High-speed parallel robots have been extensively utilized in the light industry.However,the influence of the nonlinear dynamic characteristics of high-speed parallel robots on system’s dynamic response and stable ope...High-speed parallel robots have been extensively utilized in the light industry.However,the influence of the nonlinear dynamic characteristics of high-speed parallel robots on system’s dynamic response and stable operation cannot be ignored during the high-speed reciprocating motion.Thus,trajectory planning is essential for efficiency and stability from pick-and-place(PAP)actions.This paper presents a method for planning the equal-height pick-and-place trajectory considering velocity constraints to improve the PAP efficiency and stability of high-speed parallel robots.The velocity constraints in the start-and-end points can reduce vibration from picking and placing,making the trajectory more suitable to complex beltline situations.Based on velocity constraints,trajectory optimization includes trajectory smoothness and joint torque to optimize cycle time is carried out.This paper proposes an online trajectory optimization solution.By using back propagation(BP)neural networks,the solution is simplified and can be solved in real-time.Simulation and experiments were carried out on the SR4 parallel robot.The results show that the proposed method improves the efficiency,smoothness,and stability of the robot.This paper proposes an online trajectory planning method which is velocity constraints based and can improve the efficiency and stability of high-speed parallel robots.The work of this research is conducive to finely applying high-speed parallel robots.展开更多
针对轮式移动机器人(Wheeled Mobile Robot-WMR)自身欠驱动的特点,并考虑到实际工作时的各种约束和限制,提出了一种基于模型预测控制(Model Predictive Control–MPC)的多约束轨迹规划方法。可以对车体自身的运动学约束、物理约束以及...针对轮式移动机器人(Wheeled Mobile Robot-WMR)自身欠驱动的特点,并考虑到实际工作时的各种约束和限制,提出了一种基于模型预测控制(Model Predictive Control–MPC)的多约束轨迹规划方法。可以对车体自身的运动学约束、物理约束以及避障等约束进行集中有效的处理,可以生成符合车体自身模型特点并满足各种约束的可行轨迹,充分保证了轮式移动机器人自主行驶的可行性,安全性,高效性。仿真结果充分验证了该方法的有效性。展开更多
基金National Natural Science Foundation of China(Grant Nos.51922057,91948301).
文摘High-speed parallel robots have been extensively utilized in the light industry.However,the influence of the nonlinear dynamic characteristics of high-speed parallel robots on system’s dynamic response and stable operation cannot be ignored during the high-speed reciprocating motion.Thus,trajectory planning is essential for efficiency and stability from pick-and-place(PAP)actions.This paper presents a method for planning the equal-height pick-and-place trajectory considering velocity constraints to improve the PAP efficiency and stability of high-speed parallel robots.The velocity constraints in the start-and-end points can reduce vibration from picking and placing,making the trajectory more suitable to complex beltline situations.Based on velocity constraints,trajectory optimization includes trajectory smoothness and joint torque to optimize cycle time is carried out.This paper proposes an online trajectory optimization solution.By using back propagation(BP)neural networks,the solution is simplified and can be solved in real-time.Simulation and experiments were carried out on the SR4 parallel robot.The results show that the proposed method improves the efficiency,smoothness,and stability of the robot.This paper proposes an online trajectory planning method which is velocity constraints based and can improve the efficiency and stability of high-speed parallel robots.The work of this research is conducive to finely applying high-speed parallel robots.
文摘针对轮式移动机器人(Wheeled Mobile Robot-WMR)自身欠驱动的特点,并考虑到实际工作时的各种约束和限制,提出了一种基于模型预测控制(Model Predictive Control–MPC)的多约束轨迹规划方法。可以对车体自身的运动学约束、物理约束以及避障等约束进行集中有效的处理,可以生成符合车体自身模型特点并满足各种约束的可行轨迹,充分保证了轮式移动机器人自主行驶的可行性,安全性,高效性。仿真结果充分验证了该方法的有效性。