A Survey on Multi-UAV Path Planning: Classification, Algorithms, Open Research Problems, and Future Directions

Drones, Journal Year: 2025, Volume and Issue: 9(4), P. 263 - 263

Published: March 31, 2025

Multi-UAV path planning algorithms are crucial for the successful design and operation of unmanned aerial vehicle (UAV) networks. While many network researchers have proposed UAV to improve system performance, an in-depth review multi-UAV has not been fully explored yet. The purpose this study is survey, classify, compare existing in literature over last eight years various scenarios. After detailing classification, we based on time consumption, computational cost, complexity, convergence speed, adaptability. We also examine approaches, including metaheuristic, classical, heuristic, machine learning, hybrid methods. Finally, identify several open research problems further investigation. More required smart that can re-plan pathways fly real complex Therefore, aims provide insight into engineers contribute next-generation systems.

Language: Английский

A Hybrid Optimization Framework for Dynamic Drone Networks: Integrating Genetic Algorithms with Reinforcement Learning

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(9), P. 5176 - 5176

Published: May 6, 2025

The growing use of unmanned aerial vehicles (UAVs) in diverse fields such as disaster recovery, rural regions, and smart cities necessitates effective dynamic drone network establishment techniques. Conventional optimization techniques like genetic algorithms (GAs) particle swarm (PSO) are weak when it comes to real-time adjustment the environment multi-objective constraints. This paper proposes a hybrid framework combining reinforcement learning (RL) improve deployment networks. We integrate Q-learning into GA mutation process allow drones adaptively adjust locations real time under coverage, connectivity, energy In scenario large-scale simulations for wildfire tracking, response, urban monitoring tasks, approach performs better than PSO. greatest enhancements 6.7% greater 7.5% less average link distance, faster convergence optimal deployment. proposed allows establish strong stable networks that nature adapt mission demands with efficient coordination. research has important applications autonomous UAV systems mission-critical where adaptability robustness essential.

Language: Английский

Adaptive Path Planning for Multi-UAV Systems in Dynamic 3D Environments: A Multi-Objective Framework

Designs, Journal Year: 2024, Volume and Issue: 8(6), P. 136 - 136

Published: Dec. 20, 2024

This study evaluates and compares the computational performance practical applicability of advanced path planning algorithms for Unmanned Aerial Vehicles (UAVs) in dynamic obstacle-rich environments. The Adaptive Multi-Objective Path Planning (AMOPP) framework is highlighted its ability to balance multiple objectives, including length, smoothness, collision avoidance, real-time responsiveness. Through experimental analysis, AMOPP demonstrates superior performance, with a 15% reduction length compared A*, achieving an average 450 m. Its angular deviation 8.0° ensures smoother trajectories than traditional methods like Genetic Algorithm Particle Swarm Optimization (PSO). Moreover, achieves 0% rate across all simulations, surpassing heuristic-based Cuckoo Search Bee Colony Optimization, which exhibit higher rates. Real-time responsiveness another key strength AMOPP, re-planning time 0.75 s, significantly outperforming A* RRT*. complexities each algorithm are analyzed, exhibiting complexity O(k·n) space O(n), ensuring scalability efficiency large-scale operations. also presents comprehensive qualitative quantitative comparison 14 using 3D visualizations, highlighting their strengths, limitations, suitable application scenarios. By integrating weighted optimization penalty-based strategies spline interpolation, provides robust solution UAV planning, particularly scenarios requiring smooth navigation adaptive re-planning. work establishes as promising real-time, efficient, safe operations

Language: Английский