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Related Experiment Video

Updated: May 23, 2025

Operation of the Collaborative Composite Manufacturing CCM System
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Path planning algorithm for logistics autonomous vehicles at Cainiao stations based on multi-sensor data fusion.

Yan Chen1

  • 1College of Transportation Management, Zhejiang Institute of Communications, Hangzhou, Zhejiang, China.

Plos One
|May 20, 2025
PubMed
Summary

DynaFusion-Plan enhances unmanned vehicle logistics by fusing multi-sensor data for superior path planning and obstacle avoidance in dynamic environments. This model ensures smoother, safer, and more adaptable routes for intelligent distribution.

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Area of Science:

  • Robotics and Autonomous Systems
  • Artificial Intelligence
  • Computer Vision

Background:

  • Efficient path planning and obstacle avoidance are critical for unmanned vehicles in complex, dynamic logistics environments like Cainiao Station.
  • Existing methods struggle with multi-sensor data fusion and path optimization, limiting performance in crowded and dynamic settings.

Purpose of the Study:

  • To propose DynaFusion-Plan, a novel path planning model leveraging multi-sensor image fusion for enhanced unmanned vehicle navigation.
  • To address limitations in current approaches by improving dynamic environment mapping, path optimization, and real-time control.

Main Methods:

  • Sensor data fusion module: Utilizes Convolutional Neural Networks (CNN) and Lidar-Inertial Odometry and Simultaneous Localization and Mapping (LIO-SAM) for high-precision dynamic environment mapping.
  • Path planning module: Integrates Artificial Potential Field (APF) and Deep Deterministic Policy Gradient (DDPG) algorithms to optimize path length, smoothness, and obstacle avoidance.
  • Decision and control module: Employs Model Predictive Control (MPC) and Long Short-Term Memory (LSTM) for real-time path tracking and dynamic adjustments.

Main Results:

  • DynaFusion-Plan demonstrated superior performance on TartanAir, NuScenes, and AirSim datasets compared to existing methods.
  • Achieved significantly better path length (42.5 m vs. 48.7 m), path smoothness, and obstacle avoidance success rate (98.7% vs. 85.4%).
  • Exhibited strong adaptability and stability, particularly in complex dynamic environments.

Conclusions:

  • DynaFusion-Plan offers an efficient and reliable solution for unmanned vehicle path planning in intelligent logistics.
  • The model provides a foundation for future research, including lightweight model design and real-world scenario validation.
  • Successfully addresses challenges in multi-sensor data fusion and path optimization for dynamic environments.