Advertisement

Low-altitude contour mapping of radiation fields using UAS swarm

  • Zachary Cook
  • Monia Kazemeini
  • Alexander Barzilov
  • Woosoon YimEmail author
Original Research Paper
  • 22 Downloads

Abstract

This paper addresses the design of lightweight radiation sensors for the small-scale unmanned aerial system (UAS) and its implementation for low-altitude radiation source localization and contour mapping. The compact high-resolution gamma-ray CZT sensors were integrated into UAS platforms as plug-and-play components using robot operating system. The swarm of UAS has advantages over a single agent-based approach in detecting radiative sources and effectively mapping the area. The proposed swarm consists of three UAS platforms in a circular formation. The proposed approach can potentially be used for low-altitude clustered environments where a conventional helicopter-based platform cannot be utilized. It can provide a relatively precise boundary of the safe area for potential human exploration as well as enhancing situation awareness capabilities for first responders. The source seeking and contour mapping algorithms are developed based on a simple 1/R2 radiation field, but they are validated in more realistic radiation field having multiple sources and physical structures with scattering and attenuation effects simulated by MCNP code. Also, gradient estimation and contour mapping algorithms are validated experimentally with small-scale multicopter platforms in the indoor flight testbed.

Keywords

UAS Swarm Radiation Mapping Source Search 

Notes

Acknowledgements

This work is supported by a Grant from Savannah River Nuclear Solutions, LLC under Contract No. 0000217400 and by the National Science Foundation’s PFI Program, Grant No. 1430328.

Supplementary material

11370_2019_277_MOESM1_ESM.pptx (83.7 mb)
Supplementary material 1 (PPTX 85712 kb)

References

  1. 1.
    Gilbertson M (2013) US Department of Energy (DOE), experience and strategic lessons learned from decommissioning and remediation of large nuclear legacy sites. In: International experts’ meeting on decommissioning and remediation after a nuclear accidentGoogle Scholar
  2. 2.
    Sen TK, Moore LJ (2000) An organizational decision support system for managing the DOE hazardous waste cleanup program. Decis Support Syst 29(1):89CrossRefGoogle Scholar
  3. 3.
    Brewer ET (2009) Autonomous localization of 1/R 2 sources using an aerial platform autonomous localization of 1/R 2 sources using an aerial platform. M.S. Thesis, Virginia Polytechnic Institute and State UniversityGoogle Scholar
  4. 4.
    Sepulchre R, Paley DA, Leonard NE (2007) Stabilization of planar collective motion: all-to-all communication. IEEE Trans Automat Control 52(5):811–824MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Leonard NE, Paley DA, Lekien F, Sepulchre R, Fratantoni DM, Davis RE (2007) Collective motion, sensor networks, and ocean sampling collective motion, sensor networks, and ocean sampling. Proc IEEE 95(1):48–74CrossRefGoogle Scholar
  6. 6.
    Raffard RL, Tomlin CJ, Boyd SP, Formulation AP (2004) Distributed optimization for cooperative agents: application to formation flight. IEEE Conf Decis Control 3:2453–2459Google Scholar
  7. 7.
    Marshall JA, Broucke ME, Francis BA (2004) Formations of vehicles in cyclic pursuit. IEEE Trans Automat Control 49(11):1963–1974MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Ogren P, Egerstedt M, Hu X (2002) A control Lyapunov function approach to multiagent coordination. IEEE Trans Robot Autom 18(5):847–851CrossRefGoogle Scholar
  9. 9.
    Arranz LB, Seuret A, De Wit CC (2009) Translation control of a fleet circular formation of AUVs under finite communication range. Proc IEEE Conf Decis Control 98:8345–8350Google Scholar
  10. 10.
    Moore BJ, Canudas-de-Wit C (2010) Source seeking via collaborative measurements by a circular formation of agents. In: American control conference (ACC)Google Scholar
  11. 11.
    Cortez RA, Tanner HG (2008) Radiation mapping using multiple robots. Trans Am Nucl Soc 99:157–159Google Scholar
  12. 12.
    Han J, Chen Y (2014) Multiple UAV formations for cooperative source seeking and contour mapping of a radiative signal field. J Intell Robot Syst 74(1–2):323–332CrossRefGoogle Scholar
  13. 13.
    Cui R, Li Y, Yan W (2016) Mutual information-based multi-AUV path planning for scalar field sampling using multidimensional RRT. IEEE Trans Syst Man Cybernet Syst 46(7):993CrossRefGoogle Scholar
  14. 14.
    Hitz G, Galceran E, Garneau M, Pomerleau F (2017) Adaptive continuous-space informative path planning for online environmental monitoring. J Field Robot 34:1427–1449CrossRefGoogle Scholar
  15. 15.
  16. 16.
  17. 17.
    Kazemeini M, Barzilov A, Yim W, Lee J (2018) Integration of CZT and CLYC radiation sensors into a UAS platform. In: Proceedings of conference on sensors and electronic instrumentation advances (SEIA’18), Amsterdam, Netherlands, pp 57–59. 19–21 Sept 2018Google Scholar
  18. 18.
    Kazemeini M, Cook Z, Lee J, Barzilov A, Yim W (2018) Plug-and-play radiation sensor components for unmanned aerial system platform. J Radioanal Nucl Chem 318:1797–1803CrossRefGoogle Scholar
  19. 19.
    Ogren P, Fiorelli E, Leonard NE (2004) Cooperative control of mobile sensor networks: adaptive gradient climbing in a distributed environment. IEEE Trans Automat Control 49(8):1292–1302MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Uryasev S (1995) Derivatives of probability functions and some applications. Ann Oper Res 56(1):287–311MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Goorley T (2012) Initial MCNP6 release overview. Nucl Technol 180(3):298–315CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of Nevada, Las VegasLas VegasUSA

Personalised recommendations