Symbiotic Simulation of Unmanned Aircraft Systems (UAS)

Slides:

Advertisements

Similar presentations

Unmanned Aircraft Systems EUROCONTROL ATM Integration

Advertisements

Optimal Path planning for (Unmanned) Autonomous Vehicles, UAVs Objective: The main aim of the project is to find out the optimal path or trajectory including.

Company Presentation.

Wide Maritime Area Airborne Surveillance Maritime Security and Needs for R&D Ostend - 22 Sept The WIMA ² S Project -

MicroCART Micro processor C ontrolled A erial R obotics T eam Abstract MicroCART is a group of EE/CprE students tasked with developing an autonomous helicopter.

Design Presentation Spring 2009 Andrew Erdman Chris Sande Taoran Li.

2014 Key Issues – National Security John C. Rose Deputy Director, Public Policy Region 6 Chair, National Security Sub-Committee.

Gilbert Islas Feb. 25, 2012 SYSM  A micro air vehicle (MAV) is a class of unmanned aerial vehicles (UAV). unmanned aerial vehicles  Size restrictions.

A discussion on.  Path Planning  Autonomous Underwater Vehicles  Adaptive Sampling  Mixed Integer Linear programming.

INNOCON Innovative solutions to the modern real time Arial surveillance challenges.

Contact: Irina Trefilova

Automatic Control & Systems Engineering Autonomous Systems Research Mini-UAV for Urban Environments Autonomous Control of Multi-UAV Platforms Future uninhabited.

Electrical and Computer Systems Engineering Postgraduate Student Research Forum 2001 Design and Development of a Distributed Avionics System for use in.

Softwalls: Preventing Aircraft from Entering Unauthorized Airspace Adam Cataldo Prof. Edward Lee Ian Mitchell Prof. Shankar Sastry CHESS Review May 8,

PROJECT ICARUS by Noah Michael, Student Project Manager & Dr. Nikos Mourtos, Faculty Project Manager AE 170A/B Course Instructor.

Sense & Avoid for UAV Systems

Autonomous Landing Hazard Avoidance Technology (ALHAT) Page 1 March 2008 Go for Lunar Landing Real-Time Imaging Technology for the Return to the Moon Dr.

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

Image Processing of Video on Unmanned Aircraft Video processing on-board Unmanned Aircraft Aims to develop image acquisition, processing and transmission.

Softwalls: Preventing Aircraft from Entering Unauthorized Airspace Adam Cataldo Prof. Edward Lee Prof. Ian Mitchell, UBC Prof. Shankar Sastry NASA JUP.

2002 MURI Minisymposium Cooperative Control of Distributed Autonomous Vehicles in Adversarial Environments 2002 MURI Minisymposium Ameesh Pandya Prof.

INTEGRATED PROGRAMME IN AERONAUTICAL ENGINEERING Coordinated Control, Integrated Control and Condition Monitoring in Uninhabited Air-Vehicles Ian Postlethwaite,

Advances in Networked Autonomous Vehicles: Technologies, Tools, and Case Studies João Borges de Sousa and Karl H. Johansson Tutorial Workshop at IEEE International.

Unmanned aerial systems, what they are and what is available? Professor Sandor M Veres University of Sheffield.

Introduction UAV is an Unmanned Aerial Vehicle Our goal is to setup two different plane configurations with Paparazzi autopilot and fly them autonomously.

Large Crowd Surveillance UAV

Federal Aviation Administration Unmanned Aircraft Systems in the National Airspace System Coming soon to airspace near you…..UAS-101 Presented At: Pecora.

A Novel Cache Architecture with Enhanced Performance and Security Zhenghong Wang and Ruby B. Lee.

GaTAC: A Scalable and Realistic Testbed for Multiagent Decision Making Ekhlas Sonu, Prashant Doshi Dept. of Computer Science University of Georgia Athens,

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

Built in Collision Avoidance for Unmanned Aircraft Systems (UAS)

Collision Avoidance for Unmanned Aircraft Systems (UAS)

Multiple UAV Collision Avoidance with Realistic UAV Models Joel George and Debasish Ghose Guidance, Control, and Decision Systems Laboratory (GCDSL) Department.

Requirements Engineering ments_analysis.

Carpe Occasio Technology SystemsSeize the Moment! Carpe OccasioTechnology Systems (COTS) Unmanned and Robotics Systems Interoperability Carpe Occasio Technology.

Introduction UAV is an Unmanned Aerial Vehicle Our goal is to setup two different plane configurations with Paparazzi autopilot and fly them autonomously.

