A dynamical system approach for detection and reaction to human guidance in physical human–robot interaction

Khoramshahi, Mahdi; Billard, Aude

doi:10.1007/s10514-020-09934-9

Cited by 20 publications

(10 citation statements)

References 48 publications

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“…The used constraint-based approach also bears similarities with the autonomous dynamical systems described in [19] for human-robot interactive applications. For instance, in the case of the automatic reactive approach motion BB, the only explicit time dependency is contained within the weights, which are used to give the user the possibility of activating/deactivating its resulting behavior at arbitrary time instances.…”

Section: Discussionmentioning

confidence: 99%

Reconfigurable Constraint-Based Reactive Framework for Assistive Robotics With Adaptable Levels of Autonomy

Iregui

Schutter

Aertbeliën

2021

IEEE Robot. Autom. Lett.

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Section: Discussionmentioning

confidence: 99%

Reconfigurable Constraint-Based Reactive Framework for Assistive Robotics With Adaptable Levels of Autonomy

Iregui

Schutter

Aertbeliën

2021

IEEE Robot. Autom. Lett.

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“…There are also aspects concerning robot operations. These include H-RIs during robot navigation [Ali and Mailah 2019], reactive obstacle avoidance [Huber et al 2019], human motion probabilistic modelling [Charalampous 2016;Pöhler et al 2019], and contact recognition [Khoramshahi and Billard 2019;Haddadin et al 2017].…”

Section: On Bystander's Safetymentioning

confidence: 99%

On the Safety of Mobile Robots Serving in Public Spaces

Salvini

Paez-Granados

Billard

2021

J. Hum.-Robot Interact.

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Since 2014, a specific standard has been dedicated for the safety certification of personal care robots, which operate in close proximity to humans. These robots serve as information providers, object transporters, personal mobility carriers, and security patrollers. In this article, we point out the shortcomings concerning EN ISO 13482:2014, which encompasses guidelines regarding the safety and design of personal care robots. In particular, we argue that the current standard is not suitable for guaranteeing people's safety when these robots operate in public spaces. Specifically, the standard lacks requirements to protect pedestrians and bystanders. The guideline implicitly assumes that private spaces, such as households and offices, present the same hazards as in public spaces. We highlight the existence of at least three properties pertaining to robots’ use in public spaces. These properties include (1) crowds, (2) social norms and proxemics rules, and (3) people's misbehaviours. We discuss how these properties impact robots’ safety. This article aims to raise stakeholders’ awareness on individuals’ safety when robots are deployed in public spaces. This could be achieved by integrating the gaps present in EN ISO 13482:2014 or by creating a new dedicated standard.

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“…Khoramshahi and Billard (2020) discussed a dynamical system approach (DSA). DSA provides a consolidated robotic structure for the lead role and follower role and offers human supervision in both roles.…”

Section: Background Study On the Human‐robot Interaction To Detect Th...mentioning

confidence: 99%

Human muscle rigidity identification by human‐robot approximation characteristics framework on internet of things platform

2021

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In the health care system and Internet of Things (IoT) platform, medical care robotics is becoming one of the quickest expanding areas of robot technology. The integration of robotics and human knowledge identifies human muscle rigidity from the healthcare data obtained from the wearable sensor. In an IoT platform, Electromyography is a method used for evaluating and tracking the electrical activity of muscles. The transferring of human muscle rigidity to a robot facilitates the robot to obtain resistive management initiatives in a useful and effective way while carrying out physical interaction activities in unstructured surroundings. The major challenges to overcome the unpredictability during physical interaction allow a robot to realize the individual behaviour with adaptability and versatility of muscles. Therefore, in this article, Human‐Robot Approximation Characteristics Framework (HRACF) has been proposed for developing physiological communication between humans and robots. HRACF permits robots to understand differential resistive abilities of muscles from human presentations. The pulses collected from Electromyography are used to retrieve human arm muscle rigidity during activity presentation. The characteristics of motion and rigidity are concurrently modelled using an estimation and approximation model with a logistic regression obtained by IoT devices. The analysed human arm muscle rigidity is then connected to the robot impedance regulator. HR model uses an optimized resistive approximator to measure the creative variables of the robot and continue driving to monitor the quoted pathways at the time of interaction. The relationship between motion data and rigidity data is systematically coded in the HR model. HRACF makes it possible to detect uncertainties through space and time that facilitates the robot to meet rigidity specification to 98[Nm/Rad] and error rate to 0.15% during physical interaction.

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A dynamical system approach for detection and reaction to human guidance in physical human–robot interaction

Cited by 20 publications

References 48 publications

Reconfigurable Constraint-Based Reactive Framework for Assistive Robotics With Adaptable Levels of Autonomy

Reconfigurable Constraint-Based Reactive Framework for Assistive Robotics With Adaptable Levels of Autonomy

On the Safety of Mobile Robots Serving in Public Spaces

Human muscle rigidity identification by human‐robot approximation characteristics framework on internet of things platform

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