Planning a drone fleet using artificial intelligence for search and rescue missions
Mario Hernandez Dominguez, Sergio Nesmachnow, José-Isidro Hernández-Vega · 2017
This article presents the simulation genetic algorithms with multi-agent system application to solve problems of collaboration and coordination, with the goal of minimizing the travel time of a fleet of drones. The experimental analysis compares the travel time of a deterministic algorithm versus a probabilistic algorithm in a multi-agent system with BDI architecture. The results show that the genetic algorithm delivers significant improvements in travel time, exceeding the deterministic algorithm by up to 35% on average. This article also discusses the feasibility of combining genetic algorithms and multi-agent systems for solving people search and rescue problems.