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Sensors for Missions

  • Luis Mejias
  • John Lai
  • Troy Bruggemann
Reference work entry

Abstract

An onboard payload may be seen in most instances as the “Raison d’Etre” for a UAV. It will define its capabilities, usability and hence market value. Large and medium UAV payloads exhibit significant differences in size and computing capability when compared with small UAVs. The latter has stringent size, weight, and power requirements, typically referred as SWaP, while the former still exhibit endless appetite for compute capability. The tendency for this type of UAVs (Global Hawk, Hunter, Fire Scout, etc.) is to increase payload density and hence processing capability. An example of this approach is the Northrop Grumman MQ-8 Fire Scout helicopter, which has a modular payload architecture that incorporates off-the-shelf components. Regardless of the UAV size and capabilities, advances in miniaturization of electronics are enabling the replacement of multiprocessing, power-hungry general-purpose processors with more integrated and compact electronics (e.g., FPGAs).

The payload plays a significant role in the quality of ISR (intelligent, surveillance, and reconnaissance) data, and also in how quickly that information can be delivered to the end user. At a high level, payloads are important enablers of greater mission autonomy, which is the ultimate aim in every UAV.

This section describes common payload sensors and introduces two cases in which onboard payloads were used to solve real-world problems. A collision avoidance payload based on electro optical (EO) sensors is first introduced, followed by a remote sensing application for power line inspection and vegetation management.

