Manipulator Trajectory Tracking Based on Kinematics Model Predictive Control

This paper explores the trajectory tracking strategy of a six-axis manipulator based on the kinematics model predictive control. Firstly, the kinematics of the manipulator was analyzed, and modeled mathematically. Next, the pose errors of the end effector were determined against the error parameters involving joints and rods. After that, the pose error model of the end effector was adopted to quantify how much the errors of different parameters affect the location of the manipulator. Finally, the geometric errors of manipulator trajectory were identified by the least squares (LS) method. Experimental results show the effectiveness of our model. The research results provide a reference for the theoretical analysis and application of manipulator trajectory tracking strategy

Mathematical modeling and kinematic analysis of 5 degrees of freedom serial link manipulator for online real-time pick and place applications

Modeling and kinematic analysis are crucial jobs in robotics that entail identifying the position of the robot’s joints in order to accomplish particular tasks. This article uses an algebraic approach to model the kinematics of a serial link, 5 degrees of freedom (DOF) manipulator. The analytical method is compared to an optimization strategy known as sequential least squares programming (SLSQP). Using an Intel RealSense 3D camera, the colored object is picked up and placed using vision-based technology, and the pixel location of the object is translated into robot coordinates. The LOBOT LX15D serial bus servo controller was used to transmit these coordinates to the robotic arm. Python3 programming language was used throughout the entire analysis. The findings demonstrated that both analytical and optimized inverse kinematic solutions correctly identified colored objects and positioned them in their appropriate goal points

An Efficient Iterative Learning Approach to Time-Optimal Path Tracking for Industrial Robots

© 2005-2012 IEEE. In pursuit of the time-optimal motion of an industrial robot along a desired path, a previously identified model is typically used to calculate the required inputs for perfect tracking. An inevitable model-plant mismatch, however, causes the obtained inputs to be suboptimal-resulting in poor tracking performance-or even be infeasible by exceeding given limits. This paper, at hand, presents a two-step iterative learning algorithm that compensates for such model-plant mismatch and finds the time-optimal motion, improving tracking performance, and ensuring feasibility. Due to an efficient solution of the path tracking problem using a sequential convex log barrier method, the delay between consecutive task executions is eliminated. To show the effectiveness of the proposed algorithm, an experimental validation on a standard industrial manipulator is performed, illustrating that the developed approach is capable of reducing the execution time while at the same time improving the tracking performance.status: publishe

r逐次更新型作業者モデルによる行動予測とその不確実性に基づく協働ロボット向け動作計画

Tohoku University小菅一弘課