotic Simulation of Unmanned Aircraft Systems (UAS)
Ocean Observatories Initiative OOI CI Kick-Off Meeting Devils Thumb Ranch, Colorado September 9-11, 2009 Autonomous Marine Sensing and Control Arjuna Balasuriya,
Marine Instrumentation Class
By: Sheri Huang Dr. Kishore Hao Men. What is an autonomous vehicle? Also known as a driverless car Computer controlled The system must be able to control.
Status Report #7 Wireless GPS-Navigated Autonomous Vehicle April 9th, 2007 Purpose: To implement a fastest route algorithm in a GPS enabled vehicle. Prepared.
788.11J Presentation “Herding Cows with Sensors” Presented by Ryan Boder.
OES Singapore’s Plan on Local AUV Competition in 2012 Oct 2010.
1 Ocean Observing With Gliders Created by Carmelina Livingston for EARTH, St. Andrew’s School of Math & Science, Charleston, SC, 2010.
Adaptive Tracking in Distributed Wireless Sensor Networks Lizhi Yang, Chuan Feng, Jerzy W. Rozenblit, Haiyan Qiao The University of Arizona Electrical.
Using Adaptive Tracking To Classify And Monitor Activities In A Site W.E.L. Grimson, C. Stauffer, R. Romano, L. Lee.
Unmanned Mobile Sensor Net - Ben Snively Unmanned Underwater Gliders Survey and extensions to work from: COOPERATIVE CONTROL OF COLLECTIVE MOTION FOR OCEAN.
Smart Sleeping Policies for Wireless Sensor Networks Venu Veeravalli ECE Department & Coordinated Science Lab University of Illinois at Urbana-Champaign.
Adaptive Sampling for MB’03 – Preliminary Update for Discussion Naomi Ehrich Leonard Mechanical and Aerospace Engineering Princeton University and the.
Dynamic Mission Planning for Multiple Mobile Robots Barry Brumitt and Anthony Stentz 26 Oct, 1999 AMRS-99 Class Presentation Brian Chemel.
ParkNet: Drive-by Sensing of Road-Side Parking Statistics Irfan Ullah Department of Information and Communication Engineering Myongji university, Yongin,
Ocean Observatories Initiative OOI CI Kick-Off Meeting Devils Thumb Ranch, Colorado September 9-11, 2009 Observation Planning and Autonomous Mission Execution.
© 2012 Anwendungszentrum GmbH Oberpfaffenhofen Idea by: Dr. Eng. Mohamed Zayan | 1.
788.11J Presentation “Flock Control: Using Information Energy” Presented by Mukundan Sridharan.
Auto N omous, self-Learning, OPTI mal and comp L ete U nderwater S ystems NOPTILUS FP7-ICT : Information and Communication Technologies NOPTILUS.
Command Current Expected Commands from Autonomy: Forward velocity, Lateral velocity, Heading, Depth + - Dynamics Thrusters to Forces/Moments Mapping.
Terrain Reconstruction Method Based on Weighted Robust Linear Estimation Theory for Small Body Exploration Zhengshi Yu, Pingyuan Cui, and Shengying Zhu.
Liam Paull1,2, Mae Seto2,3 and John Leonard1
Vesa Klumpp, Knowtion Applications of Intelligent Control in Industry and Adaption to Space Missions Vesa Klumpp, Knowtion
Multi-Agent Exploration
E190Q – Project Introduction Autonomous Robot Navigation
Underwater wireless communication
Electronics Engineering Division
UNDERWATER GLIDERS.
SENSOR BASED CONTROL OF AUTONOMOUS ROBOTS
788.11J Presentation “Active Visitor Guidance System”
Presentation transcript:

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

The Main Idea Cooperative control of multiple Autonomous Underwater Vehicle (AUV), using VBAP VBAP: Virtual Body and Artificial Potentials –Adaptable formation Control, based on sensor feedback –Design in 3D space –Bounded Error –Design dynamics for the VB, like rotation, dilation.

The Main Achievements and Contribution Making it work VBAP Algorithm Glider Simulator Implementation Design

The Challenges Underwater Communication (?) The unpredictable external water currents – By considering the previous current measurements when calculating new mission paths. Latency: – The GPS location is one cycle old – The current estimate is two cycle old.

A Slocum Glider developed by Webb Research Corporation

Results

Centroid Error

Innovation The design of the glider –Buoyancy engine –No external thrust Satellite based Communication

Reference Multi-AUV Control and Adaptive Sampling in Monterey Bay.Multi-AUV Control and Adaptive Sampling in Monterey Bay