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The Research and Engineering Center for Unmanned Vehicles Prof. Eric W. Frew October 7, 2009 Active Sensing by Unmanned Ai...
Motivation <ul><li>Motivation: Relay Communication Networks and In-Situ Atmospheric Sensing </li></ul>The goal of this wor...
Realistic Communication Environment <ul><li>Wireless communication is not easily described by a set of “links” </li></ul><...
Outline <ul><li>Introduction </li></ul><ul><li>Expected Information Rate </li></ul><ul><li>Decentralized Chaining </li></u...
Information-theoretic Robot Motion Planning <ul><li>Determine robot motion to optimize information gain over a noisy commu...
Link and Network Models: Functions of SINR <ul><li>Receive-and-Retransmit Network </li></ul><ul><ul><li>Packet based netwo...
The Expected Information Use the Extended Information Filter (EIF) framework to derive a prediction of the norm of the inf...
Bearing and Range (BR) Tracking:  2 robots, 1 moving target, No prior information <ul><li>Two robots share information to ...
Bearing and Range (BR) Tracking:  2 robots, 1 moving target, No prior information Radio Decay Exponential    = 2 Communic...
Communication Aware Tracking <ul><li>2 UA sensing with base station performing fusion and estimation. </li></ul><ul><li>Se...
Perfect Communication <ul><li>This run assumed perfect communication ( β i,j  = 1) </li></ul><ul><li>Note that the 2 senso...
Imperfect Communication <ul><li>This run is with limited communication </li></ul><ul><li>UA1 naturally becomes a relay onc...
Comparison <ul><li>The table compares the percentage of measurement packets getting through when communication is and is n...
Outline <ul><li>Introduction </li></ul><ul><li>Expected Information Rate </li></ul><ul><li>Decentralized Chaining </li></u...
Electronic Chaining <ul><li>Position Based  </li></ul>x 1 x 2 x 3 x 4 x 5 x 6 x The Problem Robust SNR Based The Solution ...
Localized Performance Functions <ul><li>Receive-and-retransmit </li></ul><ul><li>Flow-Pipe </li></ul><ul><li>Selection of ...
Phase Space:  RR Network with Shannon Capacity    = 0.1    = 2    = 100    = 10 Critical points due to fixed step size...
Simulation Movie <ul><li>2D simulation of a receive-and-retransmit network using Shannon channel capacity.  </li></ul><ul>...
Control without Gradient Knowledge <ul><li>There is a fundamental problem: </li></ul><ul><li>The gradients of the SNR fiel...
Stochastic Approximation <ul><li>Stochastic Approximation </li></ul><ul><li>Gradient and value estimates using noisy measu...
Stochastic Approximation <ul><li>Stochastic Approximation </li></ul><ul><li>Gradient and value estimates using noisy measu...
Stochastic Approximation <ul><li>Stochastic Approximation </li></ul><ul><li>Gradient and value estimates using noisy measu...
Perturbations due to  Motion of Vehicle <ul><li>“ For a UA, this looks like …” </li></ul><ul><li>Circular path about a mov...
Electronic Chaining for Nonholonomic Vehicles Extremum Seeking  control finds a set point in a closed loop system that ach...
One-Point Estimator: f = r
Averaging Two Point Estimator: f = max(x,y)
Decentralized 5-Node Chain with Noise
Leashed Chain with Noise <ul><li>Radio Parameters </li></ul><ul><li>Measured values obtained from AUGNet MNR </li></ul><ul...
Outline <ul><li>Introduction </li></ul><ul><li>Expected Information Rate </li></ul><ul><li>Decentralized Chaining </li></u...
Ad hoc UAS Ground Network (AUGNet) Timothy Brown, Brian Argrow, Eric Frew, Cory Dixon, Daniel Henkel, Jack Elston, and Har...
Networked UAS C3 Eric W. Frew, Cory Dixon, Jack Elston, Brian Argrow, and Timothy X. Brown. “Networked Communication, Comm...
Heterogeneous Unmanned Aircraft System Heterogeneous UAS that combines the CU AUGNet and CU MAV Sensor Flock. Jack Elston,...
Large Fleet with Multiple FAA COAs  <ul><li>We fly at the Table Mountain Field Site in Boulder, CO under FAA Certificates ...
