Cooperative Electronic Chaining using Small Unmanned Aircraft Cory Dixon & Eric W. Frew May 10, 2007

5/10/20072 Chaining with Mobile Vehicles Fuel range >> communication range for small vehicles Operational Range determined by the limiting value, communication range Limited size and power for antenna and electronics, e.g. no satellite or OTH communication capabilities Team of cooperative vehicles Can utilize ad hoc communication network or radio repeater Extends communication range using relay nodes Adds robustness to aircraft loss Chaining Solution Method Multivariable Extremum Seeking Control Form communication performance field from SNR Decentralized control to maximize end-to-end chain throughput Cooperative electronic chaining is the formation of a linked communication chain using a team of mobile vehicles, acting as communication relays in an ad hoc network, that maximizes the end-to-end throughput of the chain while allowing the end nodes of the chain to move independently in an unknown, dynamic environment. Long Range Sensing OTH Communications

5/10/20073 Robust Chaining and Extremum Seeking Position Based Robust SNR Based Typical ES Self-Exciting ES   Optimal Communication ChainExtremum Seeking Control x1x1 x2x2 x3x3 x4x4 x5x5 x6x6 S1S1 S2S2 S3S3 S4S4 S5S5 x x

5/10/20074 USFS/NASA: Small UAS Communication Repeater* Mission Objectives Provide real time voice relay (command channel) between ICP and fire line. Thermal imaging capabilities Near real time data relay capabilities of the thermal imagery Payload COTS Transcrypt Transpeater III radio portable repeater unit for single channel voice communications relay 2 USFS field radios set up to relay "command" channel communications in half duplex mode. Radios configured for 2 watts radiated power Thermal imaging with FLIR Micron microbolometer camera Mission Coordination UAS must be positioned so that it can see the ICP and the fire fighters. Frequency Management: As altitude increase the possibility for interference increases Airspace Coordination: The position of the UAS needs to be known by other aircraft. *Tom Zajkowski, Eleventh Biennial USDA Forest Service Remote Sensing Application Conference, Salt Lake City, Utah, April 24-28, 2006

5/10/20075 Overview Introduction Problem Setup and Related Work Decentralized Chaining Extremum Seeking Controller Simulation & Conclusion

5/10/20076 Vehicle Dynamics & UA Constraints Bicycle Kinematics Control inputs Vehicle cannot turn on itself Can be applied to wider class of nonholonomic vehicles over unicycle Unmanned Aircraft (UA) Assume vehicle has autopilot system controlling Attitude, altitude, airspeed, waypoint navigation Orbital controller (LGVF) UA Performance Constraints Constant, bounded speed: 0 < V MIN ≤ V O ≤ V MAX Steering input: |u| ≤ u MAX RF Sensor located close to CG (i.e. no forward boom) Bicycle Kinematics Aircraft Dynamics

5/10/20077  Communication environment is hard to predict in real world scenarios Communications Model Maintain communication link? Typically position based Received Power Signal-to-noise Ratio Shannon Channel Capacity C ≤ C MIN  Range ≤ Range MAX Communication Range Radio Environment Throughput vs. Range Position Based Environment can invalidate range based control Obstructions Localized noise sources Antenna patterns Performance Field C ≤ C MIN  S ≥ S MIN

5/10/20078 Standard ES Control Extremum Seeking Control Model free Actual mapping unknown Known to have an extremum Quadratic near extremum Gradient-based adaptive control Inject dither signal to linear system Demodulate output signal to estimate gradient Our approach: Use orbital motion of vehicle within environment to provide dither signal (self-excitation: Krstic and Wang, 2000) Add “virtual” center point dynamics to kinematic model Decentralized ES Treat as coupled multi-variable case Note: motion of a vehicle changes the field measured by the neighbor (tri-diagonal coupled system) Extremum Seeking control is to find a set point in a closed loop system that achieves an extremum of an unknown reference-to-output objective function.

