Metaheuristic Optimization for Multi-AGV Path Planning in Search and Rescue Missions
Arwa H. Ghoneim, Ahmed M. Elsoda, Heba A. Abdelmawla, Khaled Elbadawy, Linah Tamer, Lobna Tarek, Jessica Magdy, Omar M. Shehata · 2025
This paper addresses optimizing multi-Autonomous Ground Vehicle (AGV) coordination in autonomous ground search and rescue missions, focusing on enhancing exploration efficiency through collaborative path planning. By modeling the environment as a 2D grid and leveraging optimization algorithms, the aim is to maximize area coverage while minimizing exploration time. The optimization problem is formulated based on travel distance and the number of explored grid cells. Simulated annealing (SA), Genetic algorithm (GA), Particle Swarm Algorithm (PSO) and Artificial Protozoa Optimizer (APO) are applied to generate optimized paths for multiple AGVs, ensuring efficient task distribution. The performance of each algorithm is evaluated based on the overall objective function and computational time. The results demonstrate the framework’s capability to improve exploration efficiency and path allocation, making it a suitable approach for AGV coordination in large-scale environments.