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Abstract
The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy, and intelligence. In this article, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.
Highlights
In nature, to make up for the deficiencies of a single individual, many biological populations form coordinated and shocking cluster sports scenes through mutual communication and cooperation between individuals, such as predation of wolves, aggregation and migration of birds, and the gathering of bees, honey, ant colony movement [1], etc
Aiming at the application of the algorithm in actual scenarios, three robots inspired by termites can build pyramids and other shapes based on simple rule and local perception[11]. [12] developed an e-puck robot for teaching, and realized the cooperative behavior of 20 robots such as gathering and foraging. [13] designed the low-cost Kilobot robot, and designed the collaborative functions such as foraging and formation, and carried out thousands of cluster demonstrations, achieving the scale of the robot swarm breaking through a thousand orders. [14] combined with swarm cooperative observe orient decide to act (OODA) loop, the formation flight of more than 10 unmanned aerial vehicle (UAV) was realized for the first time [15]
How to choose strategies based on uncertainty information will directly affect the success or failure of the UAV task. [54] introduced a situation matrix to simulate the indeterminacy of the war information, and established a multi-UAV confrontation model based on uncertain information
Summary
To make up for the deficiencies of a single individual, many biological populations form coordinated and shocking cluster sports scenes through mutual communication and cooperation between individuals, such as predation of wolves, aggregation and migration of birds, and the gathering of bees, honey, ant colony movement [1], etc. The classic algorithms include particle swarm optimization algorithm [4][5], ant colony algorithm [6], which are often used in cluster collaborative control scenarios such as path planning and task allocation. Coo W J and others believe that the UAV swarm task planning problem belongs to the combination optimization of complex problems, and it is planned to use a layered control method to solve such problems from the perspective of operations research [19]. [20] proposed a six-layer hierarchical structure off drones swarms, CoMPACT, which effectively combines task planning, dynamic registration, reactive motion planning, and sudden biologically inspired group behaviors. The control Layer performs task coordination between clusters according to path information, and realizes automatic obstacle avoidance and formation control[77]-[98].
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