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Flight Test of the Novel Fixed-Wing Multireference Multiscale LN Guidance Logic for Complex Path Following

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Abstract

This paper presents the flight test verification and validation of a novel multi-reference longitudinal and lateral-directional guidance logic across multiple autonomous aircraft with distinctly different dynamics. LN guidance logic takes advantage of tracking a span of multiple references across the desired track, similar to an insect’s compound eyes, allowing for a much higher temporal resolution and more predictive and anticipatory guidance logic. The stability of LN guidance algorithms is investigated using global Lyapunov stability theorems and its stability is proved. Validation and verification flight tests of LN lateral-directional guidance logic demonstrate superior tracking performance adaptability of this novel method compared to other methods such as L1 or \({{{L}_{2}}^{+}}\). The LN longitudinal guidance is newly developed in this paper and shows excellent altitude tracking capabilities and similarly scalable. Statistical analysis of multiple flight tests involving varying environmental states with three unique aircraft and different advanced flight controllers are shown to prove the capabilities, robustness, and consistency of LN guidance.

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References

  1. Kim, J., Kim, S., et al.: Unmanned aerial vehicles in agriculture: A review of perspective of platform, control, and applications, vol. 7. https://doi.org/10.1109/ACCESS.2019.2932119 (2019)

  2. Dastgheibifard, S., Asnafi, M.: A review on potential applications of unmanned aerial vehicle for construction industry. Sustain. Struct. Mater. Int. J. 1(2), 44–53 (2018). https://doi.org/10.26392/SSM.2018.01.02.044

    Google Scholar 

  3. Bandyopadhyay, A., et al.: Coexisting in a world with urban air mobility: A revolutionary transportation system. In: 2018 Advances in Science and Engineering Technology International Conferences (ASET), pp. 1–6. https://doi.org/10.1109/ICASET.2018.8376817 (2018)

  4. Shakhatreh, H., Sawalmeh, et al.: Unmanned aerial vehicles (uavs): A survey on civil applications and key research challenges. IEEE Access 7, 48572–48634 (2019). https://doi.org/10.1109/ACCESS.2019.2909530

    Article  Google Scholar 

  5. Ritchie, H., Roser, M.: Urbanization Our World in Data. https://ourworldindata.org/urbanization (2018)

  6. Sultana, S., Weber, J.: The nature of urban growth and the commuting transition: Endless sprawl or a growth wave? Urban Stud. 51(3), 544–576 (2014). https://doi.org/10.1177/0042098013498284

    Article  Google Scholar 

  7. Cokyasar, T., Dong, W., Jin, M., Verbas, İÖ: Designing a drone delivery network with automated battery swapping machines. Comput. Oper. Res. 129, 105177 (2021). https://doi.org/10.1016/j.cor.2020.105177

    Article  MathSciNet  Google Scholar 

  8. Cheskes, S., McLeod, S.L., Nolan, M., Snobelen, P., Vaillancourt, C., Brooks, S.C., Dainty, K.N., Chan, T.C.Y., Drennan, I.R.: Improving access to automated external defibrillators in rural and remote settings: A drone delivery feasibility study. J. Amer. Heart Assoc. 9(14), 016687 (2020). https://doi.org/10.1161/JAHA.120.016687

    Article  Google Scholar 

  9. Huang, H., Savkin, A.V., Huang, C.: Reliable path planning for drone delivery using a stochastic time-dependent public transportation network. IEEE Trans. Intell. Transp. Syst. 22(8), 4941–4950 (2021). https://doi.org/10.1109/TITS.2020.2983491

    Article  Google Scholar 

  10. Leon, S., Chen, C., Ratcliffe, A.: Consumers’ perceptions of last mile drone delivery. Int. J. Logist. Res. Applic. 0(0), 1–20 (2021). https://doi.org/10.1080/13675567.2021.1957803

    Google Scholar 

  11. Leon, S., Chen, C., Ratcliffe, A.: Consumers’ perceptions of last mile drone delivery. Int. J. Logist. Res. Applic. 0(0), 1–20 (2021). https://doi.org/10.1080/13675567.2021.1957803

    Google Scholar 

  12. Rall, J.D., Reydel, B., Allaei, D.: Passenger acceptance of vtols for uam. INTER-NOISE and NOISE-CON Congress and Conference Proceedings 262(1), 1016–1022 (2020)

    Google Scholar 

  13. Deyst, J., How, J., Park, S.: Performance and Lyapunov stability of a nonlinear path-following guidance method. J. Guid. Control Dyn. 30(6), 1718–1728 (2007). https://doi.org/10.2514/1.28957

    Article  Google Scholar 

  14. Beard, R.W., W., M.T.: Straight-line and Orbit Following, 1st edn. Princeton University Press, Princeton (2012)

    Google Scholar 

  15. Xu, J., Le Pichon, T., Keshmiri, S.: Multi-eye guidance method for uavs path following. In: 2019 IEEE National Aerospace and Electronics Conference (NAECON), pp. 620–626. https://doi.org/10.1109/NAECON46414.2019.9058208 (2019)

  16. Xu, J., Keshmiri, S.: Flight test validation and verification of the novel ln guidance for unmanned aerial systems. In: 2021 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 610–618. https://doi.org/10.1109/ICUAS51884.2021.9476808 (2021)

