Manipulator Trajectory Planning based on ADPSO Algorithm

Xiaohong Tang; Yongjian Gong; Nianjiao Wang; Hong Zhang; Leilei Ren

doi:10.16578/j.issn.1004.2539.2022.05.017

您当前的位置：

首页 >

文章列表页 >

Manipulator Trajectory Planning based on ADPSO Algorithm

Test·Analysis | 更新时间：2022-09-24

- Manipulator Trajectory Planning based on ADPSO Algorithm
- Journal of Mechanical Transmission Vol. 46, Issue 5, Pages: 123-129(2022)
- 作者机构：
  
  1.中南林业科技大学机电工程学院，湖南长沙 410004
  2.湖南理工学院机械工程学院，湖南岳阳 414000
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2022.05.017
  CLC：
扫描看全文
汤小红,龚永健,王念娇等.基于ADPSO算法的机械臂轨迹规划[J].机械传动,2022,46(05):123-129.

Tang Xiaohong,Gong Yongjian,Wang Nianjiao,et al.Manipulator Trajectory Planning based on ADPSO Algorithm[J].Journal of Mechanical Transmission,2022,46(05):123-129.
汤小红,龚永健,王念娇等.基于ADPSO算法的机械臂轨迹规划[J].机械传动,2022,46(05):123-129. DOI： 10.16578/j.issn.1004.2539.2022.05.017.

Tang Xiaohong,Gong Yongjian,Wang Nianjiao,et al.Manipulator Trajectory Planning based on ADPSO Algorithm[J].Journal of Mechanical Transmission,2022,46(05):123-129. DOI： 10.16578/j.issn.1004.2539.2022.05.017.

摘要

焊接机械臂工作路径复杂，对规划轨迹平滑性要求较高，并且规划轨迹需满足各关节运动学约束。提出了带扰动的自适应粒子群（Adaptive particle swarm optimization，ADPSO）算法，可以在满足关节约束条件下规划出时间、能力、跃度最优轨迹。采用5次NURBS曲线插值关节工作路径点，使各关节位置、速度、加速度、跃度曲线均连续光滑。利用ADPSO算法进行多目标最优轨迹规划，首先，将粒子外推思想与粒子群优化（Particle swarm optimization，PSO）算法结合，以增强粒子搜索能力；然后，对搜索所得个体极值与群体极值引入扰动，加快粒子收敛速度。在 Matlab环境下进行仿真分析，对比其他智能算法，ADPSO算法的优化效果更好、优化时效性更快。

Abstract

The working path of welding manipulator is complex，which requires high smoothness of the planning trajectory，and the planning trajectory needs to meet the kinematics constraints of each joint. An adaptive particle swarm optimization （ADPSO） algorithm with disturbance is proposed，which can plan the optimal trajectory of time，ability and jump under joint constraints. The quintic NURBS curve is used to interpolate the joint working path points，so that the joint position，velocity，acceleration and jump curves are continuous and smooth. The ADPSO algorithm is used for multi-objective optimal trajectory planning. Firstly，the idea of particle extrapolation is combined with particle swarm optimization （PSO） algorithm to enhance the ability of particle search，and then disturbance is introduced to the individual extremum and group extremum to accelerate the convergence speed of particles. Simulation analysis is carried out in Matlab environment， compared with other intelligent algorithms，ADPSO algorithm has better optimization effect and faster optimization timeliness.

关键词

机械臂轨迹规划5次NURBS曲线自适应粒子群优化算法多目标优化

Keywords

ManipulatorTrajectory planningQuintic NURBS curveAdaptive particle swarm optimization algorithmMultiobjective optimization

references

乐英，库巍，卢艺，等.基于优化的六自由度工业机器人NURBS轨迹规划［J］.组合机床与自动化加工技术，2017（11）：41-43.

LE Ying，KU Wei，LU Yi，et al.NURBS trajectory planning of six-degree-of-freedom industrial robot based on optimization［J］.Modular Machine Tool and Automatic Manufacturing Technology，2017（11）：41-43.

李小霞，汪木兰，刘坤，等.基于五次B样条的机械手关节空间平滑轨迹规划［J］.组合机床与自动化加工技术，2012（8）：39-42.

LI Xiaoxia，WANG Mulan，LIU Kun，et al.Smooth trajectory planning of manipulator joint space based on quintic B-spline［J］.Modular Machine Tool and Automatic Manufacturing Technology，2012（8）：39-42.

张秀林.基于遗传算法的机械臂时间最优轨迹规划［D］.兰州：兰州理工大学，2014：65-74.

ZHANG Xiulin.Time optimal trajectory planning of manipulator based on genetic algorithm［D］.Lanzhou：Lanzhou University of Technology，2014：65-74.