Keywords

Global Navigation Satellite System Global Navigation Satellite System Graphic Process Unit Power Line Collision Avoidance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. P. Angelov, C.D. Bocaniala et al., A passive approach to autonomous collision detection and avoidance, in Proceedings of the 10th Internatinal Conference on Computer Modeling and Simulation (IEEE computer Society, Washington, DC 2008), pp. 64–69Google Scholar
  2. ARA, Airborne Research Australia. Flinders University (2011), http://ara.es.flinders.edu.au.
  3. T.S. Bruggemann, J.J. Ford, Compensation of Unmodeled Aircraft Dynamics in Airborne Inspection of Linear Infrastructure Assets (The Australian Control Conference (AUCC), Melbourne, 2011)Google Scholar
  4. T.S. Bruggemann, J.J. Ford et al., Control of aircraft for inspection of linear infrastructure. IEEE Trans. Control Syst. Technol. 19(6), 1409 (2011)CrossRefGoogle Scholar
  5. Y. Cao, H. Chao et al., Autopilots for small unmanned aerial vehicles: a survey. Int. J. Control Autom. Syst. 8(1), 44 (2010)Google Scholar
  6. J. Chase, B. Nelson et al., Real-time optical flow calculations on FPGA and GPU architectures: a comparison study, in 16th International Symposium on Field-Programmable Custom Computing Machines, FCCM ’08 (IEEE Computer Society, Los Alamitos, 2008)Google Scholar
  7. R.H. Couch, C.W. Rowland et al., Lidar in-space technology experiment: NASA’s first in-space lidar system for atmospheric research. Opt. Eng. 30(1), 88–95 (1991)CrossRefGoogle Scholar
  8. R.O. Dubayah, J.B. Drake, Lidar remote sensing for forestry. J. For. 98(6), 44–46 (2000)Google Scholar
  9. D. Dusha, L. Mejias, Error analysis and attitude observability of a monocular GPS/visual odometry integrated navigation filter. Int. J. Robot. Res. 31(6), 714–737 (2012)CrossRefGoogle Scholar
  10. D. Dusha, L. Mejias et al., Fixed-wing attitude estimation using temporal tracking of the horizon and optical flow. J. Field Robot. 28(2), 372 (2011)Google Scholar
  11. FAA, Introduction to TCAS II – Version 7 (2000)Google Scholar
  12. D.A. Forsyth, J. Ponce, Computer Vision: A Modern Approach (Prentice Hall, Upper Saddle River, 2002)Google Scholar
  13. C.M. Geyer, D. Dey et al., Prototype sense-and avoid system for UAVs. CMU-RI-TR-09-09, Robotics Institute, Carnegie Mellon University (2009)Google Scholar
  14. D. Greer, R. Mudford et al. Airborne systems laboratory for automation research, in Proceedings of the 27th Congress of the International Council of the Aeronautical Sciences (ICAS) Nice (Optimage, 2010)Google Scholar
  15. M.S. Grewal, L.R. Well et al. Global Positioning Systems, Inertial Navigation, and Integration (Wiley-Interscience, Hoboken 2007)CrossRefGoogle Scholar
  16. M. Hanlon, ScanEagle UAV gets Synthetic Aperture Radar (SAR). Gizmag., (2008), http://www.gizmag.com/scaneagle-uav-gets-synthetic-aperture-radar-sar/9007/
  17. W.H. Harman, TCAS – A system for preventing midair collisions. Linc. Lab. J. 2, 458 (1989)Google Scholar
  18. B.C. Karhoff, J.I. Limb et al., Eyes in the domestic sky: an assessment of sense and avoid technology for the Army’s warrior unmanned aerial vehicle, in IEEE Systems and Information Engineering Design Symposium, Charlottesville (IEEE, 2006), pp. 36–42Google Scholar
  19. R. Karlsson, F. Gustafsson, Recursive Bayesian estimation: bearings-only applications. IEE Proc. Radar Sonar Navig. 152(5), 313 (2005)CrossRefGoogle Scholar
  20. C. Kiemle, G. Ehret et al., Estimation of boundary layer humidity fluxes and statistics from airborne differential absorption lidar (DIAL). J. Geophys. Res. 102(D24), 29189–29203 (1997)CrossRefGoogle Scholar
  21. F. Kunzi, Development of High User Benefit ADS-B Applications: Conflict Detection for General Aviation, MIT. International Center for Air Transportation (2009)Google Scholar
  22. C.-P. Lai, Y.-J. Ren et al., ADS-B based collision avoidance radar for unmanned aerial vehicles, in IEEE MTT-S International Microwave Symposium Digest, 2009. MTT ’09 (IEEE, Piscataway, 2009), p. 88Google Scholar
  23. J. Lai, J.J. Ford et al. Detection versus false alarm characterisation of a vision-based airborne Dimtarget collision detection system. Manuscript Accepted to International Conference on Digital Image Computing: Techniques and Applications (DICTA) (IEEE, Washington, DC 2011a)Google Scholar
  24. J. Lai, L. Mejias et al., Airborne vision-based collision-detection system. J. Field Robot. 28(2), 157 (2011b)CrossRefGoogle Scholar
  25. J. Lai, J.J. Ford et al. Field-of-view, detection range, and false alarm trade-offs in vision-based aircraft detection. Proceedings of the 28th Congress of the International Council of the Aeronautical Sciences (ICAS), Brisbane (Optimage, 2012)Google Scholar
  26. M.A. Lefsky, W.B. Cohen et al., Lidar remote sensing for ecosystem studies. BioScience 52(1), 19–30 (2002)CrossRefGoogle Scholar
  27. Z. Li, T. Bruggemann et al., Towards automated power line corridor monitoring using advanced aircraft control and multi-source feature fusion. J. Field Robot. 29(1), 4–24 (2012)CrossRefGoogle Scholar
  28. T. Merz, S. Duranti et al., Autonomous landing of an unmanned helicopter based on vision and inertial sensing, in Experimental Robotics IX, vol. 21 (Springer, Berlin/Heidelberg, 2006), p. 352CrossRefGoogle Scholar
  29. NavWorx, ADS600-B Remote transceiver. NavWorx Inc. (2011), http://www.navworx.com/
  30. Riegl RIEGL Laser Measurement Systems GmbH. Retrieved Feb., 2011, from www.riegl.com (2001)
  31. RTCA, Minimum Aviation System Performance Standard for Automatic Dependent Surveillance Broadcast (ADS-B). Retrieved Feb, 2011, from www.rtca.org (2002)
  32. M. Soumekh, Synthetic Aperture Radar Signal Processing With Matlab Algorithms (Wiley, New York, 1999)Google Scholar
  33. G.W. Stimson, Introduction to Airborne Radar (SciTech Pub, Mendham, 1998)Google Scholar
  34. R. Szeliski, Computer Vision: Algorithms and Applications (Springer, New York/London, 2011)CrossRefGoogle Scholar
  35. Terranean, Terranean Mapping Technologies (2011), http://www.terranean.com.au/
  36. D.B. Thomas, L. Howes et al., A comparison of CPUs, GPUs, FPGAs, and massively parallel processor arrays for random number generation, in Proceedings of the ACM/SIGDA International Symposium on Field Programmable Gate Arrays (ACM, Monterey, 2009), pp. 63–72CrossRefGoogle Scholar
  37. J. Utt, J. McCalmont et al., Test and integration of a detect and avoid system, in AIAA 3rd “Unmanned Unlimited” Technical Conference, Workshop and Exhibit, 20–23 Sept. 2004 (Chicago, American Institute of Aeronautics and Astronautics (AIAA), 2004)Google Scholar
  38. J. Utt, J. McCalmont et al., Development of a sense and avoid system, in Infotech@Aerospace. American Institute of Aeronautics and Astronautics (AIAA), Arlington, Virginia, 26–29 Sept. 2005Google Scholar
  39. L. Wein, C. Capelle et al., GPS and stereovision-based visual odometry: application to urban scene mapping and intelligent vehicle localization. Int. J. Veh. Technol. (Article ID 439074) (2011)Google Scholar
  40. L. Yuee, L. Zhengrong et al., Classification of airborne LIDAR intensity data using statistical analysis and Hough transform with application to power line corridors, in Digital Image Computing: Techniques and Applications, 2009. DICTA ’09, Melbourne, Australia (IEEE, 2009)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Australian Research Centre for Aerospace AutomationQueensland University of TechnologyBrisbaneAustralia

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