Human–Robot Interface for Embedding Sliding Adjustable Autonomy Methods

This work discusses a novel human–robot interface for a climbing robot for inspecting weld beads in storage tanks in the petrochemical industry. The approach aims to adapt robot autonomy in terms of the operator’s experience, where a remote industrial joystick works in conjunction with an electromyo...

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Main Authors: Piatan Sfair Palar, Vinícius de Vargas Terres, André Schneider de Oliveira
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/20/5960
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spelling doaj-c1f666c83d0246edadd68d77b3b864262020-11-25T03:57:26ZengMDPI AGSensors1424-82202020-10-01205960596010.3390/s20205960Human–Robot Interface for Embedding Sliding Adjustable Autonomy MethodsPiatan Sfair Palar0Vinícius de Vargas Terres1André Schneider de Oliveira2Graduate School of Electrical Engineering and Computer Science (CPGEI), Federal University of Technology-Paraná (UTFPR), Avenida 7 de Setembro 3165, Curitiba 80230-901, BrazilGraduate School of Electrical Engineering and Computer Science (CPGEI), Federal University of Technology-Paraná (UTFPR), Avenida 7 de Setembro 3165, Curitiba 80230-901, BrazilGraduate School of Electrical Engineering and Computer Science (CPGEI), Federal University of Technology-Paraná (UTFPR), Avenida 7 de Setembro 3165, Curitiba 80230-901, BrazilThis work discusses a novel human–robot interface for a climbing robot for inspecting weld beads in storage tanks in the petrochemical industry. The approach aims to adapt robot autonomy in terms of the operator’s experience, where a remote industrial joystick works in conjunction with an electromyographic armband as inputs. This armband is worn on the forearm and can detect gestures from the operator and rotation angles from the arm. Information from the industrial joystick and the armband are used to control the robot via a Fuzzy controller. The controller works with sliding autonomy (using as inputs data from the angular velocity of the industrial controller, electromyography reading, weld bead position in the storage tank, and rotation angles executed by the operator’s arm) to generate a system capable of recognition of the operator’s skill and correction of mistakes from the operator in operating time. The output from the Fuzzy controller is the level of autonomy to be used by the robot. The levels implemented are Manual (operator controls the angular and linear velocities of the robot); Shared (speeds are shared between the operator and the autonomous system); Supervisory (robot controls the angular velocity to stay in the weld bead, and the operator controls the linear velocity); Autonomous (the operator defines endpoint and the robot controls both linear and angular velocities). These autonomy levels, along with the proposed sliding autonomy, are then analyzed through robot experiments in a simulated environment, showing each of these modes’ purposes. The proposed approach is evaluated in virtual industrial scenarios through real distinct operators.https://www.mdpi.com/1424-8220/20/20/5960sliding autonomyhuman–robot interfaceMyo armbandfuzzy controller
collection DOAJ
language English
format Article
sources DOAJ
author Piatan Sfair Palar
Vinícius de Vargas Terres
André Schneider de Oliveira
spellingShingle Piatan Sfair Palar
Vinícius de Vargas Terres
André Schneider de Oliveira
Human–Robot Interface for Embedding Sliding Adjustable Autonomy Methods
Sensors
sliding autonomy
human–robot interface
Myo armband
fuzzy controller
author_facet Piatan Sfair Palar
Vinícius de Vargas Terres
André Schneider de Oliveira
author_sort Piatan Sfair Palar
title Human–Robot Interface for Embedding Sliding Adjustable Autonomy Methods
title_short Human–Robot Interface for Embedding Sliding Adjustable Autonomy Methods
title_full Human–Robot Interface for Embedding Sliding Adjustable Autonomy Methods
title_fullStr Human–Robot Interface for Embedding Sliding Adjustable Autonomy Methods
title_full_unstemmed Human–Robot Interface for Embedding Sliding Adjustable Autonomy Methods
title_sort human–robot interface for embedding sliding adjustable autonomy methods
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-10-01
description This work discusses a novel human–robot interface for a climbing robot for inspecting weld beads in storage tanks in the petrochemical industry. The approach aims to adapt robot autonomy in terms of the operator’s experience, where a remote industrial joystick works in conjunction with an electromyographic armband as inputs. This armband is worn on the forearm and can detect gestures from the operator and rotation angles from the arm. Information from the industrial joystick and the armband are used to control the robot via a Fuzzy controller. The controller works with sliding autonomy (using as inputs data from the angular velocity of the industrial controller, electromyography reading, weld bead position in the storage tank, and rotation angles executed by the operator’s arm) to generate a system capable of recognition of the operator’s skill and correction of mistakes from the operator in operating time. The output from the Fuzzy controller is the level of autonomy to be used by the robot. The levels implemented are Manual (operator controls the angular and linear velocities of the robot); Shared (speeds are shared between the operator and the autonomous system); Supervisory (robot controls the angular velocity to stay in the weld bead, and the operator controls the linear velocity); Autonomous (the operator defines endpoint and the robot controls both linear and angular velocities). These autonomy levels, along with the proposed sliding autonomy, are then analyzed through robot experiments in a simulated environment, showing each of these modes’ purposes. The proposed approach is evaluated in virtual industrial scenarios through real distinct operators.
topic sliding autonomy
human–robot interface
Myo armband
fuzzy controller
url https://www.mdpi.com/1424-8220/20/20/5960
work_keys_str_mv AT piatansfairpalar humanrobotinterfaceforembeddingslidingadjustableautonomymethods
AT viniciusdevargasterres humanrobotinterfaceforembeddingslidingadjustableautonomymethods
AT andreschneiderdeoliveira humanrobotinterfaceforembeddingslidingadjustableautonomymethods
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