Adaptive Evolutionary Search Algorithm With Obstacle Avoidance for Multiple Uavs

Abstract

Unmanned air vehicles (UAVs) can be used for surveillance and searching, usually with a specified goal i.e. a given area to search or, person or object to find. In these cases, multiple UAVs can be more effective for searching than a single vehicle due to the greater area of coverage that they offer. This paper outlines a search methodology, based on evolutionary algorithms (EAs), which guides a swarm of UAVs throughout a search mission. The autonomously navigating vehicles also avoid unforeseen obstacles via reactive obstacle sensing.

UAV flight coordination for communication networks: genetic algorithms versus game theory

“…Their analysis considers factors like fuel consumption and terrain avoidance, demonstrating a new level of complexity in path planning. Carruthers et al (2005) investigate a search method for multi-UAV missions related to surveillance and searching in unknown areas using GAs. Their work allows several UAVs to dynamically fly through a search space and autonomously navigate by avoiding obstacles, without a priori knowledge of the environment.…”

Network-Aware Genetic Algorithms for the Coordination of MALE UAV Networks

“…In most of these contributions, the communication network between the UAVs is assumed. Exemplar works include Carruthers et al, [6] where the authors proposed a GA-based collision-aware coordination system for UAV missions related to surveillance and searching in unknown areas, with the assumption that the communications are constantly available.…”

Evolutionary Coordination System for Fixed-Wing Communications Unmanned Aerial Vehicles

“…In [3], the authors investigate a search method for multi-UAV missions related to surveillance and searching unknown areas, using EAs. Their work allows several UAVs to dynamically fly throughout a search space and autonomously navigate by avoiding unforeseen obstacles, without a priori knowledge of the environment.…”