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UAV Path Planning Using Optimization Approaches: A Survey

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Abstract

Path planning is one of the most important steps in the navigation and control of Unmanned Aerial Vehicles (UAVs). It ensures an optimal and collision-free path between two locations from a starting point (source) to a destination one (target) for autonomous UAVs while meeting requirements related to UAV characteristics and the serving area. In this paper, we present an overview of UAV path planning approaches classified into five main categories including classical methods, heuristics, meta-heuristics, machine learning, and hybrid algorithms. For each category, a critical analysis is given based on targeted objectives, considered constraints, and environments. In the end, we suggest some highlights and future research directions for UAV path planning.

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Acknowledgements

This work is supported by the Directorate General for Scientific Research and Technological Development (DG-RSDT) of Algeria; and the “ADI 2021” project funded by the IDEX Paris-Saclay, ANR-11-IDEX-0003-02.

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Authors and Affiliations

  1. Laboratoire Ingénierie des Systèmes et Télécommunications, Université M’Hamed Bougara, Avenue de l’Indépendence, 35000, Boumerdes, Algeria

    Amylia Ait Saadi & Yassine Meraihi

  2. LISV Laboratory, University of Paris-Saclay, 10-12 Avenue of Europe, 78140, Velizy, France

    Amylia Ait Saadi & Amar Ramdane-Cherif

  3. ECE Paris School of Engineering, 37 quai de Grenelle, 75015, Paris, France

    Assia Soukane

  4. Laboratoire de Méthodes de Conception de Systèmes, Ecole nationale Supérieure d’Informatique, Algiers, Algeria

    Asma Benmessaoud Gabis

  5. Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, Fortitude Valley, QLD, 4006, Australia

    Seyedali Mirjalili

  6. YFL (Yonsei Frontier Lab), Yonsei University, Seoul, Korea

    Seyedali Mirjalili

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  1. Amylia Ait Saadi
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Correspondence to Yassine Meraihi.

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A: Appendix

A: Appendix

In the present appendix, we gather in a table the set of acronyms used in this paper and their meanings.

