November 9, Planning and Control of Unmanned Aircraft Systems in Realistic Communication Environments
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.
3.
4.
5.
6.
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.
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.
11.
12.
13.
14.
15.
16.
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
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. 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. 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.
34. VORTEX 2 COA Status Blue: Committed Yellow: Validated Green: Active 61 commits 2/12/09 2008 WSA-51; 2009 WSA-82
42. The Research and Engineering Center for Unmanned Vehicles Prof. Eric W. Frew http://recuv.colorado.edu/~frew [email_address] The End
Notas del editor
RR network has performance function which has a spatially distributed gradient mapping. FP network looks spatially distributed when S1=S2=S3=…=Sn (and only when). There are more critical points in the FP due to the fact that dC/dS=0.
![Motivation ,[object Object],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](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)