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

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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.

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

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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.

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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

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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.

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Correspondence to Daniel M. Xavier.

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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
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Table 5 Algorithm parameters under 5 m/s wind
Full size table
Table 6 Algorithm parameters under 10 m/s wind
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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

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