Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Path-following Algorithms Comparison using Software-in-the-Loop Simulations for UAVs

  • Regular paper
  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Unmanned Aerial Vehicles (UAVs) are aircraft that can be manually operated or autonomously guided through an autopilot. In the last case, the system is responsible for stabilising the aircraft and executing guidance tasks such as path-following. In literature, several path-following algorithms were proposed for straight lines and loiter paths. Previous comparisons generally consider straight line algorithms in a 2D space using the kinematic model of an aircraft. In order to complement existing research, this paper compares four 3D path-following algorithms for loiter paths under different wind intensities. Tests are made through Software-in-the-Loop (SiL) simulations using the dynamic model of a fixed-wing UAV. Furthermore, a genetic algorithm is employed to tune the parameters and the analysis is carried out under varying wind conditions. The algorithms compared are four well-known geometric methods: Carrot-Chasing (CC), Non-Linear Guidance Law (NLGL), Pure Pursuit and Line-of-Sight (PLOS) and Vector Field (VF). Results show that Vector Field has the smallest errors, while PLOS is the most resistant to wind disturbance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

All data obtained from X-Plane simulations can be requested by e-mail to the corresponding author.

Code availability

All MATLAB code can be requested by e-mail to the corresponding author.

References

  1. Abozied, M.A.H., Qin, S.: (2016) High performance path following for uav based on advanced vector field guidance law. In: 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp 555–564 https://doi.org/10.1109/IMCEC.2016.7867272

  2. Aguiar, A.P., Hespanha, J.P., Kokotović, P.V.: Performance limitations in reference tracking and path following for nonlinear systems. Automatica 44(3), 598–610 (2008). https://doi.org/10.1016/j.automatica.2007.06.030

    Article  MathSciNet  MATH  Google Scholar 

  3. Åström, K., Hägglund, T.: (2006) Advanced PID Control. ISA-The Instrumentation, Systems, and Automation Society, Pittsburgh

  4. Beyer, H.G., Sendhoff, B.: Robust optimization - a comprehensive survey. Computer Methods in Applied Mechanics and Engineering 196(33–34), 3190–3218 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bittar, A., Figuereido, H.V., Guimaraes, P.A., Mendes, A.C.: (2014) Guidance software-in-the-loop simulation using x-plane and simulink for uavs. In: 2014 International Conference on Unmanned Aircraft Systems (ICUAS), Orlando, FL, USA, pp 993–1002 https://doi.org/10.1109/ICUAS.2014.6842350

  6. Blum, C., Puchinger, J., Raidl, G.R., Roli, A.: Hybrid metaheuristics in combinatorial optimization: A survey. Applied Soft Computing 11(6), 4135–4151 (2011)

    Article  MATH  Google Scholar 

  7. Breivik, M., Fossen, T.: (2005a) Guidance-based path following for autonomous underwater vehicles. In: Proceedings of OCEANS 2005 MTS/IEEE, pp 2807–2814 Vol. 3 https://doi.org/10.1109/OCEANS.2005.1640200

  8. Breivik, M., Fossen, T.I.: (2005b) Principles of guidance-based path following in 2D and 3D. In: Proceedings of the 44th IEEE Conference on Decision and Control, pp 627–634 https://doi.org/10.1109/CDC.2005.1582226

  9. Craighead, J., Murphy, R., Burke, J., Goldiez, B.: (2007) A survey of commercial open source unmanned vehicle simulators. In: 2007 IEEE International Conference on Robotics and Automation, pp 852–857

  10. Curry, R., Lizarraga, M., Mairs, B., Elkaim, G.: L+2, an improved line of sight guidance law for UAVs. American Control Conference (ACC) 2013, 1–6 (2013). https://doi.org/10.1109/ACC.2013.6579804

    Article  Google Scholar 

  11. Documentation, S.: (2020) Simulation and model-based design. https://www.mathworks.com/products/simulink.html. Accessed 7 June 2021

