Hybrid Route Optimisation for Maximum Air to Ground Channel Quality

Abstract

The urban air mobility market is expected to grow constantly due to the increased interest in new forms of transportation. Managing aerial vehicles fleets, dependent on rising technologies such as artificial intelligence and automated ground control stations, will require a solid and uninterrupted connection to complete their trajectories. A path planner based on evolutionary algorithms to find the most suitable route has been previously proposed by the authors. Herein, we propose using particle swarm and hybrid optimisation algorithms instead of evolutionary algorithms in this work. The goal of speeding the route planning process and reducing computational costs is achieved using particle swarm and direct search algorithms. This improved path planner efficiently explores the search space and proposes a trajectory according to its predetermined goals: maximum air-to-ground quality, availability, and flight time. The proposal is tested in different situations, including diverse terrain conditions for various channel behaviours and no-fly zones.