  17. Sedlmair, N., Theis, J., Thielecke, F.: Experimental comparison of nonlinear guidance laws for unmanned aircraft. IFAC-PapersOnLine 53(2), 14805–14810 (2020). https://doi.org/10.1016/j.ifacol.2020.12.1922, 21st IFAC World Congress

    Article  Google Scholar 

  18. Cho, N., Kim, Y., Park, S.: Three-dimensional nonlinear path-following guidance law based on differential geometry. IFAC Proceedings Volumes 47(3), 2503–2508 (2014). https://doi.org/10.3182/20140824-6-ZA-1003.00171, 19th IFAC World Congress

    Article  Google Scholar 

  19. Curry, R., Lizarraga, M., Mairs, B., Elkaim, G.H.: L2+ an improved line of sight guidance law for uavs. In: American Control Conference. https://doi.org/10.1109/ACC.2013.657980 (2013)

  20. Sujit, P.B., Saripalli, S., Sousa, J.B.: Unmanned aerial vehicle path following: A survey and analysis of algorithms for fixed-wing unmanned aerial vehicless. IEEE Control. Syst. Mag. 34(1), 42–59 (2014). https://doi.org/10.1109/MCS.2013.2287568

    Article  MathSciNet  Google Scholar 

  21. Nelson, D.R., Barber, D.B., McLain, T.W., Beard, R.W.: Vector field path following for miniature air vehicles. IEEE Trans. Robot. 23(3), 519–529 (2007). https://doi.org/10.1109/TRO.2007.898976

    Article  Google Scholar 

  22. Ratnoo, A., Sujit, P.B., Kothari, M.: Adaptive optimal path following for high wind flights. IFAC Proceedings Volumes 44(1), 12985–12990 (2011). https://doi.org/10.3182/20110828-6-IT-1002.03720, 18th IFAC World Congress

    Article  Google Scholar 

  23. Horridge, G.A.: The compound eye of insects. Sci. Am. 237(1), 108–121 (1977)

    Article  Google Scholar 

  24. Zbikowski, R.: Fly like a fly. IEEE Spectrum (2005)

  25. Park, S., Deyst, J., How, J.P.: Performance and lyapunov stability of a nonlinear path following guidance method. J. Guid. Control Dyn. 30(6), 1718–1728 (2007). https://doi.org/10.2514/1.28957

    Article  Google Scholar 

  26. Kim, A.R., Vivekanandan, P., McNamee, P., Sheppard, I., Blevins, A., Sizemore, A.: Dynamic modeling and simulation of a quadcopter with motor dynamics. AIAA Modeling and Simulation Technologies Conference (2017)

  27. Brandt, J., Selig, M.: Propeller performance data at low reynolds numbers. 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition (2011)

  28. Jardin, M.R., Mueller, E.R.: Optimized measurements of unmanned-air-vehicle mass moment of inertia with a bifilar pendulum. J. Aircr., 46 (2009)

  29. Roskam, J.: Airplane flight dynamics and automatic flight controls DARcorporation (2001)

  30. Chowdhury, M., Keshmiri, S., Xu, J.: Design and flight test validation of a uas lateral-directional model predictive controller. In: 2021 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 639–646. https://doi.org/10.1109/ICUAS51884.2021.9476811 (2021)

  31. Shukla, D., Lal, R., Hauptman, D., Keshmiri, S.S., Prabhakar, P., Beckage, N.: Flight test validation of a safety-critical neural network based longitudinal controller for a fixed-wing UAS. https://doi.org/10.2514/6.2020-3093

  32. Shukla, D., Keshmiri, S., Beckage, N.: Imitation learning for neural network autopilot in fixed-wing unmanned aerial systems. In: 2020 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1508–1517. https://doi.org/10.1109/ICUAS48674.2020.9213850 (2020)

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Acknowledgements

The authors would like to thank the Heising-Simons foundation and National Aeronautics and Space Administration for their support. Much appreciation is given to collaborators from the KU Flight Research Laboratory: Hady Benyamen, Alex Zugazagoitia, Vishvas Sharma, Julian Moreno, Brennen Wheatly, Tyler Andrew, and Joseph Ativie for their assistance in flight test support and execution.

Funding

This work was completed with funding from the Heising-Simons foundation and National Aeronautics and Space Administration (NASA) grants #80NSSC19C0102 and 80NSSC20M0109/R52545-21-00934, and matching funds from the State of Kansas.

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Authors and Affiliations

  1. Department of Aerospace Engineering, University of Kansas, Lawrence, KS, 66045, USA

    Jeffrey Xu, Aaron McKinnis, Shawn Keshmiri & Robert Bowes

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  1. Jeffrey Xu
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  2. Aaron McKinnis
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  3. Shawn Keshmiri
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  4. Robert Bowes
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Contributions

JX developed the LN guidance logic algorithms adaptive weighting scheme. SK proposed the idea of multi-reference guidance and oversaw the development. AM provided Lyapunov stability analysis of the guidance logic. RB was the flight test lead. All authors reviewed the manuscript.

Corresponding author

Correspondence to Jeffrey Xu.

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Xu, J., McKinnis, A., Keshmiri, S. et al. Flight Test of the Novel Fixed-Wing Multireference Multiscale LN Guidance Logic for Complex Path Following. J Intell Robot Syst 105, 63 (2022). https://doi.org/10.1007/s10846-022-01660-x

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