VORTEX 2 COA Status Blue: Committed Yellow: Validated Green: Active 61 commits 2/12/09 2008 WSA-51; 2009 WSA-82
Additional Capabilities Cold Weather Operation Autonomous Takeoff and Landing Pilot’s Eye View
AUGNet: Throughput vs. Range <ul><li>UAV Increases Communication Range </li></ul><ul><li>UAV Performance is More Erratic <...
Radio Frequency Data <ul><li>Data collected at Table Mountain facility </li></ul>
Chaining = RF Source Estimation <ul><li>Second chaining experiment </li></ul><ul><ul><li>1 UA to seek peak of 1 RF source ...
RF Source Estimation: UKF = DMC <ul><li>For comparison can also estimate node position </li></ul><ul><li>An Unscented Kalm...
Latest Flight Results with NexSTAR-1 <ul><li>Electronic Chaining </li></ul><ul><ul><li>One NexSTAR </li></ul></ul><ul><ul>...
Looking Ahead… <ul><li>Introduction </li></ul><ul><li>Expected Information Rate </li></ul><ul><li>Decentralized Chaining <...
The Research and Engineering Center for Unmanned Vehicles Prof. Eric W. Frew http://recuv.colorado.edu/~frew [email_addres...
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November 9, Planning and Control of Unmanned Aircraft Systems in Realistic Communication Environments

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November 9, Planning and Control of Unmanned Aircraft Systems in Realistic Communication Environments

  1. 1. The Research and Engineering Center for Unmanned Vehicles Prof. Eric W. Frew October 7, 2009 Active Sensing by Unmanned Aircraft Systems in Realistic Communication Environments Cory Dixon, Jack Elston, Maciej Stachura
  2. 2. Motivation <ul><li>Motivation: Relay Communication Networks and In-Situ Atmospheric Sensing </li></ul>The goal of this work is to develop a planning and control framework for multi-vehicle networks (UA, UGV, etc) in realistic communication environments that moves beyond simple geometric and graph-based representations STORM WILDFIRE POLAR
  3. 3. Realistic Communication Environment <ul><li>Wireless communication is not easily described by a set of “links” </li></ul><ul><ul><li>Self-interference (collision avoidance), noise </li></ul></ul><ul><li>Distance dependency on power and throughput </li></ul><ul><li>Fading (noise) effects are hard to predict </li></ul><ul><ul><li>Non-uniform RSSI field </li></ul></ul>Throughput vs. Range Communication Range Comm. Typical Disk (Graph) Communication Model No Comm.
  4. 4. Outline <ul><li>Introduction </li></ul><ul><li>Expected Information Rate </li></ul><ul><li>Decentralized Chaining </li></ul><ul><li>Experimental Systems </li></ul>Robot Sensor Network M S C B B M,C M,S,C M,C M,S,C M,S,C
  5. 5. Information-theoretic Robot Motion Planning <ul><li>Determine robot motion to optimize information gain over a noisy communication link (or network of noisy links) </li></ul><ul><li>Can be used as initial condition for model-free adaptation to mitigate noise and interference </li></ul>Objective : Develop an information-theoretic framework for the robot motion planning that integrates sensing, communication, and actuation into a single approach.
  6. 6. Link and Network Models: Functions of SINR <ul><li>Receive-and-Retransmit Network </li></ul><ul><ul><li>Packet based networks, e.g. 802.11, 802.15 </li></ul></ul><ul><ul><li>Single RF frequency </li></ul></ul><ul><ul><li>Only one node can transmit at any given instant </li></ul></ul>1 2 3 4 5 6 c 21 c 65 1 2 3 4 5 6 c 21 c 65 <ul><li>Repeater Network (a.k.a. Flow-Pipe) </li></ul><ul><ul><li>Used by emergency personnel </li></ul></ul><ul><ul><li>Analog repeater, i.e. only repeats the physical signal, does not decode </li></ul></ul><ul><ul><li>Unique frequency for every communication link </li></ul></ul>Rappaport Goodput Models Shannon Capacity Probabilistic Erasure Channel SINR P receive 0 1 S low S high distance Communication Range Shannon Capacity
  7. 7. The Expected Information Use the Extended Information Filter (EIF) framework to derive a prediction of the norm of the information matrix which is the inverse of the estimate error covariance matrix. Assumes multiple independent sensors Probability of successful transmission from sensor i to base j. Throughput of transmission from sensor i to base j. Eric W. Frew. “Information-Theoretic Integration of Sensing and Communication for Active Robot Networks.” Invited to special issue of Mobile Networks and Applications, 14(3):267-280 June 2009 Maciej Stachura, Anthony Carfang, and Eric W. Frew. “Cooperative Target Tracking with a Communication Limited Active Sensor Network.” International Workshop on Robotic Wireless Sensor Networks , Marina Del Rey, CA, June 2009.