  12. Elkaim, G., Lie, F., Gebre-Egziabher, D.: (2015) Principles of guidance, navigation, and control of UAVs. In: Valavanis KP, Vachtsevanos GJ (eds) Handbook of Unmanned Aerial Vehicles, Springer Netherlands, pp 347–380 https://doi.org/10.1007/978-90-481-9707-1_56

  13. Fleming, P.J., Purshouse, R.C.: Evolutionary algorithms in control systems engineering: a survey. Control Engineering Practice 10(11), 1223–1241 (2002)

    Article  Google Scholar 

  14. Fortuna, J., Fossen, T.I.: (2015) Cascaded line-of-sight path-following and sliding mode controllers for fixed-wing UAVs. In: 2015 IEEE Conference on Control Applications (CCA), pp 798–803 https://doi.org/10.1109/CCA.2015.7320715

  15. Gerlach, A., Kingston, D., Walker, B.: UAV navigation using predictive vector field control. American Control Conference (ACC) 2014, 4907–4912 (2014). https://doi.org/10.1109/ACC.2014.6859082

    Article  Google Scholar 

  16. Jain, R.: The art of computer systems performance analysis - techniques for experimental design, measurement, simulation, and modeling. Wiley, Wiley professional computing (1991)

    MATH  Google Scholar 

  17. Johnson, E.N., Fontaine, S.: (2001) Use of flight simulation to complement flight testing of low-coast UAVs. In: AIAA Infotech@ Aerospace, pp 1–6

  18. Julian, L.: (2017) X-Plane 11 - Cessna 172 - Pilot’s Operating Manual. Laminar Research

  19. Jung, W., Lim, S., Lee, D., Bang, H.: (2016) Unmanned aircraft vector field path following with arrival angle control. Journal of Intelligent & Robotic Systems pp 1–15 https://doi.org/10.1007/s10846-016-0332-5

  20. Kukreti, S., Kumar, M., Cohen, K.: (2016) Genetically tuned LQR based path following for UAVs under wind disturbance. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp 267–274 https://doi.org/10.1109/ICUAS.2016.7502620

  21. Laminar, R.: (2014) X-Plane 10 Manual. Laminar Research

  22. Mathisen, S.H., Gryte, K., Fossen, T.I., Johansen, T.A.: (2016) Non-linear model predictive control for longitudinal and lateral guidance of a small fixed-wing UAV in precision deep stall landing. In: AIAA Infotech@ Aerospace, p 0512

  23. MATLAB: (2018) version 9.4.0.813654 (R2018a). The MathWorks Inc., Natick, Massachusetts, USA

  24. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)

    Book  MATH  Google Scholar 

  25. NASA Ames Research Center (2017) X-plane communications toolbox. https://github.com/nasa/XPlaneConnect, [Online. Retrieved March 27, 2019.]

  26. Nelson, D., Barber, D., McLain, T., Beard, R.: (2006) Vector field path following for small unmanned air vehicles. In: 2006 American Control Conference, IEEE, Minneapolis, pp 7–

  27. Nelson, D., Barber, D., McLain, T., Beard, R.: Vector field path following for miniature air vehicles. IEEE Transactions on Robotics 23(3), 519–529 (2007). https://doi.org/10.1109/TRO.2007.898976

    Article  Google Scholar 

  28. Open Robotics: (2002) Robotic operating system. https://gazebosim.org/home. Accessed 23 May 2021

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

    Article  Google Scholar 

  30. Pelizer, G.V., da Silva, N.B.F., Branco, K.R.L.J.: (2017a) Comparison of 3d path-following algorithms for unmanned aerial vehicles. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), Miami, FL, USA, pp 498–505 https://doi.org/10.1109/ICUAS.2017.7991338