COBXXXX EXPERIMENTAL FRAMEWORK FOR EVALUATION OF GUIDANCE AND CONTROL ALGORITHMS FOR UAVS Sérgio Ronaldo Barros dos Santos,

Personnel Nick BabcockAutonomous Systems Public Relations Kendrick Gibson Airship Technology Communications Jesse HuguetFlight Director Program Manager.

UK Aerial Robotics Team UK IDEA Laboratory Workforce Development: The UK Aerial Robotics Team and the PAX River Student UAV Competition Dale McClure (Matt.

Network UAV C3 Stage 1 Final Briefing Timothy X Brown University of Colorado at Boulder Interdisciplinary Telecommunications Program Electrical and Computer.

1 15 November 2004 CCSDS Security Architecture 15 th November 2004 Toulouse.

UAV’s: Scenarios Smart bombs  no need to return to base Reconnaissance/surveillance  types of sensors used, mission specifics (e.g. round trip, refuel.

Issues and Challenges for Co-operative UAV Missions Chris Halliday and Tony Dodd.

Toward Computing An Optimal Trajectory For An Environment- Oriented Unmanned Aerial Vehicle (UAV) Rodrigo Romero, Jerald Brady, Octavio Lerma, Vladik Kreinovich,

Aeronautics & Astronautics Autonomous Flight Systems Laboratory All slides and material copyright of University of Washington Autonomous Flight Systems.

11 STANAG 4586 Working Group Jan 2010 Mike Meakin.

Autonomous Polar Atmospheric Observations John J. Cassano University of Colorado.

1 SOARS Matt Edwards Arseny Dolgov John Shelton Johnny Jannetto Galina Dvorkina Nick Driver Eric Kohut Kevin Eberhart Self Organizing Aerial Reconnaissance.

Boeing-MIT Collaborative Time- Sensitive Targeting Project July 28, 2006 Stacey Scott, M. L. Cummings (PI) Humans and Automation Laboratory

THESIS COLLOQUIUM Collision avoidance and coalition formation of multiple unmanned aerial vehicles in high density traffic environments Joel George M 1.

SAS_05_Contingency_Lutz_Tal1 Contingency Software in Autonomous Systems Robyn Lutz, JPL/Caltech & ISU Doron Tal, USRA at NASA Ames Ann Patterson-Hine,

End of Semester 1 Presentation: November 28, 2007.

High Fidelity Simulation as a Route to Certification Autonomous Systems: Legal / Regulatory Aspects and V&V Workshop 22 nd February 2016 Dr. M. Jump.

Multiplication Find the missing value x __ = 32.

788.11J Presentation “Flock Control: Using Information Energy” Presented by Mukundan Sridharan.

Students: Yossi Turgeman Avi Deri Self-Stabilizing and Efficient Robust Uncertainty Management Instructor: Prof Michel Segal.

A dvancement in UAV T echnology Prof M S Prasad Institute of Space Science & Technology Amity University, Noida

Roadmap for the Application and Technology Development of UAVs in Japan The Public-Private Sector Conference on Improving the Environment for UAVs, 28th.

Intelligent Payload Module Concept Evolution

Terrain Reconstruction Method Based on Weighted Robust Linear Estimation Theory for Small Body Exploration Zhengshi Yu, Pingyuan Cui, and Shengying Zhu.

Distributed Vehicle Routing Approximation

Enabling Team Supervisory Control for Teams of Unmanned Vehicles

CanberraUAV Stephen Dade August 2016.

Washington Army National Guard

UAV Vision Landing Motivation Data Gathered Research Plan Background

Optimal UAV Flight Path Selection

Lockheed Martin Challenge

Distributed Sensing, Control, and Uncertainty

Stephen Dade UAV Outback Challenge.

Mixed Up Multiplication Challenge

Presentation transcript:

Symbiotic Simulation of Unmanned Aircraft Systems (UAS)

Auburn Objective “fly safely and efficiently, in a limited space, a fleet of autonomous UAS on a cooperative mission with terrestrial vehicles. ” Challenging Constraints –UAS must fly at constant speed –UAS must fly at the same altitude

Requirements 1.Control multiple UAS in a limited space 2.Standardized evaluation of collision avoidance algorithms designed/implemented by different teams 3.Control a mix of real and simulated UAS 4.Dynamic simulation model (Learn from real UAS) (in progress) 5.STANAG 4586 compliant (in progress)

Constraints on UAS Independent missions Upper limit on the altitude Lower limit on the altitude (stealth or regulations) Speed within a limited range Limited communications range for low end UAS

Software Architecture

Achievements Ground station Fly up to 16 UAVs in 1 km x 1km with no near miss Fly up to 8 UAVs in a 500 m x 500 m with no near miss

Ground Station (Screen)

In Progress STANAG 4596 compliance Symbiotic simulation End to end security Make UAS fault tolerant to GPS failures/jamming

Questions?