  8. 8. Bearing and Range (BR) Tracking: 2 robots, 1 moving target, No prior information <ul><li>Two robots share information to localize a stationary target </li></ul><ul><ul><li>Minimum allowable range to target </li></ul></ul><ul><ul><li>Well known optima for case of perfect communication </li></ul></ul>
  9. 9. Bearing and Range (BR) Tracking: 2 robots, 1 moving target, No prior information Radio Decay Exponential  = 2 Communication decreases as separation increases Information increases as distance increases Radio Decay Exponential  = 4
  10. 10. Communication Aware Tracking <ul><li>2 UA sensing with base station performing fusion and estimation. </li></ul><ul><li>Sensors have 8 o error and operate at 2Hz. </li></ul><ul><li>Run time is 90s with 0.5s step size. </li></ul><ul><li>β i,j is based on empirical data. </li></ul><ul><li>Initial position error of approximately 420m. </li></ul><ul><li>Optimization is done in a receding horizon framework. </li></ul><ul><li>T p is the planning horizon and T c is the control horizon, which is some fraction of T p . </li></ul>
  11. 11. Perfect Communication <ul><li>This run assumed perfect communication ( β i,j = 1) </li></ul><ul><li>Note that the 2 sensors together orbit the target with an angular offset. </li></ul><ul><li>The black dots show the UKF estimated position based on 2 measurements. </li></ul>
  12. 12. Imperfect Communication <ul><li>This run is with limited communication </li></ul><ul><li>UA1 naturally becomes a relay once the target is too far for direct comms. </li></ul><ul><li>There are several instances where only 1 or 0 measurements get through </li></ul>
  13. 13. Comparison <ul><li>The table compares the percentage of measurement packets getting through when communication is and is not considered in the optimization. As expected more get through when it is considered. </li></ul><ul><li>The algorithm with communication has a 12% percent improvement in position RMS because of these extra measurements. </li></ul>2-D Position RMS Comm. In Planning No Comm. In Planning UA1 85.0% 67.2% UA2 85.6% 66.1% Both 73.9% 52.2% None 3.3% 18.9% Perfect Comm No Comm in Planning Comm in Planning 3.77 m 4.50 m 3.95 m
  14. 14. Outline <ul><li>Introduction </li></ul><ul><li>Expected Information Rate </li></ul><ul><li>Decentralized Chaining </li></ul><ul><li>Experimental Systems </li></ul>Robot Sensor Network M S C B B M,C M,S,C M,C M,S,C M,S,C
  15. 15. Electronic Chaining <ul><li>Position Based </li></ul>x 1 x 2 x 3 x 4 x 5 x 6 x The Problem Robust SNR Based The Solution Objective : Use mobility of relay nodes to maximize the directed capacity of a cascaded wireless relay network in a noisy RF environment using a gradient-based decentralized mobility controller. S 1 S 2 S 3 S 4 S 5 x
  16. 16. Localized Performance Functions <ul><li>Receive-and-retransmit </li></ul><ul><li>Flow-Pipe </li></ul><ul><li>Selection of J i : </li></ul><ul><ul><li>Critical points of J i must correspond to an optimal network chain configuration. </li></ul></ul><ul><ul><li>Two available methods </li></ul></ul><ul><ul><ul><li>Find a global performance function with a spatially distributed gradient mapping. </li></ul></ul></ul><ul><ul><ul><li>Find a meaningful local objective function which drive the agents to the global objective. </li></ul></ul></ul>Local 3-node Network x i-1 x i S i D C i,i-1 x i+1 C i+1,i Critical Point Proof: Critical Point Proof: Cory Dixon and Eric W. Frew. “Maintaining Optimal Communication Chains in Robotic Sensor Networks using Mobility Control.” Invited to special issue of Mobile Networks and Applications (MONET) , 14(3):281-291 June 2009
  17. 17. Phase Space: RR Network with Shannon Capacity  = 0.1  = 2  = 100  = 10 Critical points due to fixed step size of simulation. Location of Global Max Location local maximum Location of least local maximum
  18. 18. Simulation Movie <ul><li>2D simulation of a receive-and-retransmit network using Shannon channel capacity. </li></ul><ul><ul><li>t = 250s: a noise source is introduced </li></ul></ul><ul><ul><li>t = 500s: the number of relays changes from 3 to 2. </li></ul></ul><ul><ul><li>t = 600s: the destination starts to move </li></ul></ul><ul><li>All control is decentralized </li></ul>