5/10/20079 Vehicle Steering using ES: Kristic et al. Source Seeking with Nonholonomic Unicycle Without Position Measurement Part I: Tuning Forward Velocity Part II: Tuning Angular Velocity

5/10/ References & Related Work Communication and Control Connectivity & Limited Range Communications (Beard and McLain, 2003), (Spanos and Murray, 2004) Controlled mobility to Improve/Maintain Network Performance (Goldenberg et al., 2004), (Dixon and Frew, 2005), (Frew et al., 2006) – “Establishment and Maintenance of a Delay Tolerant Network through Decentralized Mobility Control” Vehicle Control in a Sampled Environment Cooperative Level Set Tracking (Boundary Tracking) (Hsieh et al., 2004), (Marthaler & Bertozzi, 2003) Cooperative Gradient Climbing (Bachmayer et al., 2002), (Ogren et al., 2004) Adaptive Sampling Utilizing Vehicle Motion (Fiorelli et al., 2003) (Krstic et al, 2006) – “Source Seeking with Nonholonomic Unicycle without Position Measurement - Part I: Tuning of Forward Velocity “ Extremum Seeking (Peak Seeking) (Ariyur and Krstic 2003) – “Real-Time Optimization by Extremum-Seeking Control” Multivariable (Ariyur and Krstic, 200?), (Rotea, 2000) Discrete Time (Krstic, 2002)

5/10/ Overview Introduction Problem Setup and Related Work Decentralized Chaining Extremum Seeking Controller Simulation & Conclusion

5/10/ Maximize end-to-end throughput Only looking at physical layer effects throughput => channel capacity Constant data stream with no buffering Maximum chain capacity is determined by minimum link capacity Shannon Channel Capacity Maximizing Chain Throughput   Radio Environment => The SNR provides a robust metric of communication performance capability and can be locally sampled by individual vehicles

5/10/ Maximin SNR Field xx Initial SetupOptimal Maximin Solution

5/10/ Decentralized Performance Map Performance Function Use the SNR of each neighbor link to form the feedback signal Can form different mappings to accomplish different communication goals

5/10/ Overview Introduction Problem Setup and Related Work Decentralized Chaining Electronic Chaining ES Controller Simulation & Conclusion

5/10/ LGVF Orbital Controller Lyapunov Vector Guidance Field Loiter circles at radius R o about a center point X cp Lyapunov Function Globally stable guidance field Heading tracking controller Guidance Vector Field Vehicle Trajectory

5/10/ Electronic Chaining for Nonholonomic Vehicles Dither Signal (self exciting) Provided by motion of the vehicle, within the field Demodulation signal is directly deirived from the vehicle motion Virtual Center Point Control motion of center point and allow LGVF controller to control UA steering Limit dynamics of center point so UA can track and maintain an orbit For stability V CP ≤ V MAX, good results are obtained when V CP < V MAX s.t.  ≤  MAX

5/10/ Extremum Seeking Analysis: Path Gradient Time derivative of Cost Function Path Gradient Low-pass filtering generates gradient control update Gradient estimate is always in direction of true gradient  Motion of Vehicle within Performance Field

5/10/ Linear Convergence Rate Bound Assumptions Vehicle speed is small compared to environment Initial error is very large Linear Convergence to optimal set-point Optimal Performance Metric Bounded Vehicle Motion Positional Error Convergence

5/10/ Overview Introduction Problem Setup and Related Work Decentralized Chaining Extremum Seeking Controller Simulation & Conclusion

5/10/ Multi-UA Simulation Radio Parameters Measured values obtained from AUGNet MNR K = 2350  = 3.2 Noise is 1/1000 th power of other nodes UA Parameters Ares UAV with Piccolo Autopilot V = 30 m/s Max Bank Angle = 30 deg => Max Turn Rate = 0.19 rad/sec

5/10/ Minimum SNR in Chain

5/10/ Bounded Convergence Rate

5/10/ Extension to Relaying for Multiple Nodes

5/10/ Conclusion Electronic Chaining Connect two disconnected network (radio) nodes Maximize end-to-end throughput Can be formulated as tracking the peak of a performance function, that is difficult to predict Self-exciting Extremum Seeking Model free, adaptive controller based on motion of vehicle SNR as Control Input Does not require any additional communication Is extensible from one node to many nodes Provides a robust measure of link quality and bandwidth Simulation Results Show that the ES controller can be used as decentralized controller Chain responds to dynamic environment Uknown, dynamic noise Unpredictable movement of end nodes Future work Obtain COA Experimental testing with Ares UAS and AUGNet b system

5/10/ Ares Measurement of RSSI using AUGNet MNR

5/10/ Questions and Comments are Welcomed! Thanks for Coming