Vol. 4 No. 1 (2024): Issue 4
Articles

Deep Learning-Based Optimization for Mobile Robotic Delivery Systems

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  • Diwei Zhu,
  • Yunxiang Gan,
  • Xiaoyang Chen
DOI: https://doi.org/10.71070/oaml.v1i1.2

Published 2024-11-10

Keywords

  • Delivery system,
  • deep learning,
  • spatial attention mechanism,
  • DDPG algorithm,
  • nd-to-end optimization,
  • path planning
    • ...More
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How to Cite

Zhu, D., Gan, Y., & Chen, X. (2024). Deep Learning-Based Optimization for Mobile Robotic Delivery Systems. Optimizations in Applied Machine Learning, 4(1). https://doi.org/10.71070/oaml.v1i1.2

Copyright (c) 2024 Optimizations in Applied Machine Learning

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

In today's logistics and delivery landscape, mobile robot delivery systems have attracted considerable attention due to their efficiency and adaptability. Nevertheless, current robotic delivery solutions encounter various obstacles in complex and dynamically changing environments. Traditional algorithms, for instance, struggle with processing high-dimensional and unstructured data, resulting in inefficient adaptation to real-time environmental changes, which compromises accuracy and efficiency in path planning and task execution. Moreover, the lack of robust perception and decision-making mechanisms limits the robots' ability to handle intricate scenarios and fluctuating delivery demands. To tackle these challenges, this paper proposes an optimization approach for mobile robot delivery systems that leverages deep learning. The study initially integrates a spatial attention mechanism within the model, enabling the robot to focus on critical environmental regions and dynamically adjust attention points, thus enhancing obstacle recognition and avoidance in complex settings, which improves navigation accuracy and path planning. Furthermore, the Deep Deterministic Policy Gradient (DDPG) algorithm is utilized to optimize policies, supporting efficient learning in high-dimensional continuous spaces and empowering robots to acquire effective delivery strategies in challenging environments. Finally, an end-to-end optimization approach allows the system to convert sensor inputs directly into control commands, reducing intermediate complexity and minimizing error accumulation, thereby streamlining the system’s structure. Experimental results confirm that the proposed method substantially boosts delivery system performance, excelling in key metrics like path planning accuracy, task efficiency, and system robustness. The successful integration of the spatial attention mechanism with the deep policy gradient algorithm demonstrates a valuable new approach for advancing future robot delivery system optimizations.

