Honeywell Navigation, Communication and Control SAE G&C Committee Meeting Oct 19, 2005

Slides:



Advertisements
Similar presentations
Robot Sensor Networks. Introduction For the current sensor network the topography and stability of the environment is uncertain and of course time is.
Advertisements

Enter Your Organization Enter Name of Technology
Simbeeotic: A Simulator and Testbed for Micro-Aerial Vehicle Swarm Experiments Bryan Kate, Jason Waterman, Karthik Dantu and Matt Welsh Presented By: Mostafa.
Gilbert Islas Feb. 25, 2012 SYSM  A micro air vehicle (MAV) is a class of unmanned aerial vehicles (UAV). unmanned aerial vehicles  Size restrictions.
Concept of Operations UAV and UGV team provides dual perspectives on the target Autonomous transit from base of operations, to surveillance zone, and back.
INNOCON Innovative solutions to the modern real time Arial surveillance challenges.
Sniper Tracking With Wireless Sensor Networks
Automatic Control & Systems Engineering Autonomous Systems Research Mini-UAV for Urban Environments Autonomous Control of Multi-UAV Platforms Future uninhabited.
Softwalls: Preventing Aircraft from Entering Unauthorized Airspace Adam Cataldo Prof. Edward Lee Ian Mitchell Prof. Shankar Sastry CHESS Review May 8,
Image Processing of Video on Unmanned Aircraft Video processing on-board Unmanned Aircraft Aims to develop image acquisition, processing and transmission.
Commodity Architectures and Army Research Challenges Workshop on Edge Computing Using New Commodity Architectures (EDGE) 24 May 2006 J. Michael Coyle Program.
RIT Micro Air Vehicle (MAV) Structural Design Project (P09121)
1 AE - Control and Simulation – Micro Air Vehicle laboratory Flying Robots : the MAV-lab - Delfly: 3g, 10 cm, camera - Guinness book of records - Autonomous.
UNCLASSIFIED//FOR OFFICIAL USE ONLY DISTRIBUTION STATEMENT C: Distribution authorized to Department of Defense (DoD) and U.S. DoD Contractors only. This.
Vehicle Tracking/Payload Release System For Small UAV Project Team
UAV Helicopter Project 12-May-08 Presented by Mark Diel Whirled Air Incorporated Stanford University.
Better Robots 1 The Goal: More Robots Enabling Fewer Soldiers Military “robots” today lack autonomy –Currently, many soldiers control one robot –Want few.
TTCP Uninhabited Air Vehicle Systems Presentation to NDIA Paul Pace Chair AER TP-6.
1 DARPA TMR Program Collaborative Mobile Robots for High-Risk Urban Missions Second Quarterly IPR Meeting January 13, 1999 P. I.s: Leonidas J. Guibas and.
Integrated Avionics Systems
Development of a Mini-UAV for Urban Environments Tony Dodd and Beniamin Apopei.
DISTRIBUTION STATEMENT D: Distribution authorized to the Department of Defense and U.S. DoD contractors. This document contains technical data for Administrative.
Project Partners AUVSI North America 10 th - 14 th August 2009 Mr Reece Clothier Prof. Rodney Walker The Smart Skies Project.
Luis Cordero Business Development phone: mail: Spanish SME ̴ 50 employees > 90% engineers and PhD Spin-off started.
Prospecting for atmospheric energy for autonomous flying machines G. D. Emmitt and C. O'Handley Simpson Weather Associates Lidar Working Group Meeting.
Autonomous Machines By: Tyler Roberts.
Kaifei Chen, Siyuan He, Beidi Chen, John Kolb, Randy H. Katz, David E
The Micro-CART project teaches students how to familiarize themselves with a project that they were not part of from conception to completion. Students.
Computational Mechanics and Robotics The University of New South Wales
Stan Ferguson AIAA Applied Aero Technical Committee.
Built in Collision Avoidance for Unmanned Aircraft Systems (UAS)
Collision Avoidance for Unmanned Aircraft Systems (UAS)
The Eos-Explorer CHENRAN YE IMDE ECE 4665/5666 Fall 2011.
DARPA TMR Program Collaborative Mobile Robots for High-Risk Urban Missions Third Quarterly IPR Meeting May 11, 1999 P. I.s: Leonidas J. Guibas and Jean-Claude.
Computer Vision Driven Micro- Aerial Vehicle (MAV): Obstacles Avoidance Lim-Kwan (Kenny) Kong - Graduate Student Dr. Jie Sheng - Faculty Advisor Dr. Ankur.
Control Science Center of Excellence Overview 28 Feb 2007 Dr. David B. Doman Control Design and Analysis Branch Air Vehicles Directorate Air Force Research.
Symbiotic Simulation of Unmanned Aircraft Systems (UAS)
Physics Based Formation Behavior in Autonomous Robots Mark Patterson.
10/19/2005 ACGSC Fall Meeting, Hilton Head SC Copyright Nascent Technology Corporation © 2005 James D. Paduano 1 NTC ACTIVITIES 2005 Outline 1)Activities.
UAV See & Avoid Employing Vision Sensors
Issues and Challenges for Co-operative UAV Missions Chris Halliday and Tony Dodd.
Calspan Status Update AG&C Committee, March 1-3, 2006 Lake Tahoe, NV E. Ohmit.
Thermal Camera Systems CHILI Jalapeno Habanero. 640 x µm HgCdTe (Mercury Cadmium Telluride) long-wave thermal Less Glint from water Low Life Cycle.
Aeronautics & Astronautics Autonomous Flight Systems Laboratory All slides and material copyright of University of Washington Autonomous Flight Systems.
Weight: 52 kg Hull Diameter: 21.3 cm Vehicle Length: 1.5 meters Depth Range: 4–200 meter (coastal model) or 1000 meter (1- km model) Speed: 0.4 m/sec.
Guidance and Control Programs at Honeywell Sanjay Parthasarathy Honeywell Aerospace Advanced Technology October 11, 2006
Boeing-MIT Collaborative Time- Sensitive Targeting Project July 28, 2006 Stacey Scott, M. L. Cummings (PI) Humans and Automation Laboratory
Motes and Sensor Dust.
Design Team # 4 Design of low cost flight computer for unmanned aerial vehicles Status Report # 5 Ryan Morlino Chris Landeros Sylvester Meighan Stephen.
Reliable Navigation of Mobile Sensors in Wireless Sensor Networks without Localization Service Qingjun Xiao, Bin Xiao, Jiaqing Luo and Guobin Liu Department.
SWARMS Scalable sWarms of Autonomous Robots and Mobile Sensors Ali Jadbabaie, Daniel E. Koditchek, Vijay Kumar (PI), and George Pappas l.
Mini Autonomous Flying Vehicle CASDE is part of the National effort to develop a Micro Air Vehicle. CASDE has chosen a Mini Vehicle, in the short term,
MIT Lincoln Laboratory Dynamic Declarative Networking Exploiting Declarative Knowledge To Enable Energy Efficient Collaborative Sensing Daniel J. Van Hook.
Scarab Autonomous Traverse Carnegie Mellon December 2007 David Wettergreen.
Auto-Park for Social Robots By Team I. Meet the Team Alessandro Pinto ▫ UTRC, Sponsor Dorothy Kirlew ▫ Scrum Master, Software Mohak Bhardwaj ▫ Vision.
International Symposium on Remote Sensing of the Environment The NASA IKHANA UAS Platform Introduction to UAV Content provided by: V. Ambrosia, G. Buoni,
788.11J Presentation “Flock Control: Using Information Energy” Presented by Mukundan Sridharan.
Friends and Partners of Aviation Weather
Counter Drone Technology and Critical Infrastructure Protection
Unmanned Control & Tracking System (UCATS)
Unmanned Surveillance To combat Rhino Poaching
UAV Vision Landing Motivation Data Gathered Research Plan Background
Counter-UAV Challenges: Is GNSS Spoofing Effective?
Lockheed Martin Challenge
Big-Picture Questions
Patent Liability Analysis
Autonomous Targeting Vehicle (ATV)
VEHICLE TECHNOLOGY AIR CONDITIONING SYSTEMS.
DARPA Subterranean Challenge “Software” Projects (first version)
DDR&E Advanced Capabilities Overview
Presentation transcript:

Honeywell Navigation, Communication and Control SAE G&C Committee Meeting Oct 19, 2005

2HONEYWELL - Proprietary SP-SAE-G&C-Oct 2005 Fall Update iPINS – personal navigator system - DARPA seedling program - Demonstration of inertial navigation sensors, dead reckoning, time-of-arrival and gait analysis algorithms Autonomous systems - Micro Air Vehicle (MAV)  Flights by soldiers at Ft. Benning and Schofield  At least 8.5km range, autonomous operations - Organic Air Vehicle – Phase 2 of program commenced  Focus on collision avoidance algorithms and demonstrations  Sensors detect 6mm wire at 50+m  Demonstration of in-flight collision avoidance  Design of vehicle progressing - Heterogeneous Urban RSTA Technologies (HURT)  Northrop Grumman-led DARPA program  Honeywell research on Planning and Control

3HONEYWELL - Proprietary SP-SAE-G&C-Oct 2005 HURT Provides On-Demand Collaborative RSTA for Obscured Targets Urban RSTA requires horizontal viewpoints and rapid reaction to cues and perceived threats The HURT control layer can make existing vehicles behave like a distributed robotic sensor Rapid “fly-by shooting” of concealed targets Persistent mobile sensors Overhead surveillance Agile urban navigation to see in portals Ground and subterranean recce Cueing sensors FUTURE UAV Side-looking RSTA Multilateral geolocation LOS comms relay chain

4HONEYWELL - Proprietary SP-SAE-G&C-Oct 2005 Demonstration results Sept 22, 2005 at Victorville, CA 4 small UAVs - 2 pointers - 1 raven - 1 Yamaha R-Max Honeywell – provided vehicle tracking and surveillance algorithms, integrated into the HURT ground control station The HURT system autonomously prioritized each request and directed the most suitable UAV to the location for a closer look while maintaining continuous broad-area surveillance by the other UAVs.