PhD Comprehensive Presentation Addendum Slides April 15, 2005 Cory Dixon

1. Simultaneous Design and Analysis of Communications and Control for Collaborative Teams of Unmanned Aircraft PhD Comprehensive Presentation Addendum Slides April 15, 2005 Cory Dixon Research & Engineering Center for Unmanned Vehicles Aerospace Engineering Sciences University of Colorado

2. Are your taxes done yet? “The Department’s funding for UAV development has risen from just above $3 billion in the 1990s to over $12 billion for 2004 through 2009.” http://www.whitehouse.gov/omb/budget/fy2005/defense.html “President Bush's FY '06 budget request for DOD includes between $1.7 billion and $2 billion for UAVs …” Dyke Weatherington, head of the Pentagon's UAV Task Force http://aimpoints.hq.af.mil/display.cfm?id=1033

3. Overview • Introduction • Related Work • Mobile Ad Hoc Networks (MANETs) • Collaborative Control • Joint Design of Communication and Control • Research Application Problem • My Experience & Previous Research • AUGNet • Leashing • PIGF • Research Timeline & Summary

4. UAV Missions at CU AUGNet Leashing Problem Wild-land Fire Tornado Chasing Artic Climate & Sea-Ice

5. Intelligence, Surveillance, and Reconnaissance Missions • Small UAVs provide two fundamental capabilities • In-situ sensing • Atmospheric • EOI • Aerial communications • Relay nodes for other UAVs or ground units. • RF eavesdropping and source localization/detection • ISR missions are Dull, Dirty, Dangerous • Dull: ISR missions can be long, and uneventful • Dirty: UAVs can be sent to contaminated areas • Dangerous: No risk of human life, possibly expendable • Common research interests • UAV Teams • Collaborative teams can accomplish complex, multi-tasked missions • Teams are heterogeneous, utilizing capabilities of specialized vehicles • Networked Communications • Provides remote operator/scientist with timely, high-level data and control • Enables communication among teammates, as opposed to neighbors

6. UAV Team Control UAV Autopilots Low-level control of aircraft dynamics Provides high level interface to aircraft control Team & Task Assignment Allocation of resources Assignment of task to individuals within the team Consensus Problem Method of coming to agreement on action Mobile Ad-Hoc Networks MAC (Media Access Control) Channel access method Decentralized protocols suffer from hidden terminals Network Routing Provide address mechanism to users Determines routes between sources and destinations Route discovery Proactive On-Demand Probabilistic Quality of Service (QoS) Throughput Packet deliver End-to-end delays Team Control and Network Communication When the communication and control systems are independently designed, there is no guarantee that the combined system is capable of achieving the desired performance and capabilities provided by the individual systems.

7. Network Protocols Affect UAV Control & Operations Network Topology Point-to-Point Multi-Hop Quality of Service Bandwidth Time Delays Dropped Packets Mixed Traffic and Loads Real-Time (Local Control) Best-effort (Data Downlink) Control of UAV Affects Network Performance Mobility affects network topology Routes are time varying May not have a communication link QoS is impacted by physical link Distances between vehicles Antenna Gains and Pattern Neighbor communication Adds congestion to network Small packets with large headers Communication and Control are Interlinked Joint design and analysis of the control and communication systemsis required to utilize the full potential of collaborative UAV teams.

8. Overview • Introduction • Related Work • Mobile Ad Hoc Networks (MANETs) • Collaborative Control • Joint Design of Communication and Control • Research Application • My Experience and Previous Work • AUGNet • Leashing • PIGF • Research Timeline & Summary