  19. 19. Control without Gradient Knowledge <ul><li>There is a fundamental problem: </li></ul><ul><li>The gradients of the SNR field can not be accurately predicted by a vehicle in a dynamic, unknown, environment! </li></ul><ul><li>The solution is in Stochastic Approximation and Extremum Seeking methods! </li></ul>
  20. 20. Stochastic Approximation <ul><li>Stochastic Approximation </li></ul><ul><li>Gradient and value estimates using noisy measurements </li></ul><ul><ul><li>More samples reduce stochastic noise </li></ul></ul>
  21. 21. Stochastic Approximation <ul><li>Stochastic Approximation </li></ul><ul><li>Gradient and value estimates using noisy measurements </li></ul><ul><ul><li>More samples reduce stochastic noise </li></ul></ul>
  22. 22. Stochastic Approximation <ul><li>Stochastic Approximation </li></ul><ul><li>Gradient and value estimates using noisy measurements </li></ul><ul><ul><li>More samples reduce stochastic noise </li></ul></ul>
  23. 23. Perturbations due to Motion of Vehicle <ul><li>“ For a UA, this looks like …” </li></ul><ul><li>Circular path about a moving control point </li></ul><ul><ul><li>Move control point using SA methods </li></ul></ul><ul><ul><li>Fly vehicle around control point to generate perturbations </li></ul></ul><ul><ul><li>Make many measurements to reduce errors from measurement noise </li></ul></ul>
  24. 24. Electronic Chaining for Nonholonomic Vehicles Extremum Seeking control finds a set point in a closed loop system that achieves an extremum of an unknown reference-to-output objective function. <ul><li>Extremum Seeking Control </li></ul><ul><ul><li>Model free </li></ul></ul><ul><ul><li>Gradient-based adaptive control </li></ul></ul><ul><li>Our approach: </li></ul><ul><ul><li>Use orbital motion of vehicle within environment to provide dither signal (self-excitation) </li></ul></ul><ul><ul><li>Add “virtual” center point dynamics to kinematic model </li></ul></ul><ul><li>Decentralized ES </li></ul><ul><ul><li>Treat as coupled multi-variable case </li></ul></ul>Cory Dixon and Eric W. Frew. “Decentralized Extremum-Seeking Control of Nonholonomic Vehicles to Form a Communication Chain.” Advances in Cooperative Control and Optimization . Lecture Notes in Computer Science, Vol. 369, Michael J. Hirsch, Panos Pardalos, Robert Murphey, and Don Grundel, Eds. Springer-Verlag, Nov. 2007.
  25. 25. One-Point Estimator: f = r
  26. 26. Averaging Two Point Estimator: f = max(x,y)
  27. 27. Decentralized 5-Node Chain with Noise
  28. 28. Leashed Chain with Noise <ul><li>Radio Parameters </li></ul><ul><li>Measured values obtained from AUGNet MNR </li></ul><ul><ul><li>K = 2350 </li></ul></ul><ul><ul><li> = 3.2 </li></ul></ul><ul><ul><li>Noise is 1/1000 th power of other nodes </li></ul></ul><ul><li>UA Parameters </li></ul><ul><li>Ares UAV with Piccolo Autopilot </li></ul><ul><ul><li>V = 30 m/s </li></ul></ul><ul><ul><li>Max Bank Angle = 30 deg </li></ul></ul><ul><ul><ul><li>=> Max Turn Rate = 0.19 rad/sec </li></ul></ul></ul>
  29. 29. Outline <ul><li>Introduction </li></ul><ul><li>Expected Information Rate </li></ul><ul><li>Decentralized Chaining </li></ul><ul><li>Experimental Systems </li></ul>Robot Sensor Network M S C B B M,C M,S,C M,C M,S,C M,S,C
  30. 30. Ad hoc UAS Ground Network (AUGNet) Timothy Brown, Brian Argrow, Eric Frew, Cory Dixon, Daniel Henkel, Jack Elston, and Harvey Gates. “Experiments Using Small Unmanned Aircraft to Augment a Mobile Ad Hoc Network.” Emerging Technologies in Wireless LANs: Theory, Design, and Deployment , Edited by Benny Bing, Ch. 28, p. 123-145, 2007. UAV Nodes Mobile Nodes Meshed Radio Network Fixed Site 1 Fixed Site 2 Test Bed Gateway and Test Range IP Router Range Network Table Mountain Field Site University of Colorado Monitor Server Remote Monitor Internet
  31. 31. Networked UAS C3 Eric W. Frew, Cory Dixon, Jack Elston, Brian Argrow, and Timothy X. Brown. “Networked Communication, Command, and Control of an Unmanned Aircraft System. “ AIAA Journal of Aerospace Computing, Information, and Communication , 5(4):84–107, 2008. Eric W. Frew, Cory Dixon*, Jack Elston*, Brian Argrow, and Timothy X. Brown. “Networked Communication, Command, and Control of an Unmanned Aircraft System. “ AIAA Journal of Aerospace Computing, Information, and Communication , 5(4):84–107, 2008.
