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Optimal path planning for two-wheeled self-balancing vehicle pendulum robot based on quantum-behaved particle swarm optimization algorithm

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Abstract

Based on the self-balance of free-floating two-wheel self-balancing pendulum robot system, an optimal path planning of two-wheel self-balancing pendulum robot is proposed. Firstly, the corner trajectory of the two-wheel self-balancing pendulum robot is parameterized by quantum particle swarm optimization (QPSO), and the objective function is designed according to the base attitude and the control accuracy of the robot’s terminal position and attitude. The optimal path planning problem of the two-wheel self-balancing pendulum robot system is transformed into the optimization problem of the non-linear system. Quantum particle swarm optimization (QPSO) algorithm is used to solve the non-linear optimization problem, and the goal of optimal path planning is achieved. The experimental results show that the proposed method can converge to the global optimal value with fast convergence speed and less adjustment parameters. The planned joint path satisfies the range of corner, angular velocity, and angular acceleration. The joint path is smooth and suitable for the control of two-wheel self-balancing pendulum robot.

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References

  1. Dai F, Gao X, Jiang S, Guo W, Liu Y (2015) A two-wheeled inverted vehicle pendulum robot with friction compensation. Mechatronics 30:116–125

    Article  Google Scholar 

  2. He C, Huang K, Chen X, Zhang Y, Zhao H (2018) Transportation control of cooperative double-wheel inverted pendulum robots adopting udwadia-control approach. Nonlinear Dynamics 91(4):2789–2802

    Article  Google Scholar 

  3. Lee JH, Shin HJ, Lee SJ, Jung S (2013) Balancing control of a single-wheel inverted pendulum system using air blowers: evolution of mechatronics capstone design. Mechatronics 23(8):926–932

    Article  Google Scholar 

  4. Yue M, Wei X, Li Z (2014) Adaptive sliding-mode control for two-wheeled inverted pendulum vehicle based on zero-dynamics theory. Nonlinear Dynamics 76(1):459–471

    Article  MathSciNet  MATH  Google Scholar 

  5. Xu JX, Guo ZQ, Lee TH (2013) Design and implementation of a takagi–sugeno-type fuzzy logic controller on a two-wheeled mobile robot. IEEE Trans Ind Electron 60(12):5717–5728

    Article  Google Scholar 

  6. Zhou YS, Wang ZH (2016) Robust motion control of a two-wheeled inverted pendulum with an input delay based on optimal integral sliding mode manifold. Nonlinear Dynamics 85(3):2065–2074

    Article  MathSciNet  Google Scholar 

  7. Huang J, Ri MH, Wu D, Ri S (2018) Interval type-2 fuzzy logic modeling and control of a mobile two-wheeled inverted pendulum. IEEE Trans Fuzzy Syst 26(4):2030–2038

    Article  Google Scholar 

  8. Fukushima H, Muro K, Matsuno F (2015) Sliding-mode control for transformation to an inverted pendulum mode of a mobile robot with wheel-arms. IEEE Trans Ind Electron 62(7):4257–4266

    Article  Google Scholar 

  9. Fukushima H, Kakue M, Kon K, Matsuno F (2013) Transformation control to an inverted pendulum for a mobile robot with wheel-arms using partial linearization and polytopic model set. IEEE Trans Robot 29(3):774–783

    Article  Google Scholar 

  10. Lee H, Jung S (2012) Balancing and navigation control of a mobile inverted vehicle pendulum robot using sensor fusion of low cost sensors. Mechatronics 22(1):95–105

    Article  Google Scholar 

  11. Xu JX, Guo ZQ, Lee TH (2014) Design and implementation of integral sliding-mode control on an underactuated two-wheeled mobile robot. IEEE Trans Ind Electron 61(7):3671–3681

    Article  Google Scholar 

  12. Yang C, Li Z, Li J (2013) Trajectory planning and optimized adaptive control for a class of wheeled inverted pendulum vehicle models. IEEE Transactions on Cybernetics 43(1):24–36

    Article  Google Scholar 

  13. Huang J, Ding F, Fukuda T, Matsuno T (2013) Modeling and velocity control for a novel narrow vehicle based on mobile wheeled inverted pendulum. IEEE Trans Control Syst Technol 21(5):1607–1617

    Article  Google Scholar 

  14. Cui R, Guo J, Mao Z (2015) Adaptive backstepping control of wheeled inverted pendulums models. Nonlinear Dynamics 79(1):501–511

    Article  MathSciNet  MATH  Google Scholar 

  15. Yoshida K, Sekikawa M, Hosomi K (2016) Nonlinear analysis on purely mechanical stabilization of a wheeled inverted pendulum on a slope. Nonlinear Dynamics 83(1–2):905–917

    Article  MathSciNet  MATH  Google Scholar 

  16. K Xia, Z Liu. Renal segmentation algorithm combined low-level features with deep coding feature[C] 27th IEEE International Conference on Robot and Human Interactive Communication, (RO-MAN 2018)

  17. Jiang Y, Chung F-L, Wang S, Deng Z, Wang J, Qian P (2015) Collaborative fuzzy clustering from multiple weighted views. IEEE Transactions on Cybernetics 45(4):688–701

    Article  Google Scholar 

  18. Xia K-j, Yin H-s, Zhang Y-d (2019) Deep semantic segmentation of kidney and space-occupying lesion area based on SCNN and ResNet models combined with SIFT-flow algorithm. J Med Syst 43:2.https://doi.org/10.1007/s10916-018-1116-1

    Article  Google Scholar 

  19. Jiang Y, Chung F-L, Ishibuchi H et al (2015) Multitask TSK fuzzy system modeling by mining intertask common hidden structure. IEEE Transactions on Cybernetics 45(3):548–561

    Article  Google Scholar 

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Funding

This work was financially supported by Science and Technology Innovation Talents Special Foundation of Harbin (no. 2014RFXXJ051); open research project of Anhui simulation design and modern manufacture engineering technology research center (no. SGCZXYB02); Science and Technology Innovation Talents Special Foundation of Harbin (outstanding academic leaders) (no. 2014RFXXJ051).

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Authors and Affiliations

  1. College of Automation Harbin University of Science and Technology, Harbin, 150080, Heilongjiang, China

    Qingwen Qian & Junfeng Wu

  2. College of Mechanical and Electrical Engineering, Huangshan University, Huangshan, 245041, Anhui, China

    Qingwen Qian & Zhe Wang

Authors
  1. Qingwen Qian
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  2. Junfeng Wu
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  3. Zhe Wang
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Correspondence to Junfeng Wu.

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Qian, Q., Wu, J. & Wang, Z. Optimal path planning for two-wheeled self-balancing vehicle pendulum robot based on quantum-behaved particle swarm optimization algorithm. Pers Ubiquit Comput 23, 393–403 (2019). https://doi.org/10.1007/s00779-019-01216-1

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  • DOI: https://doi.org/10.1007/s00779-019-01216-1

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