Adaptive Task Scheduling for an Assembly Task Coworker Robot Based on Incremental Learning of Human's Motion Patterns

Jun Kinugawa; Akira Kanazawa; Shogo Arai; Kazuhiro Kosuge

doi:10.1109/LRA.2017.2655565

Adaptive Task Scheduling for an Assembly Task Coworker Robot Based on Incremental Learning of Human's Motion Patterns

Jun Kinugawa
, Akira Kanazawa
, Shogo Arai
, Kazuhiro Kosuge

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

35 Citations (Scopus)

Abstract

Future robots are expected to share the same workspace with humans and work in cooperation with them to improve productivity and maintain the quality of products. Considering this situation, we have developed a novel assembly task co-worker robot to support workers in their task by delivering the parts and tools to workers. Although such systems have improved work efficiency by predicting human's motion patterns, it is necessary to collect worker's data in advance and regenerate its model whenever the worker is changed. In this letter, we extend the previous system by installing an online learning algorithm and create a worker-dependent model without collecting data in advance. Trajectory prediction with high precision can be realized because of the worker-dependent model and effective utilization of the regularity of the worker's behavior. An adaptive task scheduling system based on the predicted result of the worker's behavior is proposed for improving work efficiency. Implementing the proposed algorithm, we evaluate the effectiveness of the task scheduling system by experiment. © 2016 IEEE.

Original language	English
Article number	7827110
Pages (from-to)	856-863
Journal	IEEE Robotics and Automation Letters
Volume	2
Issue number	2
DOIs	https://doi.org/10.1109/LRA.2017.2655565
Publication status	Published - 1 Apr 2017
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Research Keywords

Cognitive human-robot interaction
industrial robots
learning and adaptive systems

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10.1109/LRA.2017.2655565

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title = "Adaptive Task Scheduling for an Assembly Task Coworker Robot Based on Incremental Learning of Human's Motion Patterns",

abstract = "Future robots are expected to share the same workspace with humans and work in cooperation with them to improve productivity and maintain the quality of products. Considering this situation, we have developed a novel assembly task co-worker robot to support workers in their task by delivering the parts and tools to workers. Although such systems have improved work efficiency by predicting human's motion patterns, it is necessary to collect worker's data in advance and regenerate its model whenever the worker is changed. In this letter, we extend the previous system by installing an online learning algorithm and create a worker-dependent model without collecting data in advance. Trajectory prediction with high precision can be realized because of the worker-dependent model and effective utilization of the regularity of the worker's behavior. An adaptive task scheduling system based on the predicted result of the worker's behavior is proposed for improving work efficiency. Implementing the proposed algorithm, we evaluate the effectiveness of the task scheduling system by experiment. {\textcopyright} 2016 IEEE.",

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Adaptive Task Scheduling for an Assembly Task Coworker Robot Based on Incremental Learning of Human's Motion Patterns. / Kinugawa, Jun; Kanazawa, Akira; Arai, Shogo et al.
In: IEEE Robotics and Automation Letters, Vol. 2, No. 2, 7827110, 01.04.2017, p. 856-863.

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

TY - JOUR

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AU - Kinugawa, Jun

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AU - Arai, Shogo

AU - Kosuge, Kazuhiro

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2017/4/1

Y1 - 2017/4/1

N2 - Future robots are expected to share the same workspace with humans and work in cooperation with them to improve productivity and maintain the quality of products. Considering this situation, we have developed a novel assembly task co-worker robot to support workers in their task by delivering the parts and tools to workers. Although such systems have improved work efficiency by predicting human's motion patterns, it is necessary to collect worker's data in advance and regenerate its model whenever the worker is changed. In this letter, we extend the previous system by installing an online learning algorithm and create a worker-dependent model without collecting data in advance. Trajectory prediction with high precision can be realized because of the worker-dependent model and effective utilization of the regularity of the worker's behavior. An adaptive task scheduling system based on the predicted result of the worker's behavior is proposed for improving work efficiency. Implementing the proposed algorithm, we evaluate the effectiveness of the task scheduling system by experiment. © 2016 IEEE.

AB - Future robots are expected to share the same workspace with humans and work in cooperation with them to improve productivity and maintain the quality of products. Considering this situation, we have developed a novel assembly task co-worker robot to support workers in their task by delivering the parts and tools to workers. Although such systems have improved work efficiency by predicting human's motion patterns, it is necessary to collect worker's data in advance and regenerate its model whenever the worker is changed. In this letter, we extend the previous system by installing an online learning algorithm and create a worker-dependent model without collecting data in advance. Trajectory prediction with high precision can be realized because of the worker-dependent model and effective utilization of the regularity of the worker's behavior. An adaptive task scheduling system based on the predicted result of the worker's behavior is proposed for improving work efficiency. Implementing the proposed algorithm, we evaluate the effectiveness of the task scheduling system by experiment. © 2016 IEEE.

KW - Cognitive human-robot interaction

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SN - 2377-3774

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ER -

Adaptive Task Scheduling for an Assembly Task Coworker Robot Based on Incremental Learning of Human's Motion Patterns

Abstract

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UN SDGs

Research Keywords

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