Optimizing UAV Path Planning in Maritime Emergency Transportation: A Novel Multi-Strategy White Shark Optimizer

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(7), P. 1207 - 1207

Published: July 18, 2024

Maritime UAV path planning is a key link in realizing the intelligence of maritime emergency transportation, providing support for fast and flexible accident disposal material supply. However, most current methods are designed land environments lack ability to cope with complex marine environments. In order achieve effective environments, this paper proposes Directional Drive-Rotation Invariant Quadratic Interpolation White Shark Optimization algorithm (DD-RQIWSO). First, directional guidance speed realized through update strategy based on fitness value ordering, which improves individuals approaching optimal solution. Second, rotation-invariant mechanism hyperspheres added overcome tracking pause phenomenon WSO. addition, quadratic interpolation enhance utilization local information by algorithm. Then, wind simulation environment Lamb–Oseen vortex model was constructed better simulate real scenario. Finally, DD-RQIWSO subjected series tests 2D 3D scenarios, respectively. The results show that able realize under more accurately stably.

Language: Английский

Low-Altitude Sensing Model: Analysis Leveraging ISAC in Real-World Environments

Drones, Journal Year: 2025, Volume and Issue: 9(4), P. 283 - 283

Published: April 8, 2025

With the explosive growth of unmanned aerial vehicle (UAV) applications in numerous fields, low-altitude networks face formidable challenges monitoring. In this context, integrated sensing and communication (ISAC) through three-dimensional (3D) wide-area have emerged as key solution. However, differences networking mechanisms between sensing, along with transition from two-dimensional (2D) to 3D networking, complicate realization seamless sensing. We aimed address these by analyzing capabilities a single base station disparities Based on this, an innovative model for ISAC stations was proposed, defining boundaries providing foundation designing parameters stations. Additionally, multi-base (multi-BS) networked cellular-like architecture overcoming limitations traditional 2D achieving To validate effectiveness model, comprehensive tests were conducted both controlled laboratory conditions real-world commercial network environments. The results show that successfully achieved stable continuous expected coverage accuracy

Language: Английский

Heuristic Optimization-Based Trajectory Planning for UAV Swarms in Urban Target Strike Operations

Drones, Journal Year: 2024, Volume and Issue: 8(12), P. 777 - 777

Published: Dec. 20, 2024

Unmanned aerial vehicle (UAV) swarms have shown substantial potential to enhance operational efficiency and reduce strike costs, presenting extensive applications in modern urban warfare. However, achieving effective performance complex environments remains challenging, particularly when considering three-dimensional obstacles threat zones simultaneously, which can significantly degrade effectiveness. To address this challenge, paper proposes a target strategy using the Electric Eel Foraging Optimization (EEFO) algorithm, heuristic optimization method designed ensure precise strikes environments. The problem is formulated with specific constraints, modeling each UAV as an electric eel random initial positions velocities. This algorithm simulates interaction, resting, hunting, migrating behaviors of eels during their foraging process. During interaction phase, UAVs engage global exploration through communication environmental sensing. resting phase allows temporarily hold positions, preventing premature convergence local optima. In hunting swarm identifies pursues optimal paths, while migration transition areas, avoiding threats seeking safer routes. enhances overall capabilities by sharing information among surrounding individuals promoting group cooperation, effectively planning flight paths for strikes. MATLAB(R2024b) simulation platform used compare five algorithms—SO, SCA, WOA, MFO, HHO—against proposed missions. experimental results demonstrate that sparse undefended environment, EEFO outperforms other algorithms terms trajectory efficiency, stability, minimal costs also exhibiting faster rates. densely defended environments, not only achieves but shows superior trends cost reduction, along highest mission completion rate. These highlight effectiveness both scenarios, making it promising approach operations dynamic

Language: Английский