New Research Publication at ICRAIC 2025
A new research paper titled “Path Planning for AGVs: Balancing Computational Efficiency and Optimality” has been accepted and presented at the 2025 5th International Conference on Robotics, Automation and Intelligent Control (ICRAIC) held in Chengdu, China.
The paper is authored by A. J. Moshayedi, A. S. Roy, Z. H. Khan, S. Yang, A. Razi, and M. E. Andani. The study focuses on improving path planning strategies for Automated Guided Vehicles (AGVs) used in modern industrial automation and logistics systems.
Research Overview
Automated Guided Vehicles (AGVs) are widely used in smart factories and automated warehouses to transport materials efficiently. This research evaluates the performance of eleven classical pathfinding algorithms in an AGV navigation system based on an Ackermann-steered electric vehicle model operating within a simulated urban environment containing six charging stations.
The algorithms studied include well-known methods such as Dijkstra, Depth First Search (DFS), Floyd–Warshall, Binary Search, and Prim’s Minimum Spanning Tree. These algorithms were categorized into By-Station and By-Edge routing approaches and adapted to meet specific navigation requirements.
Key Findings
AGV performance was evaluated using several metrics including runtime, travel distance, operational cost, speed, and station visitation coverage. In addition, the study introduces a new evaluation metric called the Energy Consumption Score (ECS), which balances computational cost and operational efficiency.
The results indicate that classical algorithms remain effective for navigation in static and predictable environments. Among the evaluated methods:
- Floyd–Warshall achieved the best overall performance with the lowest operational cost and highest ECS score.
- TSP and Prim’s MST provided the highest station coverage.
- Edge-based algorithms were reliable for predefined routing scenarios but were generally less efficient in terms of energy and overall cost.
Future Work
Future research will focus on integrating heuristic and hybrid algorithms, exploring dynamic network environments, and improving multi-AGV coordination and energy-aware routing for advanced urban mobility and industrial automation systems.
Citation:
A. J. Moshayedi, A. S. Roy, Z. H. Khan, S. Yang, A. Razi and M. E. Andani, “Path Planning for AGVs: Balancing Computational Efficiency and Optimality,” 2025 5th International Conference on Robotics, Automation and Intelligent Control (ICRAIC), Chengdu, China, 2025, pp. 1–12.
DOI: 10.1109/ICRAIC67376.2025.11375850