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Machines
Volume 14
Issue 1
10.3390/machines14010027
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Real-Time Cooperative Path Planning and Collision Avoidance for Autonomous Logistics Vehicles Using Reinforcement Learning and Distributed Model Predictive Control

by
Mingxin Li
1,
Hui Li
1,
Yunan Yao
2,
Yulei Zhu
1,
Hailong Weng
1,
Huabiao Jin
2,* and
Taiwei Yang
1,2
1
COSCO Shipping Heavy Industry (Zhoushan) Co., Ltd., Zhoushan 316131, China
2
School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China
*
Author to whom correspondence should be addressed.
Machines 2026, 14(1), 27; https://doi.org/10.3390/machines14010027
Submission received: 24 November 2025 / Revised: 18 December 2025 / Accepted: 21 December 2025 / Published: 24 December 2025
(This article belongs to the Special Issue Control and Path Planning for Autonomous Vehicles)
Versions Notes

Abstract

In industrial environments such as ports and warehouses, autonomous logistics vehicles face significant challenges in coordinating multiple vehicles while ensuring safe and efficient path planning. This study proposes a novel real-time cooperative control framework for autonomous vehicles, combining reinforcement learning (RL) and distributed model predictive control (DMPC). The RL agent dynamically adjusts the optimization weights of the DMPC to adapt to the vehicle’s real-time environment, while the DMPC enables decentralized path planning and collision avoidance. The system leverages multi-source sensor fusion, including GNSS, UWB, IMU, LiDAR, and stereo cameras, to provide accurate state estimations of vehicles. Simulation results demonstrate that the proposed RL-DMPC approach outperforms traditional centralized control strategies in terms of tracking accuracy, collision avoidance, and safety margins. Furthermore, the proposed method significantly improves control smoothness compared to rule-based strategies. This framework is particularly effective in dynamic and constrained industrial settings, offering a robust solution for multi-vehicle coordination with minimal communication delays. The study highlights the potential of combining RL with DMPC to achieve real-time, scalable, and adaptive solutions for autonomous logistics.
Keywords: autonomous logistics vehicles; collision avoidance; reinforcement learning; distributed model predictive control autonomous logistics vehicles; collision avoidance; reinforcement learning; distributed model predictive control

Share and Cite

MDPI and ACS Style

Li, M.; Li, H.; Yao, Y.; Zhu, Y.; Weng, H.; Jin, H.; Yang, T. Real-Time Cooperative Path Planning and Collision Avoidance for Autonomous Logistics Vehicles Using Reinforcement Learning and Distributed Model Predictive Control. Machines 2026, 14, 27. https://doi.org/10.3390/machines14010027

AMA Style

Li M, Li H, Yao Y, Zhu Y, Weng H, Jin H, Yang T. Real-Time Cooperative Path Planning and Collision Avoidance for Autonomous Logistics Vehicles Using Reinforcement Learning and Distributed Model Predictive Control. Machines. 2026; 14(1):27. https://doi.org/10.3390/machines14010027

Chicago/Turabian Style

Li, Mingxin, Hui Li, Yunan Yao, Yulei Zhu, Hailong Weng, Huabiao Jin, and Taiwei Yang. 2026. "Real-Time Cooperative Path Planning and Collision Avoidance for Autonomous Logistics Vehicles Using Reinforcement Learning and Distributed Model Predictive Control" Machines 14, no. 1: 27. https://doi.org/10.3390/machines14010027

APA Style

Li, M., Li, H., Yao, Y., Zhu, Y., Weng, H., Jin, H., & Yang, T. (2026). Real-Time Cooperative Path Planning and Collision Avoidance for Autonomous Logistics Vehicles Using Reinforcement Learning and Distributed Model Predictive Control. Machines, 14(1), 27. https://doi.org/10.3390/machines14010027

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