柔性关节机械臂轨迹规划及振动抑制研究

谭加林; 肖正明; 伍星; 郑胜予

doi:10.13433/j.cnki.1003-8728.20200633

柔性关节机械臂轨迹规划及振动抑制研究

doi: 10.13433/j.cnki.1003-8728.20200633

1.
昆明理工大学机电工程学院振动噪声监测与控制研究所, 昆明 650500
2.
云南机电职业技术学院, 昆明 650000

基金项目:

国家自然科学基金项目 51965025

国家重点研发计划项目 2018YFB1306100

云南省重点研发计划国际科技合作专项 202003AF140007

云南省基础研究计划面上项目 202201AT070103

详细信息

作者简介:
谭加林(1997-), 硕士研究生, 研究方向为振动控制, 1172488831@qq.com

通讯作者:
肖正明, 教授, 博士生导师, 5636482@qq.com

中图分类号: TH113
计量
- 文章访问数: 310
- HTML全文浏览量: 135
- PDF下载量: 37
- 被引次数: 0
出版历程
- 收稿日期: 2021-05-12
- 刊出日期: 2023-04-25

Research on Trajectory Planning and Vibration Suppression of Flexible Joint Manipulator

1.
Institute of Monitoring & Control of Vibration & Noise, Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China
2.
Yunnan Vocational College of Mechanical Electrical Technology, Kunming 650000, China

摘要

摘要: 针对工业机器人多自由度复杂机械臂系统, 对其建立多刚体运动学模型, 仿真验证末端运动轨迹的真确性。在此基础上, 对机械臂系统的末端关节进行柔性化处理, 添加随机柔性扰动, 得到刚柔耦合机械臂较为真实的末端轨迹曲线。提出了基于混沌粒子群优化算法(CPSO)的振动抑制方案, 通过CPSO算法对机械臂末端轨迹的插值参数进行优化, 定义了柔性末端的振动变形最小的目标函数, 并给出了具体的求解步骤。数值仿真结果表明, 在满足系统约束条件的情况下, 机械臂运行平稳, 不存在角速度突变的情况, 相比于基本粒子群优化算法, CPSO算法保证了粒子群体的随机性, 提高了群体的多样性, 且收敛速度较快, 不会陷入局部最优, 在CPSO优化下的柔性末端轨迹振动明显减小, 从而说明CPSO算法能够有效优化轨迹规划参数, 减小机械臂柔性末端的振动变形。
- 柔性关节机械臂 /
- 轨迹规划 /
- 振动抑制 /
- CPSO
Abstract: Aiming at the complex multi-freedom manipulator system of industrial robot, the multi-rigid body kinematics model was established to verify the authenticity of the end motion trajectory. On this basis, the end joints of the manipulator system were flexibly processed and random flexible perturbations were added to obtain the real end trajectory curve of the rigid-flexible coupling manipulator. The vibration suppression scheme based on chaotic particle swarm optimization (CPSO) was proposed. The interpolation parameters of the end trajectory of the manipulator were optimized by CPSO algorithm. The objective function of the flexible end was defined to minimize the vibration deformation, and the specific solving steps were given. Numerical simulation results show that under the condition of satisfying the constraints of the system, the manipulator runs smoothly without angular velocity mutation. Compared with the fundamental particle swarm optimization algorithm, the CPSO algorithm ensures the randomness of the particle population, improves the diversity of the population, and has a faster convergence speed without falling into local optimum. Under CPSO optimization, the trajectory vibration of the flexible end of the manipulator is significantly reduced, which indicates that the CPSO algorithm can effectively optimize the trajectory planning parameters and reduce the vibration deformation of the flexible end of the manipulator.
- flexible joint manipulator /
- trajectory planning /
- vibration suppression /
- CPSO

HTML全文

图 1 QJR6-1工业机器人自由度分布

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图 2 柔性关节示意图

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图 3 机械臂末端轨迹

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图 4 数值仿真下的末端轨迹图

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图 5 柔性化前后末端轨迹对比

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图 6 CPSO算法优化流程图

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图 7 PSO与CPSO优化对比

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图 8 CPSO适应度曲线

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图 9 优化前后末端轨迹对比

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表 1 QJR6-1机器人D-H坐标

i	θ_i/(°)	d_i/mm	a_i/mm	α_i/(°)
1	θ₁	0	160	90
2	θ₂	0	580	0
3	θ₃	0	130	90
4	θ₄	688.5	0	90
5	θ₅	0	0	-90
6	θ₆	108	0	0

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表 2 伺服电机参数规格

参数	数值
额定转矩	2.39 NM
额定电流	4 A
线电阻	2.01 Ω
反电势	40 V
转动惯量	1.51×10^-4 kg·m²
额定转速	300 r/min

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表 3 各关节角速度 °/s

jt₁	jt₂	jt₃	jt₄	jt₅	jt₆
36	9	9	0	0	0

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表 4 各关节转角范围

关节i	[θ_imin, θ_imax]
1	[-168°, 168°]
2	[-89°, 150°]
3	[-111°, 87°]
4	[-170°, 170°]
5	[-125°, 125°]
6	[-360°, 360°]

下载: 导出CSV

参考文献(15)

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