Path planning algorithm for logistics autonomous vehicles at Cainiao stations based on multi-sensor data fusion

Yan Chen

PLOS ONE, 2025, vol. 20, issue 5, 1-26

Abstract: Efficient path planning and obstacle avoidance in a complex and dynamic environment is one of the key challenges of unmanned vehicle logistics distribution, especially in the logistics scene of Cainiao Station, which involves crowded communities and dynamic campus roads. In view of the shortcomings of existing methods in multi-sensor data fusion and path optimization, this paper proposes a path planning model based on multi-sensor image fusion, named DynaFusion-Plan. The model is able to provide an optimal path from the starting point to the target point in a complex environment, avoiding obstacles and realizing the smoothness and dynamic adjustment ability of the path. The model consists of three modules: the sensor data fusion module uses Convolutional Neural Networks (CNN) and Lidar-Inertial Odometry and Simultaneous Localization and Mapping (LIO-SAM) technology to build a high-precision dynamic environment map; the path planning module combines Artificial Potential Field (APF) and Deep Deterministic Policy Gradient (DDPG) algorithms to balance path length, smoothness, and obstacle avoidance capabilities; the decision and control module uses Model Predictive Control (MPC) and Long Short-Term Memory (LSTM) to achieve real-time path tracking and dynamic adjustment. Experimental results on TartanAir, NuScenes, and AirSim datasets show that DynaFusion-Plan significantly outperforms existing methods in key indicators such as path length (42.5 m vs. 48.7 m), path smoothness (κ=0.05 vs. κ=0.15), and obstacle avoidance success rate (98.7% vs. 85.4%), especially in complex dynamic environments. It shows strong adaptability and stability. This work provides an efficient and reliable solution for unmanned vehicle path planning in intelligent logistics scenarios, and lays the foundation for future optimization directions, such as lightweight model design and more real-world scenario verification.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0321257

DOI: 10.1371/journal.pone.0321257

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