Human-Robot Collaborative Omnidirectional Mobility for Picking Work Assistance : 1<sup>st</sup> report: Proposal of the operational interface and its feasiblity study

This study aims to develop the omnidirectional mobility system with a hands-free interface to support walking in cooperation with picking operations with Autonomous mobile robots: AMR in a small warehouse. Currently, AMRs are used to transport goods in small warehouses. However, because many of these AMRs do not capabilities of picking items from shelves, the workers must pick up, load, and unload them. Resulting in the workers must walk between the shelves and the AMRs, which takes a load of work. In this study, we develop the omnidirectional mobility for the workers to shorten the walking distance in small warehouses. The proposed mobility has a sitting stand support for the worker to sit or stand easily. The worker boards the mobility with sitting on the stand, and operates the mobility in a hands-free state. To estimate this operational intention, the mobility uses the worker’s body movement and the position of the center of mass on the boarding surface. This information is measured by using strain gauges and load cells installed at the bottom of the sitting stand support. This paper presents the configuration of the user interface and investigates the feasibility of estimating the operational intention via body movements.

Authors

Institutions

Publication Details

Published
2024-11-03
DOI
https://doi.org/10.1109/iecon55916.2024.10905984
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Human-Robot Collaborative Omnidirectional Mobility for Picking Work Assistance : 1st report: Proposal of the operational interface and its feasiblity study

Daisuke Chugo, Ryoichi Matsuda, Sho Yokota, Hiroshi Hashimoto
Advanced Manufacturing and Logistics Optimization
article

Human-Robot Collaborative Omnidirectional Mobility for Picking Work Assistance : 1st report: Proposal of the operational interface and its feasiblity study

Daisuke Chugo, Ryoichi Matsuda, Sho Yokota, Hiroshi Hashimoto
article en

Abstract

This study aims to develop the omnidirectional mobility system with a hands-free interface to support walking in cooperation with picking operations with Autonomous mobile robots: AMR in a small warehouse. Currently, AMRs are used to transport goods in small warehouses. However, because many of these AMRs do not capabilities of picking items from shelves, the workers must pick up, load, and unload them. Resulting in the workers must walk between the shelves and the AMRs, which takes a load of work. In this study, we develop the omnidirectional mobility for the workers to shorten the walking distance in small warehouses. The proposed mobility has a sitting stand support for the worker to sit or stand easily. The worker boards the mobility with sitting on the stand, and operates the mobility in a hands-free state. To estimate this operational intention, the mobility uses the worker’s body movement and the position of the center of mass on the boarding surface. This information is measured by using strain gauges and load cells installed at the bottom of the sitting stand support. This paper presents the configuration of the user interface and investigates the feasibility of estimating the operational intention via body movements.

Toyo University (JP), Kwansei Gakuin University (JP), Advanced Institute of Industrial Technology (JP)
Openalex Percentile: Top 59%
Advanced Manufacturing and Logistics Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.