Exploring Multi-Sensor Fusion Navigation Models for Delivery Robots in Food Delivery Scenarios

Yuhan Liu

doi:10.54254/2755-2721/2025.LD29174

Applied and Computational EngineeringOpen access

Exploring Multi-Sensor Fusion Navigation Models for Delivery Robots in Food Delivery Scenarios

Research Article

Open Access

Exploring Multi-Sensor Fusion Navigation Models for Delivery Robots in Food Delivery Scenarios

Yuhan Liu ^1*

¹ Xi’an GaoXin No.1 High school, High tech Development Zone, Xi'an, Shaanxi, China

^*Corresponding author: 18966608707@163.com

Published on 5 November 2025

ACE Vol.204

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-517-2

ISBN (Online): 978-1-80590-518-9

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Abstract

The rapid growth of the food delivery industry has intensified the “last mile” challenge, where efficiency and safety remain bottlenecks. Single-sensor navigation methods, such as GPS, cameras, or LiDAR alone, often fail in dynamic urban environments due to signal drift, poor lighting, or adverse weather. This study proposes a multi-sensor fusion navigation model integrating LiDAR, RGB-D cameras, GPS, IMUs, and ultrasonic sensors to achieve complementary perception. The model employs a layered framework: low-level data fusion for synchronization and correction, mid-level feature fusion for semantic–geometric alignment, and high-level decision fusion for path planning and behavior prediction. The purpose of this research is to evaluate the feasibility of applying multi-sensor fusion to food delivery robots. Nevertheless, the analysis relies heavily on simulations and benchmark datasets, without fully considering long-term costs, hardware durability, or user acceptance. Future research should include large-scale field trials, interdisciplinary cost–benefit evaluations, and advances in lightweight fusion algorithms to enable scalable deployment in real-world delivery environments.

Keywords:

food delivery, delivery robots, multi-sensor fusion, navigation model, urban environments

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References

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References

[4]. Zhang, J., & Singh, S. (2017). Low-drift and real-time lidar odometry and mapping. Autonomous Robots, 41(2), 401–416.

[7]. Lin, Y., Zhang, Z., Tan, Y., Fu, H., & Min, H. (2025). Efficient TD3-based path planning of mobile robot in dynamic environments using prioritized experience replay and LSTM. Scientific Reports.

[8]. Rehrl, K., Gröchenig, S., & Wimmer, K. (2021). Evaluating localization accuracy of automated driving systems. Sensors, 21(17), 5855.

Cite this article

Liu,Y. (2025). Exploring Multi-Sensor Fusion Navigation Models for Delivery Robots in Food Delivery Scenarios. Applied and Computational Engineering,204,15-20.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-517-2(Print) / 978-1-80590-518-9(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/london.html

Conference date: 12 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.204

ISSN: 2755-2721(Print) / 2755-273X(Online)

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