Acronym

Explanation

ABC

Artificial Bee Colony

ACO

Ant Colony Optimization

AGASA

Self Adaptive Genetic Simulated Annealing algorithm

APF

Artificial Potential Field

APF-IRP

Artificial Potential Field Improved Rolling Plan

AGOA

Adaptive Grasshopper Optimization Algorithm

aHJB

Hamilton Jacobi Bellman

ALO

Ant Lion Optimizer

ARE

Adaptive and Random Exploration

A*

A-Star

AS-N

Ant Colony Optimization with punitive measures

AT-PP

Average Throughput Path planning

ASTS

Ant System Tabu Search

BA

Bat Algorithm

BAA*

Bidirectional Adaptive A-star

BBO

Biogeography Based Optimization

BLP

Bi-Level Programming

BS

Base Station

CBGA

Center Based Genetic Algorithm

CBPSO

Chaos Based initialization Particle Swarm Optimization

CC-RRT

Chance Constraint Rapidly-exploring Random Tree

CFO

Central Force Optimization

CGLA

Cooperative and Geometric Learning Algorithm

CIPSO

Comprehensively Particle Swarm Optimization

CPSO

Constrained Particle Swarm Optimization

CS

Cuckoo Search

DA

Dragonfly Algorithm

DAALO

Dynamic Adaptive Ant Lion Optimizer

DBSCAN

Density Based Spatial Clustering of Application with Noise

DDRRT

Dynamic Domain Rapidly-exploring Random Tree

DE

Differential Evolution

Deep RL ESN

Deep Reinforcement Learning Echo State Network

Deep Sarsa

Deep State action reward state action

DELC

Differential Evolution with Level Comparison

DPSO

Discrete Particle Swarm Optimization

DQN

Deep Q Network

DRL

Deep Reinforcement Learning

D3QN

Dueling Double Deep Q Networks

Dubins SAS

Dubins Sparse A-Star

ECoVG

Elliptical Concave Visibility Graph

\(\epsilon\)DE

Constrained Differential Evolution

EEGWO

Exploration Enhanced Grey Wolf Optimizer

ESOVG

Equilateral Space Oriented Visibility Graph

ePFC

Extended Potential Field Controller

EPF-RRT

Environmental Potential Field Rapidly-exploring Random Tree

FA

Firefly Algorithm

FA-DE

Fuzzy Adaptive Differential Evolution

FBCRI

Feedback Based CRI

FDFR

First Detect First Reserve

FOA

Fruit fly Optimization Algorithm

FVF

Fuzzy Virtual Force

FWA

Firework Algorithm

GA

Genetic Algorithm

GA-LRO

Genetic Algorithm-Local Rolling Optimization

GBPSO

Global Best Particle Swarm Optimization

GEDGWO

Gaussian Estimation of Distribution Grey Wolf Optimizer

GH

Greedy Heuristic

GLS

Guided Local Search

GNSS

Global Navigation Satellite System

GP

Gaussian Process

GP-RRT

Gaussian Process Rapidly-exploring Random Tree

GSO

Glowworm Swarm Optimization

GTSP

Generalized Traveling Salesman Problem

GWO

Grey Wolf Optimizer

HDDRRT

Heuristic Dynamic Domain Rapidly-exploring Random Tree

HGA

Hybrid Genetic Algorithm

HPF

Hierarchical Potential Field

HR-MAGA

Hierarchical Recursive Multi Agent Genetic Algorithm

HSGWO-MSOS

Hybrid Simplified Grey Wolf Optimizer-Modified Symbiotic Organism Search

HVFA

Hybrid Virtual Force A* search

IABC

Improved Artificial Bee Colony

IBA

Intelligent BAT Algorithm

ICA

Imperialist Competitive Algorithm

IFFO

Improved Fruit fly Optimization

IGWO

Improved Grey Wolf Optimizer

IITD

Improved Intelligent Water Drop algorithm

ITD

Intelligent Water Drop algorithm

IPS

Improved Path Smoothing

IRRT

Improved Rapidly-exploring Random Tree

IRRT*

Informed Rapidly-exploring Random Tree-Star

IWOA

Improved Whale Optimization Algorithm

IRRTC

Improved Rapidly-exploring Random Tree Connect

LCPSO

Linear varying Coefficient Particle Swarm Optimization

LEVG

Layered Essential Visibility Graph

LKH

Lin Kernighan

LRTA-Star

Learning Real Time A-star

LVPSO

Linear varying maximum Velocity Particle Swarm Optimization

MACO

Modified Ant Colony Optimization

MCFO

Modified Central Force Optimization

mDELC

improved Differential Evolution with Level Comparison

MFO

Moth Flame Optimization

MFOA

Multi-swarm Fruit fly Optimization Algorithm

MGA

Modified Genetic Algorithm

MGOA

Modified Grey Wolf Algorithm

mHJB

Opportunistic Hamilton Jacobi Bellman

MHS

Modified Harmony Search

MILP

Mixed Integer Linear Programming

MMACO-DE

Maximum Minimum Ant Colony Optimization Differential Evolution

MMAS

Maximum Minimum Ant System

MPC

Model Predictive Control

MP-CGWO

Multi Population-Chaotic Grey Wolf Optimizer

MPFM

Modified Potential Field Method

MPGA

Multi-Population Genetic Algorithm

MT-PP

Maximum Throughput Path planning

mVD

Modified Voronoi Diagram

mVGA

Multi-frequency Vibrational Genetic Algorithm

mVGA\(^v\)

