Journal of Engineering, Project, and Production Management, 2026, 16(2), 2025-196
Route Optimization for Unmanned Delivery Vehicles Using Improved Q-Learning and Lego-Loma
Associate Professor, College of Business Administration, Zibo Polytechnic University, Zibo, 255300, China, E-mail: haoyi121121@163.com
Project Management
Received September 4, 2025; revised October 22, 2025; accepted October 23, 2025
Available online March 7, 2026
Abstract: With the development of technology and economy, unmanned delivery vehicles are being applied across multiple industries. To more accurately design optimal delivery routes and reduce time and energy consumption, this paper builds a route optimization model by integrating an improved Q-learning algorithm with an enhanced, lightweight laser-based odometry and mapping algorithm. The innovation of this study lies in the integrating multiple algorithms for route optimization. By augmenting the Q-learning algorithm with a simulated annealing algorithm and an improved reward mechanism, the approach effectively avoids local optima while enhancing global search capabilities. This advancement overcomes key limitations of traditional reinforcement learning, enabling the improved algorithm to outperform deep Q-networks and random reward reinforcement learning methods demonstrating faster convergence, stronger learning capacity, and better adaptability. Furthermore, this study incorporates data from an inertial measurement unit and an extended Kalman filter to optimize the lightweight LiDAR-based ranging algorithm, significantly improving the system’s stability and positioning accuracy. Through these integrations, the study establishes a high-performance route optimization model that avoids local optima, maintains enhanced stability, and exhibits superior environmental adaptability, making it particularly suitable for logistics enterprises and urban planning departments. This study improves the original algorithm by incorporating inertial measurement unit data for better mapping and pose estimation. Subsequently, Simulated Annealing (SA)and a modified reward mechanism are then applied to optimize Q-learning for path generation. The proposed model generates routes that are 23 m and 61 m shorter than those of the two comparison models, respectively. In addition, the Root Mean Square Error (RMSE) of the proposed model is 0.32, which is lower than those of the baseline models, demonstrating higher accuracy and better fit. These results indicate that the proposed model performs well in predicting optimal routes. It offers significant advantages in route optimization and can reliably analyze and predict complex road conditions, supporting the safe operation of unmanned delivery vehicles.
Keywords: Q-learning, LeGO-LOAM , simulated annealing, unmanned delivery vehicle
Copyright © Journal of Engineering, Project, and Production Management (EPPM-Journal).
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
Requests for reprints and permissions at eppm.journal@gmail.com.
Citation: Yue, H. (2026). Route Optimization for Unmanned Delivery Vehicles Using Improved Q-Learning and Lego-Loma. Journal of Engineering, Project, and Production Management, 16(2), 2025-196.
