Micro-Platform Verification for LiDAR SLAM-Based Navigation of Mecanum-Wheeled Robot in Warehouse Environment

Wang, Yue; Ye, Ying Yu; Zhong, Wei; Gao, Bo Lin; Mu, Chong Zhang; Zhao, Ning

doi:10.3390/wevj16100571

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Open AccessArticle

Micro-Platform Verification for LiDAR SLAM-Based Navigation of Mecanum-Wheeled Robot in Warehouse Environment

by

Yue Wang

^1,2,

Ying Yu Ye

^1,*,

Wei Zhong

^2,*,

Bo Lin Gao

²,

Chong Zhang Mu

¹ and

Ning Zhao

^1,*

¹

School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China

²

State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, China

^*

Authors to whom correspondence should be addressed.

World Electr. Veh. J. 2025, 16(10), 571; https://doi.org/10.3390/wevj16100571 (registering DOI)

Submission received: 13 August 2025 / Revised: 18 September 2025 / Accepted: 1 October 2025 / Published: 8 October 2025

(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)

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Abstract

Path navigation for mobile robots critically determines the operational efficiency of warehouse logistics systems. However, the current QR (Quick Response) code path navigation for warehouses suffers from low operational efficiency and poor dynamic adaptability in complex dynamic environments. This paper introduces a deep reinforcement learning and hybrid-algorithm SLAM (Simultaneous Localization and Mapping) path navigation method for Mecanum-wheeled robots, validated with an emphasis on dynamic adaptability and real-time performance. Based on the Gazebo warehouse simulation environment, the TD3 (Twin Deep Deterministic Policy Gradient) path planning method was established for offline training. Then, the Astar-Time Elastic Band (TEB) hybrid path planning algorithm was used to conduct experimental verification in static and dynamic real-world scenarios. Finally, experiments show that the TD3-based path planning for mobile robots makes effective decisions during offline training in the simulation environment, while Astar-TEB accurately completes path planning and navigates around both static and dynamic obstacles in real-world scenarios. Therefore, this verifies the feasibility and effectiveness of the proposed SLAM path navigation for Mecanum-wheeled mobile robots on a miniature warehouse platform.

Keywords: Mecanum-wheeled robot; SLAM; path planning; deep reinforcement learning; warehouse environment

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MDPI and ACS Style

Wang, Y.; Ye, Y.Y.; Zhong, W.; Gao, B.L.; Mu, C.Z.; Zhao, N. Micro-Platform Verification for LiDAR SLAM-Based Navigation of Mecanum-Wheeled Robot in Warehouse Environment. World Electr. Veh. J. 2025, 16, 571. https://doi.org/10.3390/wevj16100571

AMA Style

Wang Y, Ye YY, Zhong W, Gao BL, Mu CZ, Zhao N. Micro-Platform Verification for LiDAR SLAM-Based Navigation of Mecanum-Wheeled Robot in Warehouse Environment. World Electric Vehicle Journal. 2025; 16(10):571. https://doi.org/10.3390/wevj16100571

Chicago/Turabian Style

Wang, Yue, Ying Yu Ye, Wei Zhong, Bo Lin Gao, Chong Zhang Mu, and Ning Zhao. 2025. "Micro-Platform Verification for LiDAR SLAM-Based Navigation of Mecanum-Wheeled Robot in Warehouse Environment" World Electric Vehicle Journal 16, no. 10: 571. https://doi.org/10.3390/wevj16100571

APA Style

Wang, Y., Ye, Y. Y., Zhong, W., Gao, B. L., Mu, C. Z., & Zhao, N. (2025). Micro-Platform Verification for LiDAR SLAM-Based Navigation of Mecanum-Wheeled Robot in Warehouse Environment. World Electric Vehicle Journal, 16(10), 571. https://doi.org/10.3390/wevj16100571

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Micro-Platform Verification for LiDAR SLAM-Based Navigation of Mecanum-Wheeled Robot in Warehouse Environment

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