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References

  1. Aslan, M. F., Durdu, A., and Sabanci, K. (2022). Visual-inertial image-odometry network (viionet): A gaussian process regression-based deep architecture proposal for uav pose estimation. Measurement 194, 111030
  2. Chang, L., Shan, L., Jiang, C., and Dai, Y. (2021). Reinforcement based mobile robot path planning with improved dynamic window approach in unknown environment. Autonomous robots 45, 51–76
  3. Chen, L., Jiang, Z., Cheng, L., Knoll, A. C., and Zhou, M. (2022). Deep reinforcement learning based trajectory planning under uncertain constraints. Frontiers in Neurorobotics 16, 883562
  4. Chen, L., Wu, P., Chitta, K., Jaeger, B., Geiger, A., and Li, H. (2023). End-to-end autonomous driving: Challenges and frontiers. arXiv preprint arXiv:2306.16927
  5. Erke, S., Bin, D., Yiming, N., Qi, Z., Liang, X., and Dawei, Z. (2020). An improved a-star based path planning algorithm for autonomous land vehicles. International Journal of Advanced Robotic Systems 17, 1729881420962263
  6. Gomes, A. C., de Lima Junior, F. B., Soliani, R. D., de Souza Oliveira, P. R., de Oliveira, D. A., Siqueira, R. M., et al. (2023). Logistics management in e-commerce: challenges and opportunities. Revista de Gest ̃ao e Secretariado 14, 7252–7272
  7. Gu, Y., Zhu, Z., Lv, J., Shi, L., Hou, Z., and Xu, S. (2023). Dm-dqn: Dueling munchausen deep q network for robot path planning. Complex & Intelligent Systems 9, 4287–4300
  8. Gupta, A., Anpalagan, A., Guan, L., and Khwaja, A. S. (2021). Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues. Array 10, 100057
  9. Huang, R., Qin, C., Li, J. L., and Lan, X. (2023). Path planning of mobile robot in unknown dynamic continuous environment using reward-modified deep q-network. Optimal Control Applications and Methods 44, 1570–1587
  10. Jiang, M. and Huang, G. Q. (2022). Intralogistics synchronization in robotic forward-reserve warehouses for e-commerce last-mile delivery. Transportation Research Part E: Logistics and Transportation Review
  11. , 102619
  12. Jones, M., Djahel, S., and Welsh, K. (2023). Path-planning for unmanned aerial vehicles with environment complexity considerations: A survey. ACM Computing Surveys 55, 1–39
  13. Lee, D.-H. and Liu, J.-L. (2023). End-to-end deep learning of lane detection and path prediction for real-time autonomous driving. Signal, Image and Video Processing 17, 199–205
  14. Lee, M.-F. R. and Yusuf, S. H. (2022). Mobile robot navigation using deep reinforcement learning. Processes 10, 2748
  15. Li, J., Qiao, Y., Liu, S., Zhang, J., Yang, Z., and Wang, M. (2022). An improved yolov5-based vegetable disease detection method. Computers and Electronics in Agriculture 202, 107345
  16. Mirahadi, F. and McCabe, B. Y. (2021). Evacusafe: A real-time model for building evacuation based on dijkstra’s algorithm. Journal of Building Engineering 34, 101687
  17. Neri, I. and Dinarama, E. (2024). Cities’ match-making: Fostering international collaboration for climate-resilient twins. In The Routledge Handbook on Greening High-Density Cities (Routledge). 15–29
  18. Qadir, Z., Ullah, F., Munawar, H. S., and Al-Turjman, F. (2021). Addressing disasters in smart cities through uavs path planning and 5g communications: A systematic review. Computer Communications
  19. , 114–135
  20. Segato, A., Di Marzo, M., Zucchelli, S., Galvan, S., Secoli, R., and De Momi, E. (2021). Inverse reinforcement learning intra-operative path planning for steerable needle. IEEE Transactions on
  21. Biomedical Engineering 69, 1995–2005
  22. Torres, J. F., Hadjout, D., Sebaa, A., Mart ́ınez- ́Alvarez, F., and Troncoso, A. (2021). Deep learning for time series forecasting: a survey. Big Data 9, 3–21
  23. Wang, J., Liu, Y., and Li, B. (2020). Reinforcement learning with perturbed rewards. In Proceedings of the AAAI conference on artificial intelligence. vol. 34, 6202–6209
  24. Wang, X., Wang, S., Liang, X., Zhao, D., Huang, J., Xu, X., et al. (2022). Deep reinforcement learning: A survey. IEEE Transactions on Neural Networks and Learning Systems
  25. Wang, Y., Li, X., Zhang, J., Li, S., Xu, Z., and Zhou, X. (2021). Review of wheeled mobile robot collision avoidance under unknown environment. Science Progress 104, 00368504211037771
  26. Wu, J. and Li, H. (2020). Deep ensemble reinforcement learning with multiple deep deterministic policy gradient algorithm. Mathematical Problems in Engineering 2020, 1–12
  27. Wu, Z., Meng, Z., Zhao, W., and Wu, Z. (2021). Fast-rrt: A rrt-based optimal path finding method. Applied sciences 11, 11777
  28. Yan, B., Chen, T., Zhu, X., Yue, Y., Xu, B., and Shi, K. (2020). A comprehensive survey and analysis on path planning algorithms and heuristic functions. In Intelligent Computing: Proceedings of the 2020
  29. Computing Conference, Volume 1 (Springer), 581–598
  30. Zhang, L., Zhang, Y., and Li, Y. (2020a). Path planning for indoor mobile robot based on deep learning. Optik 219, 165096
  31. Zhang, Z., Wu, J., Dai, J., and He, C. (2020). A novel real-time penetration path planning algorithm for stealth uav in 3d complex dynamic environment. Ieee Access 8, 122757–122771
  32. Zhao, C., Zhu, Y., Du, Y., Liao, F., and Chan, C.-Y. (2022). A novel direct trajectory planning approach based on generative adversarial networks and rapidly-exploring random tree. IEEE Transactions on
  33. Intelligent Transportation Systems 23, 17910–17921
  34. Zhao, J., Zhao, W., Deng, B., Wang, Z., Zhang, F., Zheng, W., et al. (2023). Autonomous driving system: A comprehensive survey. Expert Systems with Applications , 122836
  35. Zhou, Y., Xiao, J., Zhou, Y., and Loianno, G. (2022). Multi-robot collaborative perception with graph neural networks. IEEE Robotics and Automation Letters 7, 2289–2296