Motivation and Vision Statement
Goals and Assumptions
Development Process
Conclusions
Acknowledgements
References
NEST Team Goals -
Develop an independent Air Operations Control (AOC) Center for unmanned aerial vehicles (UAVs)
Manage upwards of 500 to 1000 UAVs in a 10 mile radius with 1 to 10 operators
Provide operators with UAV details
Ability to give navigational commands to UAVs
Design an experiment to evaluate our solution
Construct a working prototype by using software development process learned in COMP 380/490
A Journey Into the Emotions of Software Developers
Network of Electronic Self-Navigating Transports Presentation (NEST)
1. SYSTEMS ENGINEERING
RESEARCH LABORATORY
Network of Electronic
Self-Navigating
Transports
Sean Buckley, Varatep Buranintu, Dragos Guta, Jeffrey Limbacher
Yun Kim, Pinar Sayim, David Wu
May 8th, 2015, COMP 491 Senior Design, CSUN Department of
Computer Science
Customer: Dr. Ho, Systems Engineering Research Laboratory
2. Agenda
1. Motivation and Vision Statement
2. Goals and Assumptions
3. Development Process
4. Conclusions
5. Acknowledgements
6. References
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3. Motivation
• Growing interest in drones
• Many different practical applications
• Google Wings and Amazon Prime
Air
• Customer: Dr. Ho wants large scale
unmanned aircraft system (UAS)
test bed
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4. UAS Challenges
• Safety
• Avoiding people
• Avoiding obstacles
• Managing a large fleet
• Handling off-nominal situations
• Providing pertinent information to
operators
• Operator-automation interaction
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5. Vision Statement
• Given that
• UAS automation will enable fully autonomous flight
• Humans must be able to intervene in off-nominal situations
• Current control systems are not viable
• NEST will
• Fill the gap
• Provide automation and tools for operators
• Allow a small group to manage a large set of vehicles
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7. NEST Team Goals
1. Develop an independent Air Operations Control (AOC) Center
for unmanned air vehicles (UAVs)
2. Manage upwards of 500 to 1000 UAVs in a 10 mile radius with
1 to 10 operators
3. Provide operators with UAV details
4. Ability to give navigational commands to UAVs
5. Design an experiment to evaluate our solution
6. Construct a working prototype by using software development
process learned in COMP 380/490
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8. Requirements Research
• Similar Systems
• Air Operations Centers
• Dispatch Systems
• Research broken into domains for areas of interest
• NASA & Military – AOCs, LGDs, maps with overlays, multiple displays at once
• Psychology – managing large number of things
• Gaming – Information displays, unit control
• Commercial – Vehicle monitoring, scheduling
• Meeting with customer
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9. Assumptions
• FAA
• Regulations freely permit commercial UAV flights
• Flight of UAVs over medium- to highly-populated areas allowed
• Integration of commercial UAV into National Airspace System (NAS)
• Highly automated UAVs
• Object avoidance
• Ground avoidance
• Course correction
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12. Technology choices
• Microsoft’s ASP.NET MVC using C# (Visual Studio)
• SignalR
• HTML, JavaScript, CSS
• AngularJS
• Google Maps
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13. Team Member Roles and Responsibilities
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14. Development Process
• Scrum
• One week sprints
• Gather user stories from customer, Dr. Ho
• Populate backlog
• Assign tasks
• Continuous Integration
• Meetings with industry professionals
• Team Bonding
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21. Experiment
• Goals
• A total of 3 operators running through the software
• The experiment was between two trials:
• 50 UAVs and 100 UAVs
• NASA Task Load Index assessed the overall workload of the operators for both
• Results
• Participants found value in all the displays
• Participants were able to address problems
• However, addressing problems took too much time
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23. What We Accomplished
• Command and Control of vehicles
• Multiple displays for providing information to the operator
• Map displays vehicle position and “bird’s eye view” of the operational area
• Display for vehicle-specific information
• Show delivery status
• Displays support showing large amounts of UAVs
• Events bring operator attention to issues
• System for UAV assignment based on contingency events
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24. Future Work
• Look into operator’s influence in safety
• Reactive system versus predictive system
• What automation will realistically be in the aircraft or system
• How does this influence how the operator interacts with the rest of the system
• What information to present to the operator
• Information is context sensitive
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25. What We Learned
• Spend more time on system design
• Communication is key
• Important to adapt the process to the team
• Changed sprint length and created subgroups
• Shared knowledge over specialists
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26. Comparing Semester One and Two
Semester 1 Semester 2
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27. Acknowledgements
• Dr. Nhut Ho – CSUN, Mechanical Engineering, Customer
• Dr. Walter Johnson – NASA Ames Research Center, Human Systems
Integration
• Cody Evans – Airline Operations Dispatcher
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28. References
• Amazon Prime Air, http://www.amazon.com/b?node=80377200114
• Drone Industry Expected to Grow to $11 Billion by 2024,
http://www.livescience.com/47071-drone-industry-spending-report.html
• FAA Unmanned Aircraft Systems, https://www.faa.gov/uas/
• NASA Unmanned Aerial System Traffic Management (UTM),
http://www.aviationsystemsdivision.arc.nasa.gov/utm/index.shtml
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