A LiDAR-Based Dual-Vehicle Collaborative Transportation Approach Without Communication

In scenarios where wireless communication is unavailable, achieving dual-vehicle collaborative transportation for ultra-long and heavy loads solely relying on sensor data from automated guided vehicles (AGVs) poses a significant challenge. The primary obstacle lies in the inability to obtain real-time relative pose and velocity information between the two AGVs. To address this issue, this paper proposes a multi-line LiDAR-based dual-vehicle cooperative control method to tackle the synchronization challenges in robot collaborative transportation under non-communication conditions. By integrating multi-line LiDAR with reflective markers, the proposed method achieves high-precision measurement of the relative poses between the two AGVs. Based on a leader-follower cooperative control strategy, the follower AGV maintains a stable relative position relative to the leader AGV throughout the collaborative transportation process. Additionally, a six-axis force sensor mounted on the vehicle body monitors the load force in real time, and this information is incorporated to adjust the follower’s synchronization speed, thereby enhancing the robustness of the AGV system. Experimental results from multiple real-vehicle tests demonstrate that the proposed method successfully accomplishes dual-vehicle collaborative transportation tasks under non-communication conditions. Unlike traditional cooperative control methods reliant on wireless communication, the proposed approach operates efficiently in completely non-communication environments, making it widely applicable to complex or specialized scenarios. This study provides a reliable technical solution for multi-robot collaborative transportation in industrial automation.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3579501
Primary Topic
Transportation and Mobility Innovations
Type
article
Field-Weighted Citation Impact
0.00

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article

A LiDAR-Based Dual-Vehicle Collaborative Transportation Approach Without Communication

Minghao Xia, Mingfei Li, Feng Xie, Haibin Liu
IEEE Access
Transportation and Mobility Innovations
article

A LiDAR-Based Dual-Vehicle Collaborative Transportation Approach Without Communication

Minghao Xia, Mingfei Li, Feng Xie, Haibin Liu
article en

Abstract

In scenarios where wireless communication is unavailable, achieving dual-vehicle collaborative transportation for ultra-long and heavy loads solely relying on sensor data from automated guided vehicles (AGVs) poses a significant challenge. The primary obstacle lies in the inability to obtain real-time relative pose and velocity information between the two AGVs. To address this issue, this paper proposes a multi-line LiDAR-based dual-vehicle cooperative control method to tackle the synchronization challenges in robot collaborative transportation under non-communication conditions. By integrating multi-line LiDAR with reflective markers, the proposed method achieves high-precision measurement of the relative poses between the two AGVs. Based on a leader-follower cooperative control strategy, the follower AGV maintains a stable relative position relative to the leader AGV throughout the collaborative transportation process. Additionally, a six-axis force sensor mounted on the vehicle body monitors the load force in real time, and this information is incorporated to adjust the follower’s synchronization speed, thereby enhancing the robustness of the AGV system. Experimental results from multiple real-vehicle tests demonstrate that the proposed method successfully accomplishes dual-vehicle collaborative transportation tasks under non-communication conditions. Unlike traditional cooperative control methods reliant on wireless communication, the proposed approach operates efficiently in completely non-communication environments, making it widely applicable to complex or specialized scenarios. This study provides a reliable technical solution for multi-robot collaborative transportation in industrial automation.

IEEE AccessVol. 13
Beijing University of Technology (CN), Beijing Agricultural Machinery Research Institute (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 87%
Transportation and Mobility Innovations
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