# Kinematics Based Joint-Torque Estimation Using Bayesian Particle Filters

### Roja Zakeri, Praveen Shankar

#### 2023

#### Abstract

The aim of this paper is to estimate unknown torque in a 7-DOF industrial robot using Bayesian approach by observing the kinematic quantities. This paper utilizes two PMCMC algorithms (Particle Gibbs and Particle MH algorithms) for estimating unknown parameters of Baxter manipulator including joint torques, measurement and noise errors. The SMC technique has been used to construct the proposal distribution at each time step. The results indicate that for the Baxter manipulator, both PG and PMH algorithms perform well, but PG performs better as the estimated parameters using this technique have less deviation from the true parameters value. And this is due to sampling from parameters conditional distributions.

Download#### Paper Citation

#### in Harvard Style

Zakeri R. and Shankar P. (2023). **Kinematics Based Joint-Torque Estimation Using Bayesian Particle Filters**. In *Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO*; ISBN 978-989-758-670-5, SciTePress, pages 188-195. DOI: 10.5220/0012178400003543

#### in Bibtex Style

@conference{icinco23,

author={Roja Zakeri and Praveen Shankar},

title={Kinematics Based Joint-Torque Estimation Using Bayesian Particle Filters},

booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},

year={2023},

pages={188-195},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0012178400003543},

isbn={978-989-758-670-5},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO

TI - Kinematics Based Joint-Torque Estimation Using Bayesian Particle Filters

SN - 978-989-758-670-5

AU - Zakeri R.

AU - Shankar P.

PY - 2023

SP - 188

EP - 195

DO - 10.5220/0012178400003543

PB - SciTePress