工业机器人上层控制.doc
约72页DOC格式手机打开展开
工业机器人上层控制,摘 要随着科学技术的不断进步，工业机器人相关技术也在不断向前发展，控制系统作为机器人的大脑已成为工业机器人领域的一个热门研究方向。为此，本文重点研究了控制系统中的轨迹规划部分，使用五次均匀b样条函数插值关键路径点，同时使用混合遗传算法处理机器人轨迹的相关约束，最终实现了兼顾时间最优与冲击最优的二次轨迹规划方案。首先，...
内容介绍
此文档由会员 违规屏蔽12 发布
摘 要
随着科学技术的不断进步，工业机器人相关技术也在不断向前发展，控制系统作为机器人的大脑已成为工业机器人领域的一个热门研究方向。为此，本文重点研究了控制系统中的轨迹规划部分，使用五次均匀B样条函数插值关键路径点，同时使用混合遗传算法处理机器人轨迹的相关约束，最终实现了兼顾时间最优与冲击最优的二次轨迹规划方案。
首先，本文全面总结了国内外有关轨迹规划的研究成果，详细分析了三种常见的轨迹规划优化方案——最优时间轨迹规划、最优能量轨迹规划和最优冲击轨迹规划的优缺点。最优时间轨迹规划没有考虑到冲击因素的影响，对冲击还能作进一步优化，因此本文提出了一种二次轨迹规划方案，即先通过最优时间轨迹规划得到最小执行时间，然后在最小执行时间内进行最优冲击轨迹规划，进而规划出一条既高效又平滑的运动轨迹。同时考虑到最优轨迹规划比较耗时，因此采用离线规划在线插值的策略。
然后，本文使用MATLAB配合Robotics Toolbox工具箱对二次轨迹规划方案进行了离线仿真。由于五次均匀B样条曲线不仅能够保证了各关节速度和加速度连续性还能保证了各关节冲击的连续性，连续平滑的冲击可以减少机械振动，延长机器人的工作寿命，因此本文选用B样条函数轨迹规划的插值函数。由于普通的遗传算法只能有效的处理不等式约束，对等式约束的处理效果不好，而罚函数法却能够很好的处理等式约束，所以本文结合两种优化算法的优点提出一种混合遗传算法，有效解决了轨迹规划中运动学与动力学约束。选用PUMA560为对象进行仿真与实验，结果表明，该方案可以获得比较理想的机器人运动轨迹，所提出的混合遗传算法能有效提高全局寻优的性能和算法运行的稳定性。
最后，本文在TI的OMAP3530平台上进行在线插值。对OMAP3530平台的软件架构进行了深入研究，掌握了从ARM端调用DSP端插值算法的整个流程，即使用Codec Engine进行算法调用，DSPLink负责数据在CMEM分配的共享缓存中交互，整个过程还需LPM对DSP进行电源管理。
关键字 工业机器人；轨迹规划；混合遗传算法；B样条；OMAP3530
Abstract
With the improvement of science and technology, the industrial robot is also developed quickly, and control system as the robot’s brain has become a popular research direction in the field of industrial robot. Therefore, this paper mainly focuses on the research of trajectory planning in the control system by adopting fifth-order uniform B-splines to interpolate a sequence of critical path points and using the hybrid genetic algorithm to deal with the constraints of robot’s trajectory, and finally the quadratic trajectory planning which achieves the time-optimal and the jerk-optimal is implemented.
First of all, the research results of the trajectory planning at home and broad is summarized in this paper, and the advantages and disadvantages of three optimization method of trajectory planning - the time-optimal trajectory planning, the energy-optimal trajectory planning and the jerk-optimal trajectory planning are analyzed in detail. The time-optimal trajectory planning doesn't take into account the effect of the jerk. In order to do further optimization of the jerk, a quadratic trajectory planning method is put forward in this paper. That is to say, the trajectory with minimum execution time is obtained by using time-optimal trajectory planning, then an efficient and smooth trajectory is generated in this minimum execution time by using jerk-optimal trajectory planning. Moreover, the strategy of planning offline and interpolation online is used in this paper.
Then, the quadratic trajectory planning method is simulated offline by using MATLAB and Robotics Toolbox. The velocity, acceleration and jerk of each joint trajectorys interpolated by B-splines are continuous, and the continuous and smooth jerk can reduce mechanical vibration and also extend the working life of the robot, so this paper chooses fifth-order uniform B-splines as the interpolation function. Genetic algorithm can effectively deal with inequality constraints, but it has bad effects on equality constraints. the penalty function is a good algorithm to solve the problem of equality constraints. So a hybrid genetic algorithm is put forward in this paper by combining the advantages of the two kinds of optimization algorithms to resolve the kinematics and dynamics constraints of trajectory planning. The method is applied to PUMA560, the result of simulation and experiment shows that the method can provide ideal trajectory and the hybrid genetic algorithm is more efficient and stable.
Finally, the interpolation algorithm is running on the TI OMAP3530 platform. The software of OMAP3530 is researched in depth in this paper. The entire process of calling interpolation algorithm from the ARM to the DSP is well knew. Codec Engine is used to call algorithm from the ARM to the DSP. DSPLink is responsible for data exchange in the Shared buffer distributed by CMEM, and the whole process also need LPM to manage power supply of the DSP.
