Design of a Manned-Unmanned Teaming System and Forward-Formation Control for Crevasse Detection in the Path of a Human-Driven Vehicle

This paper proposes a Man-Unmanned Teaming (MUM-T) system, which is designed to protect manned vehicles traveling in Antarctica from hazards such as crevasse by collaborating with a crevasse exploration robot. Additionally, it introduces a forward-formation between the manned vehicle and the exploration robot within the MUM-T system. In the forward-formation, the exploration robot, acting as the follower, moves ahead of the manned vehicle, which serves as the leader of the formation, to explore crevasses along the anticipated route. The follower predicts the leader’s expected path using its state information, generates a desired position, and tracks that position. For the tracking control of the crevasse exploration robot, the kinematic model based velocity command is employed. The stability and performance of the proposed MUM-T system, forward-formation, and motion control algorithm are validated through simulations. Especially, the forward-formation demonstrated more than an 86% improvement in predicting and mimicking the leader’s trajectory compared to conventional formation strategies. Finally, the field test results in Antarctica confirmed the high practical applicability of the proposed forward-formation-based MUM-T system.

Authors

Institutions

Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3575110
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Design of a Manned-Unmanned Teaming System and Forward-Formation Control for Crevasse Detection in the Path of a Human-Driven Vehicle

Jong-Deuk Lee, Taeyoung Uhm, Na‐Hyun Lee, Hangyeul Lee et al.
IEEE Access
Advanced Chemical Sensor Technologies
article

Design of a Manned-Unmanned Teaming System and Forward-Formation Control for Crevasse Detection in the Path of a Human-Driven Vehicle

Jong-Deuk Lee, Taeyoung Uhm, Na‐Hyun Lee, Hangyeul Lee, Ji‐Wook Kwon, Young-Ho Choi
article en

Abstract

This paper proposes a Man-Unmanned Teaming (MUM-T) system, which is designed to protect manned vehicles traveling in Antarctica from hazards such as crevasse by collaborating with a crevasse exploration robot. Additionally, it introduces a forward-formation between the manned vehicle and the exploration robot within the MUM-T system. In the forward-formation, the exploration robot, acting as the follower, moves ahead of the manned vehicle, which serves as the leader of the formation, to explore crevasses along the anticipated route. The follower predicts the leader’s expected path using its state information, generates a desired position, and tracks that position. For the tracking control of the crevasse exploration robot, the kinematic model based velocity command is employed. The stability and performance of the proposed MUM-T system, forward-formation, and motion control algorithm are validated through simulations. Especially, the forward-formation demonstrated more than an 86% improvement in predicting and mimicking the leader’s trajectory compared to conventional formation strategies. Finally, the field test results in Antarctica confirmed the high practical applicability of the proposed forward-formation-based MUM-T system.

IEEE AccessVol. 13
Korea Institute of Robot and Convergence (KR)
Korea Institute of Marine Science and Technology promotion
Openalex Percentile: Top 89%
Advanced Chemical Sensor Technologies
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.