6 DOF manipulators absolute positioning accuracy improvement using a neural-network

2020

IEEE Access

This paper proposes a new calibration method for enhancing robot positional accuracy of the industrial manipulators. By combining the joint deflection model with the conventional kinematic model of a manipulator, the geometric errors and joint deflection errors can be considered together to increase its positional accuracy. Then, a neural network is designed to additionally compensate the unmodeled errors, specially, non-geometric errors. The teaching-learning-based optimization method is employed to optimize weights and bias of the neural network. In order to demonstrate the effectiveness of the proposed method, real experimental studies are carried out on HH 800 manipulator. The enhanced position accuracy of the manipulator after the calibration confirms the feasibility and more positional accuracy over the other calibration methods.

Section: Introductionmentioning

confidence: 99%

Robot Manipulator Calibration Using a Model Based Identification Technique and a Neural Network With the Teaching Learning-Based Optimization

2020

IEEE Access

“…The conventional back-propagation neural network (BPNN) [34] is widely adopted by researchers [35] for compensating the unmodeled errors to increasing the precision of the robot. In robotic calibration processing, BPNN is usually employed to construct the relationship between the end effector position and the corresponding joint angle configuration [36][37][38][39]. However, the conventional BPNN has some drawbacks such as getting stuck in local minima and slowing convergence [40].…”

Section: Introductionmentioning

confidence: 99%

A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network Compensator

2020

Applied Sciences

The study proposed a robotic calibration algorithm for improving the robot manipulator position precision. At first, the kinematic parameters as well as the compliance parameters of the robot can be identified together to improve its accuracy using the joint deflection model and the conventional kinematic model calibration technique. Then, an artificial neural network is constructed for further compensating the unmodeled errors. The invasive weed optimization is used to determine the parameters of the neural network. To show the advantages of the suggested technique, an HH800 robot is employed for the experimental study of the proposed algorithm. The improved position precision of the robot after the experiment firmly proves the practicability and positional precision of the proposed method over the other algorithms in comparison.

“…Some researchers have applied the ANN to compensate for robot position errors. [19][20][21][22][23] Jang et al 19 used a radial basis function network (RBFN) to form a functional relation to describe the robot joint offset errors in terms of their joint positions. The works 19,21 have utilized an ANN in order to describe the functional relationship of end-effector positions and corresponding position errors.…”

Section: Introductionmentioning

confidence: 99%

A new calibration method for enhancing robot position accuracy by combining a robot model–based identification approach and an artificial neural network–based error compensation technique

Nguyen

Advances in Mechanical Engineering

2019

Robot position accuracy plays a very important role in advanced industrial applications. This article proposes a new method for enhancing robot position accuracy. In order to increase robot accuracy, the proposed method models and identifies determinable error sources, for instance, geometric errors and joint deflection errors. Because non-geometric error sources such as link compliance, gear backlash, and others are difficult to model correctly and completely, an artificial neural network is used for compensating for the robot position errors, which are caused by these non-geometric error sources. The proposed method is used for experimental calibration of an industrial Hyundai HH800 robot designed for carrying heavy loads. The robot position accuracy after calibration demonstrates the effectiveness and correctness of the method.