Multi-frequency Vibrational Genetic Algorithm with voronoi

MVO

Multi-Verse Optimizer

MWPS

Modified Wolf Packet Search

NBO

Nominal Belief-state Optimization

NFZ-DDRRT

No-Fly Zone Dynamic Domain Rapidly-exploring Random Tree

NSGA

Non-dominated Sorting Genetic Algorithm

NBGA

Neighborhood Based Genetic Algorithm

OGCA

Obstacle-free Graph Construction Algorithm

OGSA

Obstacle-free Graph Search Algorithm

oHJB

Opportunistic Hamilton Jacobi Bellman

OPP

Optimal Path Planning

PDE

Partial Differential Equation

PH

Pythagorean Hodograph

PIO

Pigeon Inspired Optimization

PIOFOA

Pigeon Inspired Optimization Fruit fly Optimization Algorithm

P-MAGA

Path planning Multi-Agent Genetic Algorithm

PMPSO

Position Mutation Particle Swarm Optimization

POMDP

Partially Observable Markov Decision Process

PPPIO

Predator Prey Pigeon Inspired Optimization

PRM

Probabilistic Road Map

PSO

Particle Swarm Optimization

PSO-APF

Particle Swarm Optimization-Artificial Potential Field

PSO-GA

Particle Swarm Optimization -Genetic Algorithm

PSOGSA

Particle Swarm Optimization Gravitational Search Algorithm

PSOPC

Particle Swarm Optimizer with Passive Congregation

QoS

Quality of Service

QPSO

Quantum Particle Swarm Optimization

RBF-ANN

Radial Basis Functions Artificial Neural Networks

RGA

Regular Genetic Algorithm

RGV

Reduced Visibility Graph

RHC

Receding Horizon Control

RLGWO

Reinforcement learning Grey Wolf Optimizer

RRT

Rapidly-exploring Random Tree

RRTC

Rapidly-exploring Random Tree Connect

RRT*

Rapidly-exploring Random Tree-Star

RRT* G

Rapidly-exploring Random Tree-Star Goal

RRT* GL

RRT Goal Limit

RRT* L

Rapidly-exploring Random Tree-Star Limit

RS

Random Search

RSU

Road Site Unit

RVW

Rendez-Vous Waypoints

Sarsa

State action reward state action

SA

Simulated Annealing algorithm

SADE

Self Adaptive Differential Evolution

SAS

Sparse A* Search

SCA

Sine Cosine Algorithm

SCPIO

Social Class Pigeon Inspired Optimization

SDPIO

Slow Diving Pigeon Inspired Optimization

SH

Short Horizon algorithm

SHA

Self Heuristic Ant

SHC

Short Horizon Cooperative algorithm

SICQ

Simultaneous Inform and Connect with Quality of service

SIC+

Simultaneous Inform and Connect following Quality of service

SOM

Self Organisation Map

SOMR

Surface Of Minimum Risk

SOS

Symbiotic Organism Search

SVM

Support Vector Machine

TADDRRT

Threat Assessment based Dynamic Domain Rapidly-exploring Random Tree

TARRT*

Threat Assessment based RRT* Rapidly-exploring Random Tree-Star

\(\theta\)-MAFOA

\(\theta\)-Mutation Adaptation Fruit Fly Optimization Algorithm

\(\theta\)-QPSO

Phase-encoded Quantum Particle Swarm Optimization algorithm

\(\theta\)-PSO

Phase-encoded Particle Swarm Optimization algorithm

TLBO

Teaching Learning Based Optimization

TLP-COA

Tri Level Programming Cognitive behavior Optimization Algorithm

TSP

Traveling Salesman Problem

UAV

Unmanned Aerial Vehicle

UGV

Unmanned Ground Vehicle

VD

Voronoi Diagram

VGA

Vibrational Genetic Algorithm

VPB-RRT

Variable Probability based bidirectional Rapidly-exploring Random Tree

WOA

Whale Optimization Algorithm

WPS

Wolf Packet Search

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Ait Saadi, A., Soukane, A., Meraihi, Y. et al. UAV Path Planning Using Optimization Approaches: A Survey. Arch Computat Methods Eng 29, 4233–4284 (2022). https://doi.org/10.1007/s11831-022-09742-7

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