Study on Risk Reduction in Localization of Cloud-Supported Autonomous Mobile Robots

Mao Nabeta; Kazuteru Tobita; Seiya Nakamura; Kazuhiro Mima

doi:10.51094/jxiv.930

##article.authors##

Mao Nabeta Graduate school, Shizuoka Institute of Science and Technology
Kazuteru Tobita Department of Mechanical Engineering, Shizuoka Institute of Science and Technology https://orcid.org/0000-0002-1560-366X https://researchmap.jp/read0087217
Seiya Nakamura Graduate school, Shizuoka Institute of Science and Technology
Kazuhiro Mima Department of Electrical and Electronic Engineering, Shizuoka Institute of Science and Technology https://researchmap.jp/mima3mima

DOI:

https://doi.org/10.51094/jxiv.930

キーワード:

Autonomous mobile robots、 self-localization、 sensor fusion、 cloud robotics、 communication failure resilience

抄録

In recent years, outdoor robotics applications have trended towards the "cloud robotics" approach, which involves offloading processing to other computation-rich locations via communication with the cloud, due to the increasing complexity of the tasks the robots are required to perform. However, network conditions in outdoor environments are often volatile, leading to significant performance degradation or unstable behavior in robots due to poor communication with the cloud. To address this issue, we propose a method that combines minimal self-localization capabilities on the robot side with advanced self-localization processing on the cloud side, integrating and interpolating the two. This paper discusses implementing 2D self-localization on the robot and 3D self-localization on the cloud, clarifies the characteristics of each and proposes a fusion method that leverages the features of both self-localization techniques for high accuracy and robustness against communication failures. Adaptive Monte Carlo localization (AMCL), which is a common algorithm for two-dimensional self-localization, was used on the robot side. We implemented two fusion methods: a time-varying weighted average (TVWA) and an unscented Kalman filter (UKF), and showed that these methods can reduce the maximum error compared to using the AMCL alone by approximately 0.11 m with the TVWA and by approximately 0.15 m with the UKF.

利益相反に関する開示

We have no conflict of interest.

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