Design of path planning and tracking control of quadrotor

Yi Gao; Rui Li; Yingjing Shi; Li Xiao

doi:10.3934/jimo.2021063

Article Contents

2022, Volume 18, Issue 3: 2221-2235. Doi: 10.3934/jimo.2021063

This issue Previous Article Supply chain coordination considering e-tailer's promotion effort and logistics provider's service effort Next Article A novel risk ranking method based on the single valued neutrosophic set

Design of path planning and tracking control of quadrotor

School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

^* Corresponding author: Rui Li (lirui@uestc.edu.cn)
^* Corresponding author: Rui Li (lirui@uestc.edu.cn)

Received: November 30, 2020

Revised: December 31, 2020

Published: May 2022

This work is supported in part by the National Natural Science Foundation of China under grant (No. 61973055), the Fundamental Research Funds for the Central Universities (No. ZYGX2019J062) and a grant from the applied basic research programs of Sichuan province (No. 2019YJ0206)

Abstract Full Text(HTML) Figure(7) Related Papers Cited by

Abstract

In this paper, we first design a motion planning system based on the Batch Informed Trees (BIT*) algorithm for quadrotor and a linear model predictive control (LMPC) is applied to solve the path tracking problem for a quadrotor. BIT* algorithm is used to plan a barrier-free trajectory quickly in an obstructed environment. Then we apply linear model predictive control for the full state quadrotor system model to track the generated trajectory. Finally, the BIT* algorithm simulation case is presented using RVIZ visual interface and some simulation cases are presented using MATLAB / Simulink. The results demonstrate the capability and the effectiveness of the control strategy in fast path tracking and the quadrotor stability, while the desired performance is achieved.

Keywords:

Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

Citation:

FullText(HTML)

Figure 1. The system structure diagram

Download: Full-size image PowerPoint slide

Figure 2. The earth-fixed inertial and body-fixed frames of a quadcopter

Download: Full-size image PowerPoint slide

Figure 3. Path planning module test result

Download: Full-size image PowerPoint slide

Figure 4. Reference trajectory

Download: Full-size image PowerPoint slide

Figure 5. Path tracking of the quadrotor

Download: Full-size image PowerPoint slide

Figure 6. Path tracking of $ x(t) $

Download: Full-size image PowerPoint slide

Figure 7. Path tracking of $ y(t) $

Download: Full-size image PowerPoint slide

Related Papers

Cited by

References

[1]	Y. Bai, H. Liu, Z. Shi and Y. Zhong, Robust control of quadrotor unmanned air vehicles, in Proceedings of the 31st Chinese Control Conference, Hefei, (2012), 4462–4467.
[2]	J. Carsten, D. Ferguson and A. Stentz, 3Dfield d: Improved path planning and replanning in three dimensions, in 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, (2006), 3381–3386.
[3]	Y.-B. Chen, G.-C. Luo, Y.-S. Mei, J.-Q. Yu and X.-L. Su, UAV path planning using artificial potential field method updated by optimal control theory, Internat. J. Systems Sci., 47 (2016), 1407-1420. doi: 10.1080/00207721.2014.929191.
[4]	F. Chen, J.-Q. Yu, X.-L. Su and G.-C. Luo, Path planning for multi-UAV formation, Journal of Intelligent and Robotic Systems, 77 (2015), 229-246. doi: 10.1007/s10846-014-0077-y.
[5]	N. R. Council, Autonomous Vehicles in Support of Naval Operations, The National Academies Press, Washington, DC, 2005, https://www.nap.edu/catalog/11379/autonomous-vehicles-in-support-of-naval-operations.
[6]	L. de Filippis, G. Guglieri and F. Quagliotti, Path planning strategies for UAVs in 3D environments, Journal of Intelligent & Robotic Systems, 65 (2012), 247-264.
[7]	T. Dierks and S. Jagannathan, Output feedback control of a quadrotor UAV using neural networks, IEEE Transactions on Neural Networks, 21 (2010), 50-66.
[8]	E. W. Dijkstra, A note on two problems in connexion with graphs, Numer. Math., 1 (1959), 269-271. doi: 10.1007/BF01386390.
[9]	J. D. Gammell, S. S. Srinivasa and T. D. Barfoot, Batch informed trees (BIT): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs, 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA*, USA, (2015), 3067–3074. doi: 10.1109/ICRA.2015.7139620.
[10]	S. R. Herwitz, L. F. Johnson, S. E. Dunagan, R. G. Higgins, D. V. Sullivan, J. Zheng, B. M. Lobitz, J. G. Leung, B. A. Gallmeyer, M. Aoyagi, R. E. Slye and J. A. Brass, Imaging from an unmanned aerial vehicle: Agricultural surveillance and decision support, Computers and Electronics in Agriculture, 44 (2004), 49-61.
[11]	Y. Kwangjin and S. Sukkarieh, Real-time continuous curvature path planning of UAVs in cluttered environments, in 2008 5th International Symposium on Mechatronics and its Applications, IEEE, (2008), 1–6.
[12]	X. Q. Li, L. Qiu, S. Aziz, J. F. Pan, J. P. Yuan and B. Zhang, Control method of UAV based on RRT* for target tracking in cluttered environment, in 2017 7th International Conference on Power Electronics Systems and Applications - Smart Mobility, Power Transfer & Security (PESA), (2017), 1–4.
[13]	R. Li, Y. Shi and K.-L. Teo, Coordination arrival control for multi-agent systems, Internat. J. Robust Nonlinear Control, 26 (2016), 1456-1474. doi: 10.1002/rnc.3359.
[14]	Z. Ma, T. Hu, L. Shen, W. Kong, B. Zhao and K. Yao, An iterative learning controller for quadrotor UAV path following at a constant altitude, in 2015 34th Chinese Control Conference (CCC), Hangzhou, China, (2015), 4406–4411.
[15]	M. Nguyen Duc, T. N. Trong and Y. S. Xuan, The quadrotor MAV system using PID control, in 2015 IEEE International Conference on Mechatronics and Automation (ICMA), Beijing, (2015), 506–510.
[16]	R. Sharma, M. Kothari, C. N. Taylor and I. Postlethwaite, Cooperative target-capturing with inaccurate target information, in Proceedings of the 2010 American Control Conference, Baltimore, MD, (2010), 5520–5525.
[17]	Z. Shulong, A. Honglei, Z. Daibing and S. Lincheng, A new feedback linearization LQR control for attitude of quadrotor, in 2014 13th International Conference on Control Automation Robotics Vision (ICARCV), Singapore, (2014), 1593–1597.
[18]	M. Sumaila, Techniques for quadcopter modelling & design: A review, Journal of Unmanned System Technology.
[19]	J. Xiong and G. Zhang, Sliding mode control for a quadrotor UAV with parameter uncertainties, in 2016 2nd International Conference on Control, Automation and Robotics (ICCAR), Hong Kong, (2016), 207–212.
[20]	F. Yan, Y.-S. Liu and J.-Z. Xiao, Path planning in complex 3D environments using a probabilistic roadmap method, International Journal of Automation and Computing, 10 (2013), 525-533. doi: 10.1007/s11633-013-0750-9.
[21]	B. Zhao, B. Xian, Y. Zhang and X. Zhang, Nonlinear robust adaptive tracking control of a quadrotor UAV via immersion and invariance methodology, IEEE Transactions on Industrial Electronics, 62 (2015), 2891-2902.