  31. Pelizer, G.V., Silva, N.B.F., Branco, K.R.L.J.C.: (2017b) 3D Path-Following Algorithms for Unmanned Aerial Vehicles Adjusted with Genetic Algorithm, Springer International Publishing, Cham, pp 63–80. https://doi.org/10.1007/978-3-319-61403-8_4

  32. Perez-Leon, H., Acevedo, J.J., Millan-Romera, J.A., Castillejo-Calle, A., Maza, I., Ollero, A.: An aerial robot path follower based on the ‘carrot chasing’ algorithm. In: Silva, M.F., Luís Lima, J., Reis, L.P., Sanfeliu, A., Tardioli, D. (eds.) Robot 2019: Fourth Iberian Robotics Conference, pp. 37–47. Springer International Publishing, Cham (2020)

    Chapter  Google Scholar 

  33. Ramana, M., Varma, S.A., Kothari, M.: (2016) Motion planning for a fixed-wing uav in urban environments. IFAC-PapersOnLine 49(1):419–424 https://doi.org/10.1016/j.ifacol.2016.03.090,4th IFAC Conference on Advances in Control and Optimization of Dynamical Systems ACODS 2016

  34. Silva, N.B.F., Fontes, J.V.C., Inoue, R.S., Branco, K.R.L.J.C.: Dynamic inversion and gain-scheduling control for an autonomous aerial vehicle with multiple flight stages. Journal of Control, Automation and Electrical Systems (2018). https://doi.org/10.1007/s40313-018-0375-x

    Article  Google Scholar 

  35. Stanford Artificial Intelligence Laboratory et al: (2010) Robotic operating system. https://www.ros.org. Accessed 9 June 2021

  36. Stevens, B., Lewis, F., Johnson, E.: Aircraft Control and Simulation, 3rd edn. Wiley, USA (2016)

    Google Scholar 

  37. Sujit, P., Saripalli, S., Sousa, J.: (2013) An evaluation of UAV path following algorithms. In: 2013 European Control Conference (ECC), pp 3332–3337

  38. 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 Systems 34(1), 42–59 (2014). https://doi.org/10.1109/MCS.2013.2287568

    Article  MathSciNet  Google Scholar 

  39. Tabatabaei, S.A.H., Yousefi-koma, A., Ayati, M., Mohtasebi, S.S.: (2015) Three dimensional fuzzy carrot-chasing path following algorithm for fixed-wing vehicles. In: 2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM), pp 784–788 https://doi.org/10.1109/ICRoM.2015.7367882

  40. Valavanis, K.P., Vachtsevanos, G.J.: Handbook of Unmanned Aerial Vehicles. Springer Publishing Company, Incorporated, USA (2015)

    Book  MATH  Google Scholar 

  41. Venkatraman, K., Mani, V., Kothari, M., Postlethwaite, I., Gu, D.W.: A suboptimal path planning algorithm using rapidly-exploring random trees. International Journal of Aerospace Innovations 2(1–2), 93–104 (2010)

    Article  Google Scholar 

  42. Wu, K., Cai, Z., Wang, Y.: (2016) A path following algorithm with waypoint switching strategy for unmanned aerial vehicle based on the geometric. In: 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC), pp 1565–1570 https://doi.org/10.1109/CGNCC.2016.7829023

  43. Xavier, D.M., Natassya Silva, B.F., Branco, K.R.L.J.C.: (2018) Comparison of path-following algorithms for loiter paths of unmanned aerial vehicles. In: 2018 IEEE Symposium on Computers and Communications (ISCC), Natal, RN, BR, pp 01243–01248 https://doi.org/10.1109/ISCC.2018.8538672

  44. Xavier, D.M., Natassya, B.F.S., Kalinka, R.L.J.C.B.: (2019) Path-following algorithms comparison using software-in-the-loop simulations for uavs. In: 2019 IEEE Symposium on Computers and Communications (ISCC), pp 1216–1221 https://doi.org/10.1109/ISCC47284.2019.8969604

