The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)
Online ISSN : 2424-3124
2019
Session ID : 1P2-D07
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Development of a Low Cognitive Load Planning Reminder System Adapting Changes of Work States Using Augmented Reality in Teleoperation of Heavy Machinery
*Ryuya SATOMitsuhiro KAMEZAKIYuki YAMASHITAShigeki SUGANOHiroyasu IWATA
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Abstract

Work efficiency of teleoperation of heavy machinery is less than a half of boarding operation because of difficulties in creating mental representation of work sites. Thus, we proposed a pre-offering view system for teleoperators to remember environmental information, which helps operators plan paths and working strategies, but some operators forgot their planning. Problems of previous researches including the navigation field are not considering changes of work states including moving and reaching and cognitive load. Therefore, in this paper, we develop a planning reminder system with low cognitive load which can adopt changes of work states. We display different AR depending on work states. Furthermore, we display AR only for current and next work states while operators are in low cognitive load to avoid increasing cognitive load to watch AR. The experimental results using a scale model indicates that the proposed system can decrease work time and cognitive load.

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© 2019 The Japan Society of Mechanical Engineers
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