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Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: a comprehensive review

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Abstract

Over the past decade, unmanned aerial vehicles (UAVs) have demonstrated increasing promise. In this context, we provide a review on swarm intelligence algorithms that play an extremely important role in multiple UAV collaborations. The study focuses on four aspects we consider relevant for the topic: collision avoidance, task assignment, path planning, and formation reconfiguration. A comprehensive investigation of selected typical algorithms that analyses their merits and demerits in the context of multi-UAV collaboration is presented. This research summarises the basic structure of swarm intelligence algorithms, which consists of several fundamental phases; and provides a comprehensive survey of swarm intelligence algorithms for the four aspects of multi-UAV collaboration. Besides, by analysing these key technologies and related applications, the research trends and challenges are highlighted. This broad review is an outline for scholars and professionals in the field of UAV swarms.

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Abbreviations

2D:

Two-dimensional

3D:

Three-dimensional

ABC:

Artificial bee colony

ABCIS:

ABC with intellective search and special division

ACO:

Ant colony optimization

AEFA:

Artificial electric field algorithm

CCA:

Cooperative coevolutionary algorithm

CPTD:

Control parametrization and time discretization

CM:

Cauchy mutant

DBF:

Dynamic Bayesian framework

DBVF:

Distance-based value function

DE:

Differential evolution

EKF:

Extended Kalman filter

EP:

Evolutionary programming

ES:

Evolutionary strategy

FESGA:

Fuzzy elite strategy genetic algorithm

GA:

Genetic algorithm

GAP:

Generalized assignment problem

GH:

Greedy heuristic

GS:

Greedy strategy

GSA:

Gravitational search algorithm

GWO:

Grey wolf optimizer

IBA:

Inspired bat algorithm

IoT:

Internet-of-things

LSO:

Lion swarm optimisation

MFOA:

Multi-swarm fruit fly optimization algorithm

MOSFLA:

Multi-objective shuffled frog-leaping algorithm

MPGA:

Multi-population GA

MTS:

Minimum time search

NNA:

Nearest neighbour algorithm

PIO:

Pigeon-inspired optimization

PRM:

Probabilistic roadmap

PSO:

Particle swarm optimization

SAA:

Simulated annealing algorithm

SCA:

Sine cosine algorithm

SMC:

Sequential Monte Carlo

SOS:

Symbiotic organisms search

SSA:

Sparrow search algorithm

TSP:

Travelling salesman problem

UAV:

Unmanned aerial vehicle

UGV:

Unmanned ground vehicle

WOA:

Whale optimization algorithm

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Acknowledgements

The authors would like to express their sincere thanks to the members of the editorial team and anonymous reviewers whose valuable comments and contributions contributed to significantly improve this paper. This work is supported by National Natural Science Foundation of China (No. 62073330).

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  1. College of Systems Engineering, National University of Defence Technology, Changsha, China

    Jun Tang & Songyang Lao

  2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China

    Haibin Duan

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Tang, J., Duan, H. & Lao, S. Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: a comprehensive review. Artif Intell Rev 56, 4295–4327 (2023). https://doi.org/10.1007/s10462-022-10281-7

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