Robot Time-Optimal Trajectory Planning Based on Quintic Polynomial Interpolation and Improved Harris Hawks Algorithm

被引:7
|
作者
Xu, Jing [1 ,2 ]
Ren, Chaofan [2 ]
Chang, Xiaonan [2 ]
机构
[1] Jiangsu Univ Sci & Technol, Marine Equipment & Technol Inst, 2 Mengxi Rd, Zhenjiang 212003, Peoples R China
[2] Jiangsu Univ Sci & Technol, Sch Mech Engn, 666 Changhui Rd, Zhenjiang 212114, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
time-optimal trajectory planning; quintic polynomial interpolation; Harris hawks algorithm; nonlinear energy decrement strategy; OPTIMIZATION;
D O I
10.3390/axioms12030245
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Time-optimal trajectory planning is one of the most important ways to improve work efficiency and reduce cost and plays an important role in practical application scenarios of robots. Therefore, it is necessary to optimize the running time of the trajectory. In this paper, a robot time-optimal trajectory planning method based on quintic polynomial interpolation and an improved Harris hawks algorithm is proposed. Interpolation with a quintic polynomial has a smooth angular velocity and no acceleration jumps. It has widespread application in the realm of robot trajectory planning. However, the interpolation time is usually obtained by testing experience, and there is no unified criterion to determine it, so it is difficult to obtain the optimal trajectory running time. Because the Harris hawks algorithm adopts a multi-population search strategy, compared with other swarm intelligent optimization algorithms such as the particle swarm optimization algorithm and the fruit fly optimization algorithm, it can avoid problems such as single population diversity, low mutation probability, and easily falling into the local optimum. Therefore, the Harris hawks algorithm is introduced to overcome this problem. However, because some key parameters in HHO are simply set to constant or linear attenuation, efficient optimization cannot be achieved. Therefore, the nonlinear energy decrement strategy is introduced in the basic Harris hawks algorithm to improve the convergence speed and accuracy. The results show that the optimal time of the proposed algorithm is reduced by 1.1062 s, 0.5705 s, and 0.3133 s, respectively, and improved by 33.39%, 19.66%, and 12.24% compared with those based on particle swarm optimization, fruit fly algorithm, and Harris hawks algorithms, respectively. In multiple groups of repeated experiments, compared with particle swarm optimization, the fruit fly algorithm, and the Harris hawks algorithm, the computational efficiency was reduced by 4.7019 s, 1.2016 s, and 0.2875 s, respectively, and increased by 52.40%, 21.96%, and 6.30%. Under the optimal time, the maximum angular displacement, angular velocity, and angular acceleration of each joint trajectory meet the constraint conditions, and their average values are only 75.51%, 38.41%, and 28.73% of the maximum constraint. Finally, the robot end-effector trajectory passes through the pose points steadily and continuously under the cartesian space optimal time.
引用
收藏
页数:20
相关论文
共 50 条
  • [1] Robot Time-Optimal Trajectory Planning Based on Improved Cuckoo Search Algorithm
    Wang, Wenjie
    Tao, Qing
    Cao, Yuting
    Wang, Xiaohua
    Zhang, Xu
    IEEE ACCESS, 2020, 8 : 86923 - 86933
  • [2] Time-optimal trajectory planning of robot based on improved adaptive genetic algorithm
    Yu, Hai
    Meng, Qingxi
    Zhang, Jiayan
    Feng, Xugang
    PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC), 2018, : 6397 - 6402
  • [3] Time-Optimal Trajectory Planning of Industrial Robot based on Improved Particle Swarm Optimization Algorithm
    Shi, Buhai
    Xu, Jiaxiang
    PROCEEDINGS OF THE 39TH CHINESE CONTROL CONFERENCE, 2020, : 3683 - 3688
  • [4] Time-Optimal Trajectory Planning for Delta Robot Based on Quintic Pythagorean-Hodograph Curves
    Su, Tingting
    Cheng, Long
    Wang, Yunkuan
    Liang, Xu
    Zheng, Jun
    Zhang, Haojian
    IEEE ACCESS, 2018, 6 : 28530 - 28539
  • [5] Time-optimal trajectory planning based on improved adaptive genetic algorithm
    孙农亮
    王艳君
    JournalofMeasurementScienceandInstrumentation, 2012, 3 (02) : 103 - 108
  • [6] An improved PSO algorithm for time-optimal trajectory planning of Delta robot in intelligent packaging
    Liu, Cheng
    Cao, Guo-Hua
    Qu, Yong-Yin
    Cheng, Yan-Ming
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2020, 107 (3-4): : 1091 - 1099
  • [7] An improved PSO algorithm for time-optimal trajectory planning of Delta robot in intelligent packaging
    Cheng LIU
    Guo-Hua CAO
    Yong-Yin QU
    Yan-Ming CHENG
    The International Journal of Advanced Manufacturing Technology, 2020, 107 : 1091 - 1099
  • [8] Time-optimal Trajectory Planning of Dulcimer Music Robot Based on PSO Algorithm
    Zhang, Weimin
    Fu, Shixiong
    PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020), 2020, : 4769 - 4774
  • [9] Time-Optimal Trajectory Planning for Industrial Robot based on Improved Hybrid-PSO
    Shi, Buhai
    Zeng, Haifeng
    2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC), 2021, : 3888 - 3893
  • [10] Time-Optimal Trajectory Planning for Industrial Robot
    Chen Weihua
    Zhang Tie
    Zou Yanbiao
    ISTM/2009: 8TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1-6, 2009, : 2847 - 2850