Comprehensive Review of Path Planning Techniques for Unmanned Aerial Vehicles (UAVs) DOI
Pawan Kumar, Kunwar Pal, Mahesh Chandra Govil

и другие.

ACM Computing Surveys, Год журнала: 2025, Номер unknown

Опубликована: Май 29, 2025

Unmanned Aerial Vehicles (UAVs) have gained significant attention in recent years for their potential applications surveillance, monitoring, search and rescue, mapping. However, efficient optimal path planning remains a key challenge UAV navigation. This survey paper reviews various algorithms, encompassing Sampling-Based techniques, Potential Field methods, Bio-Inspired Artificial Intelligence-based approaches. We explore factors affecting planning, including environmental constraints, objectives, uncertainties. vital A comparative analysis of these techniques focuses on strengths, weaknesses, applicability different scenarios, heuristic, mathematical, Bio-Inspired, machine-learning methods. Critical parameters like length, flight time, number UAVs targets, dynamics, obstacle management, algorithmic approaches, real-time execution, collision avoidance are examined. aims to inform researchers, practitioners, engineers offering insights into techniques' challenges, limitations, future research directions. By presenting comprehensive overview state-of-the-art methods trends, our provides clear understanding the diverse path-planning strategies, merits demerits, highlights challenges unresolved issues field.

Язык: Английский

Cooperative Patrol Control of Multiple Unmanned Surface Vehicles for Global Coverage DOI Creative Commons
Yuan Liu,

Xirui Xu,

Guoxing Li

и другие.

Journal of Marine Science and Engineering, Год журнала: 2025, Номер 13(3), С. 584 - 584

Опубликована: Март 17, 2025

The cooperative patrol control of multiple unmanned surface vehicles (Multi-USVs) in dynamic aquatic environments presents significant challenges global coverage efficiency and system robustness. study proposes a algorithm for based on hybrid embedded task state information model reward reshaping techniques, addressing environments. By integrating patrol, collaboration, obstacle graphs, the generates kinematically feasible actions real time optimizes exploration-cooperation trade-off through dense structure. Simulation results demonstrate that achieves 99.75% 1 km × area, reducing completion by 23% 74% compared to anti-flocking partition scanning algorithms, respectively, while maintaining collision rates between agents (CRBAA) obstacles (CRBAO) below 0.15% 0.5%. Compared DDPG, SAC, PPO frameworks, proposed training framework (TFMUSV) 28% higher rewards with 40% smaller fluctuations later stages. This provides an efficient reliable solution autonomous monitoring search-rescue missions complex

Язык: Английский

Процитировано

0
A Novel HGW Optimizer with Enhanced Differential Perturbation for Efficient 3D UAV Path Planning DOI Creative Commons
Lei Lv, Hongjuan Liu, Ruofei He

и другие.

Drones, Год журнала: 2025, Номер 9(3), С. 212 - 212

Опубликована: Март 16, 2025

In general, path planning for unmanned aerial vehicles (UAVs) is modeled as a challenging optimization problem that critical to ensuring efficient UAV mission execution. The challenge lies in the complexity and uncertainty of flight scenarios, particularly three-dimensional scenarios. this study, one introduces framework 3D environment. To tackle challenge, we develop an innovative hybrid gray wolf optimizer (GWO) algorithm, named SDPGWO. proposed algorithm simplifies position update mechanism GWO incorporates differential perturbation strategy into search process, enhancing ability avoiding local minima. Simulations conducted various scenarios reveal SDPGWO excels rapidly generating superior-quality paths UAVs. addition, it demonstrates enhanced robustness handling complex environments outperforms other related algorithms both performance reliability.

Язык: Английский

Процитировано

0
Modified Grey Wolf Optimizer and Application in Parameter Optimization of PI Controller DOI Creative Commons

Long Sheng,

Sen Wu,

Zongyu Lv

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(8), С. 4530 - 4530

Опубликована: Апрель 19, 2025

The Grey Wolf Optimizer (GWO) is a well-known metaheuristic algorithm that currently has an extremely wide range of applications. However, with the increasing demand for accuracy, its shortcomings low exploratory and population diversity are increasingly exposed. A modified (M-GWO) proposed to tackle these weaknesses GWO. M-GWO introduces mutation operators different location-update strategies, achieving balance between exploration development. experiment validated performance using CEC2017 benchmark function compared results five other advanced algorithms: Improved (IGWO), GWO, Whale Optimization Algorithm (WOA), Dung Beetle (DBO), Harris Hawks (HHO). indicate better than competitor algorithms on all 29 functions in dimensions 30 50, except 26 dimension 28 50. Compared algorithms, most effective algorithm, overall effectiveness 96.5%. In addition, order show value practical engineering field, used optimize PI controller parameters current loop permanent magnet synchronous motor (PMSM) system. By designing parameter optimization scheme based M-GWO, fluctuation q-axis d-axis reduced. designed reduces around −2~1 −2~2 A. comparing current-tracking errors under validity optimized proved.

Язык: Английский

Процитировано

0
A distance determination wolf pack algorithm for solving high-dimensional complex functions and its application DOI
Yi‐Hsiang Lai, Husheng Wu, Qiang Peng

и другие.

The Journal of Supercomputing, Год журнала: 2025, Номер 81(8)

Опубликована: Май 16, 2025

Язык: Английский

Процитировано

0
Comprehensive Review of Path Planning Techniques for Unmanned Aerial Vehicles (UAVs) DOI
Pawan Kumar, Kunwar Pal, Mahesh Chandra Govil

и другие.

ACM Computing Surveys, Год журнала: 2025, Номер unknown

Опубликована: Май 29, 2025

Unmanned Aerial Vehicles (UAVs) have gained significant attention in recent years for their potential applications surveillance, monitoring, search and rescue, mapping. However, efficient optimal path planning remains a key challenge UAV navigation. This survey paper reviews various algorithms, encompassing Sampling-Based techniques, Potential Field methods, Bio-Inspired Artificial Intelligence-based approaches. We explore factors affecting planning, including environmental constraints, objectives, uncertainties. vital A comparative analysis of these techniques focuses on strengths, weaknesses, applicability different scenarios, heuristic, mathematical, Bio-Inspired, machine-learning methods. Critical parameters like length, flight time, number UAVs targets, dynamics, obstacle management, algorithmic approaches, real-time execution, collision avoidance are examined. aims to inform researchers, practitioners, engineers offering insights into techniques' challenges, limitations, future research directions. By presenting comprehensive overview state-of-the-art methods trends, our provides clear understanding the diverse path-planning strategies, merits demerits, highlights challenges unresolved issues field.

Язык: Английский

Процитировано

0