9. Mobile Ad Hoc Networks • T. Camp • Effects of mobility on several routing protocols • Demonstrates different types of mobility models affect protocols in different ways, generally reducing performance • Woo and Culler • CSMA/CA with adaptive rate control scheme • protocol is based on concast network • X. Hong et. al • Multicast Landmark ad-hoc routing (M-LANMAR) • Adapted to coordinated motion scenarios (teams) • Qin and Kunz • Study impact of a realistic physical on several routing protocols • proposed new signal power thresholds for route discovery for AODV and DSR • MANETS (Wireless Sensor Networks) • MAC: IEEE 802.11, TDMA, FDMA, CDMA, Dual-Tone • Routing: DSR, AODV, DSDV, IGF, GR, ZRP • QoS: IEEE 802.11e, PSFQ Node Mobility Network Performance

10. UAV Team Control • T. W. McLain & R. W. Beard • Team Coordination and Consensus • Coordination Variable • Coordination functions • Communication Constraints • Cooperative search with collision avoidance • Coordinated target assignment • R. M. Murray et al. • Graph theoretic approach • Show link from graph Laplacian (topology) and the consensus problem (information flow) • They prove a separation principle for formation stability • Stability of information flow graph • Stability of individual vehicle control • Introduce geometric connectivity robustness • Optimal Control & Estimation over Unreliable Communication Links • Imer et al.: Finite Horizon Optimal Control over TCP & UDP • Pollini et al.: Robustness to communication failures in formation flight • B. Sinopoli et al.: Kalman filtering with intermittent observations • P. J. Seiler: String stability on infinite vehicle platoons Network Performance Cooperative Control

11. Research Motivation Need to close the loop! Combining ad hoc wireless networks with control of unmanned aircraft teams is not well understood at this time and there are two fundamental problems with current systems due to the fact that the communication and control systems are designed independently. Network Performance Cooperative Control Network Performance Cooperative Control Network Topology Node Mobility Network Topology Node Mobility • Communication systems that are currently used are not well suited for the mixed traffic types required by UAV teams and the highly dynamic network topology of UAV teams. • Collaborative team controllers only consider limited, possibly incorrect, information about the underlying network communications and simply ignore the fact that the motion of the vehicle directly affects the performance of the network, and thus the performance of the control system.

12. Overview • Introduction • Related Work • Mobile Ad Hoc Networks (MANETs) • Collaborative Control • Joint Design of Communication and Control • Research Application Problem • My Experience & Previous Research • AUGNet • Leashing • PIGF • Research Timeline & Summary

13. Research Thesis Joint design and analysis of the control and communication systemsis required to utilize the full potential of collaborative UAV teams. • Communication should be developed specifically for UAV team control in ISR missions • Provide balanced traffic loading • Hard real-time requirements from control system • Soft real-time and bandwidth guarantees for video and VOIP • Low priority data such as atmospheric measurements and UAV health & status • Adaptable and robust to UAV mobility • Mobility is expected and possibly predictable • Takes advantage of UAV team control • UAV Team hierarchy should be reflected in network routing • Knowledge of neighbor position is fundamental to network and control system • Control system should consider communication performance as a controlled objective • Network connectedness is generally required • Provides situational awareness from all UAVs to control center • Enables information sharing among all team members • Extended to include ground network connectedness in addition to team • Network performance can be improved by UAV motion • Proper UAV dispersion will provide reliable link qualities and network coverage • UAV can find remote networks, and act as a data mule Example: In UAV team control, knowledge of neighbor positions is so fundamental that it should be considered at the lowest level of the communication system.

14. Co-Design Framework A proposed hierarchical framework in which network and control design can be considered simultaneously.

15. Research Goals & Contributions • Develop theory for using mobility control to affect network performance • Position based control is not enough to guarantee a communications link • Utilize communication-based information for control • Signal-to-Noise Ratio (SNR) • Neighbor connectedness • Balance communication performance with other mission objectives • Development of theory • Co-design and analysis of communications and UAV team control • Introduction of a hierarchical framework for co-design • Development of simulation environment for co-analysis • Development and testing of a jointly designed system • Develop MANET protocol and UAV team control scheme • Test on a physical system in addition to software simulation

