Research Article
Multi-agent Simulation for Scheduling and Path Planning of Autonomous Intelligent Vehicles
@INPROCEEDINGS{10.1007/978-3-031-57523-5_15, author={Kader Sanogo and M’hammed Sahnoun and Abdelkader Mekhalef Benhafssa}, title={Multi-agent Simulation for Scheduling and Path Planning of Autonomous Intelligent Vehicles}, proceedings={Simulation Tools and Techniques. 15th EAI International Conference, SIMUtools 2023, Seville, Spain, December 14-15, 2023, Proceedings}, proceedings_a={SIMUTOOLS}, year={2024}, month={4}, keywords={Simulation AIV Job-shop scheduling FMS Multi-agent System Industry 5.0}, doi={10.1007/978-3-031-57523-5_15} }- Kader Sanogo
M’hammed Sahnoun
Abdelkader Mekhalef Benhafssa
Year: 2024
Multi-agent Simulation for Scheduling and Path Planning of Autonomous Intelligent Vehicles
SIMUTOOLS
Springer
DOI: 10.1007/978-3-031-57523-5_15
Abstract
Autonomous and Guided Vehicles (AGVs) have long been employed in material handling but necessitate significant investments, such as designating specific movement areas. As an alternative, Autonomous and Intelligent Vehicles (AIVs) have gained traction due to their adaptability, intelligence, and capability to handle unexpected obstacles. Yet, challenges like optimizing scheduling and path planning, and managing routing conflicts persist. This study introduces a simulator tailored for AIV scheduling and path planning in various production systems. The simulator supports both predictive, where paths are pre-determined, and dynamic scheduling, with real-time optimization. Paths are determined using Dijkstra’s method, ensuring AIVs use the shortest route. When path-sharing conflicts arise, a multi-criteria priority system comes into play, and its impact on the makespan is assessed. Experimental results highlight the advantage of AIVs over AGVs in most scenarios and the simulator’s efficiency in generating effective schedules, incorporating the priority management system.