  45. Yu, S., Li, X., Chen, H., Allgöwer, F.: Nonlinear model predictive control for path following problems. International Journal of Robust and Nonlinear Control 25(8), 1168–1182 (2015). https://doi.org/10.1002/rnc.3133

    Article  MathSciNet  MATH  Google Scholar 

  46. Zhang, X., Mi, C.: Vehicle Power Management: Modeling. Control and Optimization, Power Systems, Springer, London (2011)

Download references

Acknowledgements

Prior to this paper, the authors have published an article at an international conference with the same scope of this work [44]. However, differently from this paper, the publication referred to only three path-following algorithms, while the present study also considers Vector Field. Previously, the algorithms’ parameters were manually tuned using a technique of trial and error, whereas a Genetic Algorithm is used in this work to achieve a more reliable comparison. Furthermore, in [44] the analysis was performed in an ideal scenario (without wind), which is not representative of real flight conditions. In this paper, a more robust comparison is achieved considering three environmental scenarios: no wind, 5 m/s wind and 10 m/s wind. The genetic algorithm was applied to every path-following strategy at each environmental condition in order to obtain a good performance in every scenario considered. Therefore, the comparison presented in this paper is more robust supported by more reliable results as the simulation considers more realistic flight conditions. The former article lacks in variability and reproducibility, presenting an older version of the Pure-Pursuit and Line-of-Sight algorithm that is not suited for simulations using a big aircraft and long circular paths. Preliminary results indicated a better performance of an improved version of NLGL when compared to Carrot-Chasing and PLOS. In this work, however, results show that Vector Field is more accurate in following the path, while PLOS\(_{+}\) is more robust to disturbances. The similarities of this work in comparison to [44] are due to the scope addressed in the papers. Both works present a definition of the path-following problem, a section explaining the theory behind the strategies and also a description of the simulation environment. However, the methodology applied and results obtained are different, assuring the novelty of this work.

Funding

The authors acknowledge the support granted by FAPESP, through process 2017/21303-2, USP and UTFPR.

Author information

Authors and Affiliations

  1. Escola de Engenharia de São Carlos, Universidade de São Paulo, São Carlos, Brazil

    Daniel M. Xavier

  2. Universidade Tecnológica Federal do Paraná, Cornélio Procópio, Brazil

    Natassya B. F. Silva

  3. Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos, Brazil

    Kalinka R. L. J. C. Branco

Authors
  1. Daniel M. Xavier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Natassya B. F. Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kalinka R. L. J. C. Branco
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D. M. Xavier carried out the algorithm’s implementation to MATLAB and X-Plane, the genetic algorithm optimization and the comparison. N. B. F. Silva carried out the PID implementation, helping with the control theory and participating in the manuscript design and study coordination. K. R. L. J. C. Branco conceived the study and participated in its design and coordination, helping to draft the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Daniel M. Xavier.

Ethics declarations

Conflicts of interest

The authors declare that they have no competing or conflicting interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The authors acknowledge the support granted by FAPESP through process 2017/21303-2 and UTFPR. Research was also sponsored by the Army Research Office and was accomplished under Grant Number W911NF-18-1-0012. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorised to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

A: Appendix

A: Appendix

The parameters settled by the genetic algorithm for each path-following strategy are reported in Table 4 (no wind), Table 5 (5 m/s wind) and Table 6 (10 m/s wind). The optimization was carried out three times for each strategy: one for each environmental scenario studied.

Table 4 Algorithm parameters in the absence of wind
Full size table
Table 5 Algorithm parameters under 5 m/s wind
Full size table
Table 6 Algorithm parameters under 10 m/s wind
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xavier, D.M., Silva, N.B.F. & Branco, K.R.L.J.C. Path-following Algorithms Comparison using Software-in-the-Loop Simulations for UAVs. J Intell Robot Syst 106, 63 (2022). https://doi.org/10.1007/s10846-022-01764-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-022-01764-4

Keywords

Search

Navigation