万传恒.六自由度工业机器人轨迹规划算法研究［D］.广州：华南理工大学，2012：33-62.

WAN Chuanheng.Research on the trajectory planning algorithm of six-degree-of-freedom industrial robot［D］.Guangzhou：South China University of Technology，2012：33-62.

张淦.基于改进型人工鱼群算法的机器人轨迹优化研究［D］.重庆：重庆交通大学，2016：48-61.

ZHANG Gan.Research on robot trajectory optimization based on improved artificial fish swarm algorithm［D］.Chongqing：Chongqing Jiaotong University，2016：48-61.

王玉宝，王诗宇，李备备，等.一种改进粒子群的工业机器人时间最优轨迹规划算法［J］.小型微型计算机系统，2018，39（8）：1878-1881.

WANG Yubao，WANG Shiyu，LI Beibei，et al.A time-optimal trajectory planning algorithm for industrial robots with improved particle swarms［J］.Small Microcomputer System，2018，39（8）：1878-1881.

JIN R Y，ROCCO P，GENG Y H.Cartesian trajectory planning of space robots using a multi-objective optimizatio［J］.Aerospace Science and Technology，2021，108：106360.

TILLER W.The nurbs book［J］.Computer-Aided Design，1996，28（8）：665-666.

林锋，汪地.三次非均匀B样条曲线插补算法研究［J］.组合机床与自动化加工技术，2012（8）：32-35.

LIN Feng，WANG Di.Research on interpolation algorithm of cubic non-uniform B-spline curve［J］.Modular Machine Tool and Automated Processing Technology，2012（8）：32-35.

LIANG C，HU C，GUO Z，et al.Improvement of original particle swarm optimization algorithm based on simulated annealing algorithm［C］//Proceedings of the 2008 27th Chinese Control Conference，July 16-18，2008，Kunming，Yunnan，China.New York：IEEE，2008：671-676.

赵嘉，吕莉，孙辉.自适应精英反向学习的粒子群优化算法［J］.小型微型计算机系统，2015，36（9）：2166-2171.

ZHAO Jia，LÜ Li，SUN Hui.Particle swarm optimization algorithm for adaptive elite back learning［J］.Small Microcomputer System，2015，36（9）：2166-2171.

谷晓琳，黄明，梁旭，等.一种改进惯性权重的混沌粒子群优化算法［J］.大连交通大学学报，2020，41（3）：102-106.

GU Xiaolin，HUANG Ming，LIANG Xu，et al.A chaotic particle swarm optimization algorithm with improved inertia weights［J］.Journal of Dalian Jiaotong University，2020，41（3）：102-106.

赵秋月.基于认知种群的混合粒子群优化算法研究［D］.广州：广州大学，2019：60.

ZHAO Qiuyue.Research on hybrid particle swarm optimization algorithm based on cognitive population［D］.Guangzhou：Guangzhou University，2019：60.

王鹏飞，杜忠华，牛坤，等.基于改进粒子群算法的倒立摆LQR优化控制［J］.计算机仿真，2021，38（2）：220-224.

WANG Pengfei，DU Zhonghua，NIU Kun，et al.LQR optimization control of inverted pendulum based on improved particle swarm algorithm［J］.Computer Simulation，2021，38（2）：220-224.

李俊，汪冲，李波，等.基于扰动的精英反向学习粒子群优化算法［J］.计算机应用研究，2016，33（9）：2584-2587.

LI Jun，WANG Chong，LI Bo，et al.Elite inverse learning particle swarm optimization algorithm based on disturbance［J］.Computer Application Research，2016，33（9）：2584-2587.

ARUMUGAM MS，RAO MVC，TAN AWC.A novel and effective particle swarm optimization like algorithm with extrapolation technique［J］.Applied Soft Computing Journal，2008，9（1）：308-320.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Trajectory Planning of Time Optimal Manipulator based on Complex Method

A Review of the Space Trajectory Planning of Redundant Manipulator

Design and Motion Analysis of an Automatic Retractable Manipulator for a Vehicle-mounted Wheelchair

Gait Analysis and Trajectory Planning of a Hybrid Dual-platform Staggered Hexapod Robot

Trajectory Planning of Camellia Oleifera Pollen Picking Manipulators Based on an Improved Particle Swarm Optimization Algorithm

Related Author

No data

Related Institution

School of Mechanical Engineering，University of South China

State Key Laboratory of Robotics and System,Harbin Institute of Technology

School of Mechanical Engineering,Hebei University of Technology

College of Mechanical and Vehicle Engineering, Hunan University

School of Mechanical Engineering, Tiangong University

⁰