The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)
Online ISSN : 2424-3124
2024
Session ID : 1A1-J06
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Risk Assessment of Cloud-Assisted Autonomous Mobile Robots
Errors in Self-Position Estimation and Their Impacts
*Mao NabetaKazuhiro MimaKazuteru Tobita
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Abstract

Due to the diverse shapes of robots tailored to their specific applications, conducting a uniform risk assessment is challenging. This research reevaluates the risk assessment for field and outdoor work robots, such as those used in agriculture, equipped with sensors to enable autonomous movement. It highlights the significant role that errors and losses in self-position estimation play in robot safety. Utilizing the initial risk assessment method in accordance with ISO 13482:2014, the study carried out a risk assessment for agricultural robots. By adding scenarios to anticipated damage locations, it became easier to anticipate risks and damages, offering clearer insights into risk identification. The findings indicate the need for further development in cloud robotics to emphasize the importance of a robot’s selfposition, aiming to create systems capable of handling more accurate self-positioning while maintaining possession of self-position.

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© 2024 The Japan Society of Mechanical Engineers
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