Path Planning for Unmanned Delivery Vehicles Based on Machine Vision

Shuheng Gong

doi:10.54097/ajst.v7i3.13269

Authors

Shuheng Gong

DOI:

https://doi.org/10.54097/ajst.v7i3.13269

Keywords:

Machine vision; Express unmanned vehicle; Path Planning; Target recognition.

Abstract

With the continuous development of e-commerce, the demand for express delivery services is constantly increasing. The emergence of unmanned express vehicles has perfectly solved the problem of end logistics. This article takes the living area of Hangzhou University of Electronic Science and Technology as an example. Based on the existing unmanned vehicle parking nodes, the greedy algorithm is first used to solve the optimal original path between the starting point and each node of the unmanned vehicle delivery route. Then, combined with machine vision theory, the real-time road conditions are measured through Hough transform and object detection, and the path planning is re carried out. Finally, based on Flexsim, an unmanned express vehicle delivery model for the living area of Hangzhou University of Electronic Science and Technology is established, Compare the efficiency of express delivery before and after machine vision path optimization to determine the degree of optimization in path planning.

Downloads

Download data is not yet available.

References

Song, W. (2022) A novel solution for smart unmanned minibus based on intelligent fusion sensing technology. Journal of Physics: Conference Series, 3.

Huda, A., Rabeb, M., Hadi, O., et al. (2023) A matching game-based crowdsourcing framework for last-mile delivery: Ground-vehicles and unmanned-aerial vehicles. Journal of Network and Computer Applications, 3.

Wu, Z., Dong, S., Yuan, M., et al. (2023) Rotate artificial potential field algorithm toward 3D real-time path planning for unmanned aerial vehicle. Journal of Aerospace Engineering, 4.

Zhang, S., Shen, A., Luo, X. (2023) The path following of intelligent unmanned vehicle scheme based on adaptive sliding mode-model predictive control. Journal of the Franklin Institute, 4.

Chen, J., Li, M., Su, Y., et al. (2023) Direction constraints adaptive extended bidirectional A* algorithm based on random two-dimensional map environments. Robotics and Autonomous Systems, 5.

Hu, Y. (2022) Analysis of the development prospects of intelligent logistics terminal distribution. China Logistics and Procurement, 650 (13): 57-58.

Lin, Z. (2022) Technology empowerment, upgrading of express terminal technology. Logistics Technology and Application, 27 (06): 6.

Tang, T., Chen, Y., Liang, Z., et al. (2022) Application and analysis of unmanned delivery vehicles in community fresh delivery: Taking Longcheng Pangu Community, Sucheng District, Suqian City as an example. Inner Mongolia Science and Technology and Economy, 508 (18): 6.

Liu, X. (2022) Contactless delivery is on the rise. China Logistics and Procurement, 649 (12): 34-35.

Chen, S., Xu, C., Liang, P. (2022) Research on innovation of end logistics delivery models in universities in the era of big data. Market Weekly, 35 (12): 7.

Ma, D., Chen, M. (2012) Logistics cost analysis of distribution centers based on K-Means clustering algorithm. Logistics Technology, 35 (10): 2.

Wang, Y., Wang, W., Li, X. (2018) Location selection of distribution centers based on K-Means clustering algorithm and analytic hierarchy process. Economist, 11: 2.