16. Related WorkGoldenberg et al. • “The energy optimal positions of relay nodes must lie entirely on the line between the source and the destination.” • Introduce a mobility control scheme for network performance • Decentralized control scheme • Based solely on neighbor positions • Shown communication energy can be reduced by utilizing mobility control • Introduce connectedness constraints on mobility • Constrained Mobility • Unconstrained Mobility • Study energy performance on • Single unicast flow • Multiple unicast flows • Many-to-one concast flows • Research Differences • Based on GPS position • Treats nodes as slow point masses (speed < 0.1 m/s) • Do not analyze network protocols, only use GR

17. Related WorkM. Gerla and X. Hong • “Internet-in-the-sky” • Multimedia Intelligent Network of Unattended Mobile Agents (Minuteman) • Landmark ad-hoc routing (LANMAR) • Mobile Backbone Network (MBN) • Distributed Information Database • Adapted LANMAR • Exploited coordinated movements, e.g. teams • Included a multicast framework (M-LANMAR) • Scalable QoS with backpressure routing • Research Differences • Do not control mobility • Designed independent of vehicle • Only present routing protocol

18. Related WorkX. Liu and A. J. Goldsmith • X. Liu’s Research: Joint Design of Control and Communications • Previous Work • Kalman filter in presence of packet losses • String stability with communication delays • MAC & Link Analysis on Control • Proposed a cross-layer framework to jointly design all the layers of the network to deliver the best end-to-end control performance • Research Differences • No node mobility • Small static networks => TDMA is reasonable • Network control is application

19. Overview • Introduction • Related Work • Mobile Ad Hoc Networks (MANETs) • Collaborative Control • Joint Design of Communication and Control • Research Application Problem • My Experience & Previous Research • AUGNet • Leashing • PIGF • Research Timeline & Summary

20. Research Application:Wildland Firefighting • Situational awareness is a matter of life and death • UAVs can provide reliable communication links to ground crew • In-situ atmospheric measurements provide microscale meteorology • Aerial imagery provides an unmatched level of awareness as compared to any other sensor • Typical deployment • Small / Micro Aerial Vehicles • 1-10 UAVs for Type II Fires (county resources) • 10-50 UAVs for Type I Fires (Federal & State resources) • Cary a variety of sensors • Atmospheric • EOI Sensors • Ground Units • Control center • Mobile firefighting teams • Ground wireless sensor networks • Mission Goals • Provide guaranteed communication from control center to all ground crew • Collect imagery for immediate downlink to control center • Combine microscale atmospheric measurements to generate a local weather map • Deploy ground sensors and gather information from them • What improvements / capabilities will my system provide • Team performance will not be affected by video/VOIP data streams • Non-positional control will provide robust communication links to ground units • Communication chains can be established to enable long-range sensing

21. Application Demonstration • AUGNet testbed • Emulate Wildland Fire scenario on Table Mountain • A Four plane Team • 2 equipped with cameras • All have atmospheric and communication equipment • Single control center • Multiple ground networks • Mobile crew • Static ground sensors • Demonstrate UAV team control • Team and task assignment • Control based on communications => leashing • Collect network performance in a physical environment • Utilize AUGNet tools to collect, monitor and store data • Show that the designed system performs better than was obtained by AUGNet • Distributed Macro-sensor (DMS) Testbed • Show system is adaptable • Can be applied to unmanned ground vehicles • Can scale by demonstrating on 20-50 vehicles • Only if time allows as not primary thrust Table Mountain National Radio Quiet Zone

22. Overview • Introduction • Related Work • Mobile Ad Hoc Networks (MANETs) • Collaborative Control • Joint Design of Communication and Control • Research Application Problem • My Experience & Previous Research • AUGNet • Leashing • PIGF • Research Timeline & Summary

23. AUGNet:Ad Hoc UAV Ground Network • Objective Study ad hoc network behavior on a full-scale hardware test bed with mobile ground and air-vehicle nodes. • Full-Scale Test Bed • Fixed nodes • Mobile ground nodes • UAV mounted nodes • COTS Components • Single board computer • Linux OS • 802.11b PC Card • GPS

