Optimization of Mobile Robot Delivery System Based on Deep Learning

Authors

  • Xiaoyang Chen

    The Ohio State University, Columbus, OH 43210, USA
  • Yunxiang Gan

    Moloco, CA, USA
  • Shuguang Xiong

    Microsoft Inc., Beijing 100080, China

DOI:

https://doi.org/10.30564/jcsr.v6i4.7197
Received: 3 September 2024 | Revised: 6 September 2024 | Accepted: 6 September 2024 | Published Online: 27 September 2024

Abstract

In modern logistics and delivery systems, mobile robot delivery systems have garnered significant attention due to their efficiency and flexibility. However, existing robot delivery systems still face numerous challenges in complex and dynamically changing environments. For instance, traditional algorithms exhibit low efficiency when processing high-dimensional and unstructured data, making it difficult to adapt to real-time changing environments, which results in reduced accuracy and efficiency in path planning and task execution. Additionally, the lack of effective perception and decision-making mechanisms makes it challenging for robots to handle complex scenarios and variable delivery demands. To address these issues, this paper proposes an optimization method for mobile robot delivery systems based on deep learning. Firstly, this study introduces a spatial attention mechanism into the model. By focusing on key areas in the environment and dynamically adjusting the attention points, robots can better recognize and avoid obstacles in complex environments, thus improving the accuracy of navigation and path planning. Secondly, the Deep Deterministic Policy Gradient (DDPG) algorithm is employed for policy optimization, facilitating efficient learning in high-dimensional continuous spaces, enabling robots to learn effective delivery strategies in complex environments. Finally, through an end-to-end optimization approach, the system can directly convert sensor data inputs into control command outputs, reducing the complexity and error accumulation of intermediate steps and simplifying the system structure. Experimental results demonstrate that the proposed method significantly enhances the overall performance of the delivery system. It performs exceptionally well on several key indicators, including the accuracy of path planning, task execution efficiency, and system robustness. The effectiveness of combining the spatial attention mechanism with the deep policy gradient algorithm has been fully validated, providing new insights and methods for future optimization of robot delivery systems.

Keywords:

Delivery system; Deep learning; Spatial attention mechanism; DDPG algorithm; End-to-end optimization; Path planning

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How to Cite

Chen, X., Gan, Y., & Xiong, S. (2024). Optimization of Mobile Robot Delivery System Based on Deep Learning. Journal of Computer Science Research, 6(4), 51–65. https://doi.org/10.30564/jcsr.v6i4.7197

Issue

Vol. 6 , Iss. 4 (October 2024): In progress

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Article

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Copyright © 2024 Author(s)

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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