工业机器人技术 控制单元编程平台及轨迹规划 制造业机器人系统框架

机器人应用系统总体结构

Optimal trajectory planning for industrial robots

An analysis of the results of an algorithm for optimal trajectory planning of robot manipulators is described in this paper. The objective function to be minimized is a weighted sum of the integral squared jerk and the execution time. Two possible primitives for building the trajectory are considered: cubic splines or fifth-order B-splines. The proposed technique allows to set constraints on the robot motion, expressed as upper bounds on the absolute values of velocity, acceleration and jerk. The described method is then applied to a 6-d.o.f. robot (a Cartesian gantry manipulator with a spherical wrist); the results obtained using the two different primitives are presented and discussed.

Modeling and Identification of an Industrial Robot for Machining Applications Industrial robots represent a promising, cost-saving and flexible alternative for machining applications. Due to the kinematics of a vertical articulated robot the system behavior is quite different compared to a conventional machine tool. This article describes the modeling of the robot structure and the identification of its parameters with focus on the analysis of the system's stiffness and its behavior during the milling process. Therefore a method for the calculation of the Cartesian stiffness based on the polar stiffness and the use of the Jacobian matrix is introduced. Based on the results of the identification and the experimental validation the machining performance of the robot is evaluated and conclusions are drawn.

Motion coordination for industrial robotic systems with redundant degrees of freedom

This work investigates how to distribute in an optimum fashion the desired movement of the end-effector of an industrial robot with respect to the workpiece, when there are redundant degrees of freedom, such as a positioning table. The desired motion is given as a series of acceleration functions in respective time intervals. The constraints of the optimisation are the available acceleration limit of axes, such as the table axes, the upper bounds to velocity and displacement of each axis and the avoidance of singular point areas of the robot, as defined by its manufacturer. The optimisation criterion is minimum total work for the motion. A genetic algorithm was used to solve the problem. The fitness function of the genetic algorithm calls a kinematics and dynamics simulation model of the robotic installation constructed in Matlab , in order to compute the work consumed and to check possible violation of constraints. Examples of straight line and circular movement are given to prove the concept. Results are encouraging, yet demand on computing power is high.

A computationally efficient algorithm of iterative learning control for discrete-time linear time-varying systems

Iterative Learning Control (ILC) improves the tracking accuracy of systems that repetitively perform the same task. This paper considers model-based ILC for linear time-varying (LTV) systems. The applied feedforward iteratively minimises a quadratic norm of the feedforward update and the error in the next iteration as predicted by the model. The optimal feedforward update can be derived straightforwardly