24. 5-hp Engine Fuel Tank Payload Bay with MNR AUGNet Ares

25. AUGNet: Table Mountain Field Site

26. AUGNet 3-Plane Test

27. Overview • Introduction • Related Work • Mobile Ad Hoc Networks (MANETs) • Collaborative Control • Joint Design of Communication and Control • Research Application Problem • My Experience & Previous Research • AUGNet • Leashing • PIGF • Research Timeline & Summary

28. Leashing Problem The leashing problem is to provide a communication link between two disconnected nodes (networks) using only information available from the established communication links. Leashed chain for long-range sensing

29. Use the signal-to-noise ratio (SNR) to determine control input. Leashing Controllers Constant SNR Orbit Maximize & Equalize SNR Turning Rate Control Orbit Center Point Control

30. Maximum-Equal SNR

31. Overview • Introduction • Related Work • Mobile Ad Hoc Networks (MANETs) • Collaborative Control • Joint Design of Communication and Control • Research Application Problem • My Experience & Previous Research • AUGNet • Leashing • PIGF • Research Timeline & Summary

32. Prioritized Implicit Geographic Forwarding PIGF: A Real-Time Ad-Hoc Network Protocol for Micro-Air Vehicle Swarms • Geographic Forwarding • UAVs already carry GPS • UAVs need to know neighbor’s positions for team collaboration and collision avoidance • Minimum Resources Available • Computational power • Power / Range • Real-Time • End-to-End deadline • Single hop deadline • Based on existing protocols • IGF • SPEED • RAP

33. Implicit Geographic Forwarding • IGF • MAC Layer routing • On demand routing without routing tables • Based on geographic position • Open RTS (ORTS) • Candidate nodes compete to participate • Contains geographic destination of message • CTS Response Time • Metrics • Increased distance toward destination • Energy Remaining • Can include any other measurable, local metric CTS ORTS DATA d s

34. SPEED & RAP • SPEED provides real-time scheduling between nodes. It decides the candidate node to forward the packet to. • RAP provides real-time scheduling within a single node. It decides the candidate packet to send out. • They talk about real-time in different spaces: between nodes vs. within a node. SPEED RAP

35. Overview • Introduction • Related Work • Mobile Ad Hoc Networks (MANETs) • Collaborative Control • Joint Design of Communication and Control • Research Application Problem • My Experience & Previous Research • AUGNet • Leashing • PIGF • Research Timeline & Summary

36. Future Work & Milestones • Refine Metrics • Efficiency • Robustness • Adaptability • Integrate Comm & Control Simulation • Adapt current Matlab simulation to cooperative control • Integrate and simulate PIGF in NS2 • Link Matlab & NS2 Simulations to enable co-analysis and design • Leashing • Use communication as control primitive • Formulate team control problem • Communication Impact on Control • How does communication affect control? • Control Impact on Communication Network • How does UAV motion affect comm network? • Utilize graph theoretical tools • Field Deployment & Tests • Implement comm and control on AUGNet platform • Deploy on Table Mountain test site, using AUGNet infrastructure to collect data

37. Summary Simultaneous design of the control and communication systemsis a significant research problem and is required to realize the maximum performance and capabilities of UAV teams. • Research Goals and Contributions • Develop an understanding of using mobility control to affect network performance • Utilize communication-based information for control • Balance communication performance with other mission objectives • Co-design and analysis of communications and UAV team control • Introduction of a hierarchical framework • Development of simulation environment for co-analysis • Development of theory to close the performance loop • Experimental demonstration • Utilize AUGNet testbed for wildland fire scenario • Distributed Macro-sensor (DMS) Testbed to show adaptability and scalability • Initial Experience and Research • Aerosonde • AUGNet • PIGF • Leashing

38. Questions and Comments are Welcomed Thanks for Coming Cory.Dixon@Colorado.EDU

39. Mixed traffic communication with • Multi-objective controller with communication based input