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Trajectory Planning of Robot Based on Quantum Genetic Algorithm

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Recent Developments in Mechatronics and Intelligent Robotics (ICMIR 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 690))

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Abstract

There are many possible trajectories between the given points in Euclidean geometry space for industrial robots. Under constraints of robotic kinematics or dynamics, the optimization of robotic running time is required. In this paper, a trajectory planning method of robot based on quantum genetic algorithm is proposed. Firstly, cubic B-spline function and constraint condition of robotic kinematics were introduced. Then, the chromosome and evolutionary update strategy in quantum genetic algorithm were described, and the variable fitness function of trajectory planning was also defined. Finally, the methods and procedures were introduced in detail, and the first three joint trajectories and the quantum genetic algorithm were programmed on MATLAB platform. The simulation experiments showed that trajectories of joint displacement, velocity, acceleration and jerk could be obtained effectively. The results verified the reliability and practicability of the method.

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Author information

Authors and Affiliations

  1. South China University of Technology, Guangzhou, Guangdong, China

    Guo Qingda, Quan Yanming, Liu Peijie & Chen Jianwu

Authors
  1. Guo Qingda
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  2. Quan Yanming
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  3. Liu Peijie
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  4. Chen Jianwu
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Corresponding author

Correspondence to Quan Yanming .

Editor information

Editors and Affiliations

  1. Shenyang Jianzhu University , Shenyang, Liaoning, China

    Feng Qiao

  2. SOA University , Bhubaneswar, Odisha, India

    Srikanta Patnaik

  3. Montclair State University , Montclair, New Jersey, USA

    John Wang

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Qingda, G., Yanming, Q., Peijie, L., Jianwu, C. (2018). Trajectory Planning of Robot Based on Quantum Genetic Algorithm. In: Qiao, F., Patnaik, S., Wang, J. (eds) Recent Developments in Mechatronics and Intelligent Robotics. ICMIR 2017. Advances in Intelligent Systems and Computing, vol 690. Springer, Cham. https://doi.org/10.1007/978-3-319-65978-7_84

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  • DOI: https://doi.org/10.1007/978-3-319-65978-7_84

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65977-0

  • Online ISBN: 978-3-319-65978-7

  • eBook Packages: EngineeringEngineering (R0)

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