Collision Detection for Human-Robot Interaction in an Industrial Setting using Force Myography and a Deep Learning Approach

Anvaripour, Mohammad; Saif, Mehrdad

doi:10.1109/smc.2019.8914660

Cited by 17 publications

(13 citation statements)

References 19 publications

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“…Besides, among contact detection approaches in the state of the art, there are two similar works investigating collision detection using 1D-CNN. The authors of [94] compared both approaches, CollisionNet [65] and FMA [94], where the accuracy was 88% and 90%, respectively, featuring a detection delay of 200ms [94]. While our procedure in tactile perception (what is called collision detection in the state-of-the-art literature [61,65,75,76]) reached 99% with 80ms detection delay.…”

Section: Discussionmentioning

confidence: 98%

A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation

Amin

Rezayati

Venn

et al. 2020

Sensors

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Digital-enabled manufacturing systems require a high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings; however, this presence of workers in the shared workspace with robots decreases the productivity, as the robot is not aware about the human position and intention, which leads to concerns about human safety. This issue is addressed in this work by designing a reliable safety monitoring system for collaborative robots (cobots). The main idea here is to significantly enhance safety using a combination of recognition of human actions using visual perception and at the same time interpreting physical human–robot contact by tactile perception. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if physical contact between human and cobot takes place. Two different deep learning networks are used for human action recognition and contact detection, which in combination, are expected to lead to the enhancement of human safety and an increase in the level of cobot perception about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human–robot collaboration (HRC) in industrial automation.

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Section: Discussionmentioning

confidence: 98%

A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation

Amin

Rezayati

Venn

et al. 2020

Sensors

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“…Across the literature, there are several technologies that can actually contribute towards detecting and mitigating risks in HRC [15], [16], [14], [17], [18], [19]. Among these, we highlight the special role of machine learning (ML), which provides intelligent systems to solve the most diverse engineering problems, including HRC.…”

Section: Related Workmentioning

confidence: 99%

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

Rodrigues¹,

Marrone²,

Filho³

et al. 2022

Preprint

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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.

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“…With rapid developments in artificial intelligence, it draws attention to the robotics research works too. Several works by Heo et al (2019), Sharkawy et al (2020), Cioffi et al (2020), and Anvaripour and Saif (2019) deployed artificial intelligence techniques in the safety issues in HRI. In a study by Sharkawy et al (2020), a multilayer feedforward NN is used to detect a collision and identify the collided robotic link.…”

Section: Related Workmentioning

confidence: 99%

“…The model takes joint's information and acts as a binary classifier to predict if a collision happens or not. The authors in Anvaripour and Saif (2019) use CollisionNet in combination with force myography sensors attached to the human arm to classify if a collision is intended or not.…”

Section: Related Workmentioning

confidence: 99%

A Bayesian Deep Neural Network for Safe Visual Servoing in Human–Robot Interaction

2021

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Safety is an important issue in human–robot interaction (HRI) applications. Various research works have focused on different levels of safety in HRI. If a human/obstacle is detected, a repulsive action can be taken to avoid the collision. Common repulsive actions include distance methods, potential field methods, and safety field methods. Approaches based on machine learning are less explored regarding the selection of the repulsive action. Few research works focus on the uncertainty of the data-based approaches and consider the efficiency of the executing task during collision avoidance. In this study, we describe a system that can avoid collision with human hands while the robot is executing an image-based visual servoing (IBVS) task. We use Monte Carlo dropout (MC dropout) to transform a deep neural network (DNN) to a Bayesian DNN, and learn the repulsive position for hand avoidance. The Bayesian DNN allows IBVS to converge faster than the opposite repulsive pose. Furthermore, it allows the robot to avoid undesired poses that the DNN cannot avoid. The experimental results show that Bayesian DNN has adequate accuracy and can generalize well on unseen data. The predictive interval coverage probability (PICP) of the predictions along x, y, and z directions are 0.84, 0.94, and 0.95, respectively. In the space which is unseen in the training data, the Bayesian DNN is also more robust than a DNN. We further implement the system on a UR10 robot, and test the robustness of the Bayesian DNN and the IBVS convergence speed. Results show that the Bayesian DNN can avoid the poses out of the reach range of the robot and it lets the IBVS task converge faster than the opposite repulsive pose.1

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Collision Detection for Human-Robot Interaction in an Industrial Setting using Force Myography and a Deep Learning Approach

Cited by 17 publications

References 19 publications

A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation

A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation

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

A Bayesian Deep Neural Network for Safe Visual Servoing in Human–Robot Interaction

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