六自由度工业机器人轨迹规划算法的研究

发布时间：2018-03-30 04:06

本文选题：六自由度机器人　切入点：轨迹规划　出处：《华北电力大学》2017年硕士论文

【摘要】：随着现代工业生产自动化程度的不断提升,工业机器人在各行各业的应用越来越广泛。本文以REBOT-V-6R-650六自由度机器人为研究对象,对其运动学、轨迹路径进行了分析研究和仿真,主要研究内容如下所示:第一,采用D-H法建立六自由度机器人的数学模型,求出其正、逆解的解析式,为六自由度机器人运动路径轨迹的研究建立数学基础。第二,对六自由度机器人的轨迹进行了规划。为减小机器人运动过程中的振动和冲击,延长机器人使用寿命,提高轨迹规划的精度,针对采用五次多项式样条规划的轨迹因加加速度有突变而导致的轨迹精度不高的问题,采用五次和七次非均匀B样条对机器人轨迹进行规划。仿真结果表明,采用五次和七次非均匀B样条规划的运动轨迹速度、加速度光滑,加加速度连续没有突变,机器人轨迹精度较好,运动性能优于五次多项式样条。第三,对六自由度机器人的运动路径进行了优化。以某汽车车门焊点路径为例进行分析研究,以改进的粒子群算法对焊接路径进行优化。通过个体极值追随全局极值和随机原始参考值以贪婪重组的方式重新生成粒子,在增强算法局部寻优能力的同时加快算法的收敛速度;采用多次局部调序的策略,通过随机调整粒子局部排列序,保证算法种群的多样性,防止陷入局部最优解。实验仿真结果表明,优化后的路径要优于优化前的路径。第四,基于MATLAB平台建立了六自由度机器人仿真平台。通过在MATLAB中编写的函数建立六自由度机器人的三维模型,建立了基础运动控制模块、示教模块和函数控制模块,为六自由度机器的运动路径轨迹的分析研究提供仿真平台。第五,分别在C空间和笛卡尔空间对机器人的避障路径进行了研究。在C空间中,建立机器人避障模型,通过改进的遗传算法在栅格空间中搜索避障最优路径。在笛卡尔空间中,建立了机械臂本体与障碍物碰撞检测模块,并采用六条非均匀B样条分别对六个关节的运动路径进行规划,采用遗传算法优化非均匀B样条的节点参数,求出较优的避障规划解,并在仿真平台和机器人实体上进行分析验证。
[Abstract]:With the increasing automation of modern industrial production, industrial robots are used more and more widely in various industries. In this paper, the kinematics and trajectory path of REBOT-V-6R-650 six-DOF robot are analyzed and simulated. The main research contents are as follows: first, the mathematical model of 6-DOF robot is established by D-H method, and the analytical formulas of its forward and inverse solutions are obtained, which establishes the mathematical foundation for the study of the trajectory of 6-DOF robot's motion path. In order to reduce the vibration and impact during the robot movement, prolong the service life of the robot and improve the precision of trajectory planning, the trajectory of the robot with six degrees of freedom is planned. In order to solve the problem that the trajectory with polynomial spline programming is not accurate due to the sudden change of acceleration, the fifth and seventh nonuniform B-splines are used to plan the trajectory of the robot. The simulation results show that, The trajectory velocity of the robot is smooth, the acceleration is smooth, the acceleration is continuous without mutation, the trajectory accuracy of the robot is better, and the motion performance is better than the polynomial spline of the fifth degree by using the nonuniform B-spline programming of the fifth and seventh degrees. Third, The motion path of a six-degree-of-freedom robot is optimized. The solder joint path of a car door is taken as an example. The improved particle swarm optimization algorithm is used to optimize the welding path. The particle is regenerated by greedy recombination of individual extremum following global extremum and random original reference value. The local optimization ability of the algorithm is enhanced and the convergence rate of the algorithm is speeded up. The local order of particles is adjusted randomly to ensure the diversity of the algorithm population. The experimental results show that the optimized path is better than that before optimization. Fourth, The simulation platform of six-degree-of-freedom robot is established based on MATLAB platform. The three-dimensional model of six-degree-of-freedom robot is built by the function written in MATLAB, and the basic motion control module, teaching module and function control module are established. This paper provides a simulation platform for the analysis and study of the trajectory of motion path of 6-DOF machine. Fifth, the obstacle avoidance path of robot is studied in C space and Descartes space, respectively. In C space, the obstacle avoidance model of robot is established. The improved genetic algorithm is used to search the optimal path of obstacle avoidance in grid space. In Descartes space, the collision detection module of robot arm body and obstacle is established. Six non-uniform B-splines are used to plan the motion paths of the six joints, and genetic algorithm is used to optimize the node parameters of the non-uniform B-spline, and the optimal obstacle avoidance programming solution is obtained. The simulation platform and robot entity are analyzed and verified.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP242.2

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