Dynamic Model and Inverse Kinematic Identification of a 3-DOF Manipulator Using RLSPSO.

Josias Batista,Darielson Souza,Antônio Barbosa,Laurinda Dos Reis,Rui Araújo

doi:10.3390/s20020416

Abstract

This paper presents the identification of the inverse kinematics of a cylindrical manipulator using identification techniques of Least Squares (LS), Recursive Least Square (RLS), and a dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by RLS (RLSPSO). A helical trajectory in the cartesian space is used as input. The dynamic model is found through the Lagrange equation and the motion equations, which are used to calculate the torque values of each joint. The torques are calculated from the values of the inverse kinematics, identified by each algorithm and from the manipulator joint speeds and accelerations. The results obtained for the trajectories, speeds, accelerations, and torques of each joint are compared for each algorithm. The computational costs as well as the Multi-Correlation Coefficient () are computed. The results demonstrated that the identification accuracy of RLSPSO is better than that of LS and PSO. This paper brings an improvement in RLS because it is a method with high complexity, so the proposed method (hybrid) aims to improve the computational cost and the results of the classic RLS.

Highlights

The diffusion of several systems in industrial environments has led over the years to the fact that several identification methods were developed to monitor and control various models of plants such as mobile robots or manipulator robots giving them the ability to operate accurately and efficiency [1].These robots must perform tasks with great perfection and safety
The results of speeds, accelerations and torques are presented for each trajectory generated from the identification of Least Squares (LS), recursive least squares method (RLS), and RLS with PSO (RLSPSO)
This trajectory provides the final manipulator positions collected from the encoders of each manipulator joint that were used as inputs to the algorithms that perform inverse kinematic identification

Summary

Introduction

The diffusion of several systems in industrial environments has led over the years to the fact that several identification methods were developed to monitor and control various models of plants such as mobile robots or manipulator robots giving them the ability to operate accurately and efficiency [1]. These robots must perform tasks with great perfection and safety. Some methodologies of estimation of model parameters were proposed as the recursive least squares method (RLS). According to the work presented in [2] the RLS method updates a vector of parameters and has a lower computational

Objectives

Methods

Results

Discussion

Conclusion