A framework for robotic arm pose estimation and movement prediction based on deep and extreme learning models

TL;DR

This paper introduces a new framework using deep and extreme learning models to detect robotic arm keypoints and predict their future movements, enhancing safety in human-robot collaboration environments.

Contribution

It presents a novel framework that combines deep and extreme learning techniques for robotic arm pose detection and movement prediction, improving safety measures.

Findings

Accurately detects robotic arm keypoints with low error

Successfully predicts future movements of robotic arms

Contributes to risk mitigation in collaborative robotics

Abstract

Human-robot collaboration has gained a notable prominence in Industry 4.0, as the use of collaborative robots increases efficiency and productivity in the automation process. However, it is necessary to consider the use of mechanisms that increase security in these environments, as the literature reports that risk situations may exist in the context of human-robot collaboration. One of the strategies that can be adopted is the visual recognition of the collaboration environment using machine learning techniques, which can automatically identify what is happening in the scene and what may happen in the future. In this work, we are proposing a new framework that is capable of detecting robotic arm keypoints commonly used in Industry 4.0. In addition to detecting, the proposed framework is able to predict the future movement of these robotic arms, thus providing relevant information that can be considered in the recognition of the human-robot collaboration scenario. The proposed framework is based on deep and extreme learning machine techniques. Results show that the proposed framework is capable of detecting and predicting with low error, contributing to the mitigation of risks in human-robot collaboration.