  32. 32. Heterogeneous Unmanned Aircraft System Heterogeneous UAS that combines the CU AUGNet and CU MAV Sensor Flock. Jack Elston, Eric W. Frew, Dale Lawrence, Peter Gray, and Brian Argrow. “Net-Centric Communication and Control for a Heterogeneous Unmanned Aircraft System.” Journal of Intelligent and Robotic Systems , 56(1-2):199-232, Sept., 2009 Cooperative Algorithms Application Layer Communication Protocols Sensor, Communication, and Control Fusion Data Routing and Network Configuration Physical and Transport Layers
  33. 33. Large Fleet with Multiple FAA COAs <ul><li>We fly at the Table Mountain Field Site in Boulder, CO under FAA Certificates of Authorization </li></ul><ul><ul><li>2008-WSA-1, 2008-WSA-43, 2008-WSA-64 </li></ul></ul><ul><li>We fly at the Pawnee National Grassland under FAA Certificate of Authorization 2008-WSA-53 </li></ul>
  34. 34. VORTEX 2 COA Status Blue: Committed Yellow: Validated Green: Active 61 commits 2/12/09 2008 WSA-51; 2009 WSA-82
  35. 35. Additional Capabilities Cold Weather Operation Autonomous Takeoff and Landing Pilot’s Eye View
  36. 36. AUGNet: Throughput vs. Range <ul><li>UAV Increases Communication Range </li></ul><ul><li>UAV Performance is More Erratic </li></ul>Timothy Brown, Brian Argrow, Eric Frew, Cory Dixon, Daniel Henkel, Jack Elston, and Harvey Gates. “Experiments Using Small Unmanned Aircraft to Augment a Mobile Ad Hoc Network.” Emerging Technologies in Wireless LANs: Theory, Design, and Deployment , Edited by Benny Bing, Ch. 28, p. 123-145, 2007.
  37. 37. Radio Frequency Data <ul><li>Data collected at Table Mountain facility </li></ul>
  38. 38. Chaining = RF Source Estimation <ul><li>Second chaining experiment </li></ul><ul><ul><li>1 UA to seek peak of 1 RF source </li></ul></ul><ul><ul><li>Shows ability to estimate and follow gradient of RSSI </li></ul></ul>
  39. 39. RF Source Estimation: UKF = DMC <ul><li>For comparison can also estimate node position </li></ul><ul><li>An Unscented Kalman Filter (UKF) with dynamic model compensation (DMC) is used for the estimation. </li></ul>
  40. 40. Latest Flight Results with NexSTAR-1 <ul><li>Electronic Chaining </li></ul><ul><ul><li>One NexSTAR </li></ul></ul><ul><ul><li>Two, static ground MNRs </li></ul></ul>
  41. 41. Looking Ahead… <ul><li>Introduction </li></ul><ul><li>Expected Information Rate </li></ul><ul><li>Decentralized Chaining </li></ul><ul><li>Experimental Systems </li></ul><ul><li>Future Work </li></ul>
  42. 42. The Research and Engineering Center for Unmanned Vehicles Prof. Eric W. Frew http://recuv.colorado.edu/~frew [email_address] The End

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