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doi: 10.3934/jimo.2021063

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)

Received  December 2020 Revised  January 2021 Published  March 2021

Fund Project: 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)

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.

Citation: Yi Gao, Rui Li, Yingjing Shi, Li Xiao. Design of path planning and tracking control of quadrotor. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2021063
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. Google Scholar

[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. Google Scholar

[3]

Y.-B. ChenG.-C. LuoY.-S. MeiJ.-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.  Google Scholar

[4]

F. ChenJ.-Q. YuX.-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.  Google Scholar

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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. Google Scholar

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L. de FilippisG. Guglieri and F. Quagliotti, Path planning strategies for UAVs in 3D environments, Journal of Intelligent & Robotic Systems, 65 (2012), 247-264.   Google Scholar

[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.   Google Scholar

[8]

E. W. Dijkstra, A note on two problems in connexion with graphs, Numer. Math., 1 (1959), 269-271.  doi: 10.1007/BF01386390.  Google Scholar

[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.  Google Scholar

[10]

S. R. HerwitzL. F. JohnsonS. E. DunaganR. G. HigginsD. V. SullivanJ. ZhengB. M. LobitzJ. G. LeungB. A. GallmeyerM. AoyagiR. 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.   Google Scholar

[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. Google Scholar

[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. Google Scholar

[13]

R. LiY. 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.  Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[18]

M. Sumaila, Techniques for quadcopter modelling & design: A review, Journal of Unmanned System Technology. Google Scholar

[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. Google Scholar

[20]

F. YanY.-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.  Google Scholar

[21]

B. ZhaoB. XianY. 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.   Google Scholar

show all references

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. Google Scholar

[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. Google Scholar

[3]

Y.-B. ChenG.-C. LuoY.-S. MeiJ.-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.  Google Scholar

[4]

F. ChenJ.-Q. YuX.-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.  Google Scholar

[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. Google Scholar

[6]

L. de FilippisG. Guglieri and F. Quagliotti, Path planning strategies for UAVs in 3D environments, Journal of Intelligent & Robotic Systems, 65 (2012), 247-264.   Google Scholar

[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.   Google Scholar

[8]

E. W. Dijkstra, A note on two problems in connexion with graphs, Numer. Math., 1 (1959), 269-271.  doi: 10.1007/BF01386390.  Google Scholar

[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.  Google Scholar

[10]

S. R. HerwitzL. F. JohnsonS. E. DunaganR. G. HigginsD. V. SullivanJ. ZhengB. M. LobitzJ. G. LeungB. A. GallmeyerM. AoyagiR. 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.   Google Scholar

[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. Google Scholar

[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. Google Scholar

[13]

R. LiY. 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.  Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[18]

M. Sumaila, Techniques for quadcopter modelling & design: A review, Journal of Unmanned System Technology. Google Scholar

[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. Google Scholar

[20]

F. YanY.-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.  Google Scholar

[21]

B. ZhaoB. XianY. 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.   Google Scholar

Figure 1.  The system structure diagram
Figure 2.  The earth-fixed inertial and body-fixed frames of a quadcopter
Figure 3.  Path planning module test result
Figure 4.  Reference trajectory
Figure 5.  Path tracking of the quadrotor
Figure 6.  Path tracking of $ x(t) $
Figure 7.  Path tracking of $ y(t) $
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