Keywords industrial robot；trajectory planning；hybrid genetic algorithm；B-Spline；OMAP3530
目 录
摘要 I
Abstract Ⅲ-b..
随着科学技术的不断进步，工业机器人相关技术也在不断向前发展，控制系统作为机器人的大脑已成为工业机器人领域的一个热门研究方向。为此，本文重点研究了控制系统中的轨迹规划部分，使用五次均匀B样条函数插值关键路径点，同时使用混合遗传算法处理机器人轨迹的相关约束，最终实现了兼顾时间最优与冲击最优的二次轨迹规划方案。
首先，本文全面总结了国内外有关轨迹规划的研究成果，详细分析了三种常见的轨迹规划优化方案——最优时间轨迹规划、最优能量轨迹规划和最优冲击轨迹规划的优缺点。最优时间轨迹规划没有考虑到冲击因素的影响，对冲击还能作进一步优化，因此本文提出了一种二次轨迹规划方案，即先通过最优时间轨迹规划得到最小执行时间，然后在最小执行时间内进行最优冲击轨迹规划，进而规划出一条既高效又平滑的运动轨迹。同时考虑到最优轨迹规划比较耗时，因此采用离线规划在线插值的策略。
然后，本文使用MATLAB配合Robotics Toolbox工具箱对二次轨迹规划方案进行了离线仿真。由于五次均匀B样条曲线不仅能够保证了各关节速度和加速度连续性还能保证了各关节冲击的连续性，连续平滑的冲击可以减少机械振动，延长机器人的工作寿命，因此本文选用B样条函数轨迹规划的插值函数。由于普通的遗传算法只能有效的处理不等式约束，对等式约束的处理效果不好，而罚函数法却能够很好的处理等式约束，所以本文结合两种优化算法的优点提出一种混合遗传算法，有效解决了轨迹规划中运动学与动力学约束。选用PUMA560为对象进行仿真与实验，结果表明，该方案可以获得比较理想的机器人运动轨迹，所提出的混合遗传算法能有效提高全局寻优的性能和算法运行的稳定性。
最后，本文在TI的OMAP3530平台上进行在线插值。对OMAP3530平台的软件架构进行了深入研究，掌握了从ARM端调用DSP端插值算法的整个流程，即使用Codec Engine进行算法调用，DSPLink负责数据在CMEM分配的共享缓存中交互，整个过程还需LPM对DSP进行电源管理。
关键字 工业机器人；轨迹规划；混合遗传算法；B样条；OMAP3530
Abstract
With the improvement of science and technology, the industrial robot is also developed quickly, and control system as the robot’s brain has become a popular research direction in the field of industrial robot. Therefore, this paper mainly focuses on the research of trajectory planning in the control system by adopting fifth-order uniform B-splines to interpolate a sequence of critical path points and using the hybrid genetic algorithm to deal with the constraints of robot’s trajectory, and finally the quadratic trajectory planning which achieves the time-optimal and the jerk-optimal is implemented.
First of all, the research results of the trajectory planning at home and broad is summarized in this paper, and the advantages and disadvantages of three optimization method of trajectory planning - the time-optimal trajectory planning, the energy-optimal trajectory planning and the jerk-optimal trajectory planning are analyzed in detail. The time-optimal trajectory planning doesn't take into account the effect of the jerk. In order to do further optimization of the jerk, a quadratic trajectory planning method is put forward in this paper. That is to say, the trajectory with minimum execution time is obtained by using time-optimal trajectory planning, then an efficient and smooth trajectory is generated in this minimum execution time by using jerk-optimal trajectory planning. Moreover, the strategy of planning offline and interpolation online is used in this paper.
Then, the quadratic trajectory planning method is simulated offline by using MATLAB and Robotics Toolbox. The velocity, acceleration and jerk of each joint trajectorys interpolated by B-splines are continuous, and the continuous and smooth jerk can reduce mechanical vibration and also extend the working life of the robot, so this paper chooses fifth-order uniform B-splines as the interpolation function. Genetic algorithm can effectively deal with inequality constraints, but it has bad effects on equality constraints. the penalty function is a good algorithm to solve the problem of equality constraints. So a hybrid genetic algorithm is put forward in this paper by combining the advantages of the two kinds of optimization algorithms to resolve the kinematics and dynamics constraints of trajectory planning. The method is applied to PUMA560, the result of simulation and experiment shows that the method can provide ideal trajectory and the hybrid genetic algorithm is more efficient and stable.
Finally, the interpolation algorithm is running on the TI OMAP3530 platform. The software of OMAP3530 is researched in depth in this paper. The entire process of calling interpolation algorithm from the ARM to the DSP is well knew. Codec Engine is used to call algorithm from the ARM to the DSP. DSPLink is responsible for data exchange in the Shared buffer distributed by CMEM, and the whole process also need LPM to manage power supply of the DSP.
Keywords industrial robot；trajectory planning；hybrid genetic algorithm；B-Spline；OMAP3530
目 录
摘要 I
Abstract Ⅲ-b..
