改进粒子群算法的自动充电机械臂时间最优轨迹研究

朱浩; 赵清海; 郑群锋; 宁长久

doi:10.13433/j.cnki.1003-8728.20220271

改进粒子群算法的自动充电机械臂时间最优轨迹研究

doi: 10.13433/j.cnki.1003-8728.20220271

朱浩^1,,
赵清海^{1, 2, ,},
郑群锋³,
宁长久³

1.
青岛大学机电工程学院，山东青岛　266071
2.
青岛大学电动汽车智能化动力集成技术国家地方联合工程研究中心，山东青岛　266071
3.
北京理工大学机械与车辆学院，北京　100081

基金项目: 国家自然科学基金项目（52175236）

详细信息

作者简介:
朱浩，硕士研究生，1448346371@qq.com

通讯作者:
赵清海，副教授，博士，zqhbit@163.com

中图分类号: TH122
计量
- 文章访问数: 47
- HTML全文浏览量: 20
- PDF下载量: 0
- 被引次数: 0
出版历程
- 收稿日期: 2022-03-05
- 刊出日期: 2024-03-25

Exploring Time-optimal Trajectory of Automatic Charging Manipulator with Improved Particle Swarm Optimization Algorithm

1.
School of Mechanical and Electrical Engineering, Qingdao University, Qingdao 266071, Shandong, China
2.
National Engineering Research Center for Intelligent Electrical Vehicle Power System, Qingdao University, Qingdao 266071, Shandong, China
3.
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

摘要

摘要: 针对桁架充电机械臂关节空间轨迹规划的时间优化问题，提出了一种非线性动态学习因子的粒子群算法。通过运动学分析获取工作空间，引入3-5-3多项式插值进行轨迹规划。结合运动过程中的速度与加速度约束，寻求运动过程中的最短时间。对比改进粒子群算法和基本粒子群算法的收敛速度，分析各关节优化前后运动时间的变化情况，并进行仿真实验验证。结果表明：改进粒子群算法的收敛性能较基本粒子群算法更快，整体运动时间缩短约33%，证实改进粒子群算法的可行性。
- 桁架充电机械臂 /
- 时间优化 /
- 非线性动态学习因子 /
- 粒子群算法
Abstract: A particle swarm optimization (PSO) algorithm based on the nonlinear dynamic learning factor was proposed to solve the time optimization problem in the joint space trajectory planning of a truss charging manipulator. The workspace was obtained through kinematic analysis, and the 3-5-3 polynomial interpolation was introduced for the trajectory planning. The shortest motion time was sought through combining velocity constraints with acceleration constraints. The convergence speed of the improved PSO algorithm was compared with that of the basic PSO algorithm, and the variation of motion time of each joint before and after optimization was analyzed. The simulation results show that the convergence performance of the improved PSO algorithm is faster than that of the basic PSO algorithm and that the overall motion time is shortened by about 33%, confirming the feasibility of the improved PSO algorithm.
- truss charging manipulator /
- time optimization /
- nonlinear dynamic learning factor /
- particle swarm optimization algorithm

HTML全文

图 1 桁架充电机械臂三维模型

Figure 1. Three-dimensional model of truss charging arm

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图 2 桁架充电机器人改进D-H坐标系

Figure 2. Improved D-H coordinate system for trusscharging robot

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图 3 桁架充电机机械臂工作区域分析

Figure 3. Analysis of the working area of the truss charging arm

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图 4 改进粒子群轨迹时间优化流程图

Figure 4. Improved particle swarm trajectory time optimization flow chart

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图 5 桁架充电机械臂时间优化实验平台

Figure 5. Experimental platform for time optimization of truss charging robot arm

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图 6 桁架充电机械臂运动学三维模型

Figure 6. Three dimensional kinematic model of truss charging robotic arm

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图 7 关节1 ~ 5最优粒子位置迭代各曲线时间分布

Figure 7. Time distribution of the optimal particle position iteration curves for joints 1-5

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图 8 关节1 ~ 5的两种算法收敛过程对比图

Figure 8. Comparison of convergence processes between two algorithms for joints 1-5

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图 9 优化前后位置、速度与加速度对比

Figure 9. Comparison of position, velocity, and acceleration before and after optimization

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图 10 实验与仿真时间优化结果对比图

Figure 10. Comparison of experimental and simulation time optimization results

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表 1 D-H参数表

Table 1. D-H parameters

关节 ${\theta _i}$ d_i a_i−1 ${\alpha _{i - 1}}$ q_i

1 −90° 0 0 −90° d₁

2 90° 0 0 90° d₂

3 −90° 0 0 −90° d₃

4 0 0.03 m 0 0 q₄

5 0 0.10 m 0 90° q₅

下载: 导出CSV

表 2 各关节空间运动变量插值点

Table 2. Interpolation points of motion variables in each joint space

关节初始点路径点1 路径点2 终止点

1 0 0.8 m 1.7 m 2.5 m

2 0 1.05 m 2.10 m 2.75 m

3 0 0.6 m 1.1 m 1.8 m

4 0 18° 36° 90°

5 0 15° 40° 90°

下载: 导出CSV

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