Wearable Sensors for Human–Robot Walking Together

Moschetti, Alessandra; Cavallo, Filippo; Esposito, Dario; Penders, Jacques; Nuovo, Alessandro Di

doi:10.3390/robotics8020038

Cited by 11 publications

(8 citation statements)

References 36 publications

(54 reference statements)

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“…Recent studies have been concentrated on visual and non-visual perception systems to recognize human actions [9]. One method amongst non-visual approaches consists of using wearable devices [10][11][12][13][14][15]. Nevertheless, applying this technology as a possible solution for an industrial situation seems at present neither feasible nor comfortable in industrial environments because of restrictions that it will impose on the operator's movements.…”

Section: Human Action Recognition (Har)mentioning

confidence: 99%

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: Human Action Recognition (Har)mentioning

confidence: 99%

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

Amin

Rezayati

Venn

et al. 2020

Sensors

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“…Generally speaking, the signals that are employed for monitoring human activity could be classified as biological and non-biological signals [17]- [21]. For instance, in [14] wearable IMU's were used to identify human position and movments trajectory while working with a mobile robot. Also, Xie et al [30] employed haptic flexible sensors to estimate interaction force besides other wearable sensors for HRC.…”

Section: Overview Of Wearable Sensors In Hrcmentioning

confidence: 99%

“…A critical application of HRC is when humans and robots perform joint tasks in a shared workspace [3]. In order to communicate the intentions of the human to the robot, sensors are utilised such as an Electromyogram (EMG) [7], Inertial Measurement Unit (IMU) [14]. The EMG signals are typically acquired from a human upper-limb since they are mostly used in the given tasks [7].…”

Section: Introductionmentioning

confidence: 99%

Effective Human-Robot Collaboration Through Wearable Sensors

Al-Yacoub

Buerkle

Flanagan

et al. 2020

2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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With the developments of collaborative robots in manufacturing, physical interactions between humans and robots represent a vital role in performing tasks collaboratively. Most conducted studies focused on robot motion planning and control during the execution of a task. However, for effective task distribution and allocation, human physical and psychological status are essential. In this research, a hardware setup and support software for a set of wearable sensors and a data acquisition framework, are developed. This can be used to develop more efficient Human-Robot collaboration strategies. The developed framework is intended to recognise the human mental state and physical activities. Subsequently, a robot could effectively and naturally perform the given task with the human. Besides, the collected data through the developed hardware enables online classification of human intentions and activities; therefore, robots can actively adapt to ensure the safety of the human while delivering the required task.

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“…IMU suits were proposed as a solution to the problem of unobstructed motion capture as early as 2004 in the context of humanoid robot control [ 19 ], and motion capture of specific body parts for specific applications has improved significantly since then. IMU systems for tracking head [ 20 ], leg [ 21 ], hand [ 22 ], and wrist [ 23 ] motion have been used in various industrial robotic applications as well as for rehabilitation tasks [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

A Wearable IMU System for Flexible Teleoperation of a Collaborative Industrial Robot

Škulj

Vrabič

Podržaj

2021

Sensors

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Increasing the accessibility of collaborative robotics requires interfaces that support intuitive teleoperation. One possibility for an intuitive interface is offered by wearable systems that measure the operator’s movement and use the information for robot control. Such wearable systems should preserve the operator’s movement capabilities and, thus, their ability to flexibly operate in the workspace. This paper presents a novel wireless wearable system that uses only inertial measurement units (IMUs) to determine the orientation of the operator’s upper body parts. An algorithm was developed to transform the measured orientations to movement commands for an industrial collaborative robot. The algorithm includes a calibration procedure, which aligns the coordinate systems of all IMUs, the operator, and the robot, and the transformation of the operator’s relative hand motions to the movement of the robot’s end effector, which takes into account the operator’s orientation relative to the robot. The developed system is demonstrated with an example of an industrial application in which a workpiece needs to be inserted into a fixture. The robot’s motion is compared between the developed system and a standard robot controller. The results confirm that the developed system is intuitive, allows for flexible control, and is robust enough for use in industrial collaborative robotic applications.

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Wearable Sensors for Human–Robot Walking Together

Cited by 11 publications

References 36 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

Effective Human-Robot Collaboration Through Wearable Sensors

A Wearable IMU System for Flexible Teleoperation of a Collaborative Industrial Robot

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