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Hybrid metaheuristic-driven 3D path planning for UAVs in complex urban environments: a multi-objective fusion framework.

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Titel: Hybrid metaheuristic-driven 3D path planning for UAVs in complex urban environments: a multi-objective fusion framework.
Autoren: Cheng, Qing, Zhang, Zhengyuan, Du, Yunfei, Zhao, Xi
Quelle: Geocarto International; Dec2025, Vol. 40 Issue 1, p1-31, 31p
Schlagwörter: ROBOTIC path planning, TRAJECTORY optimization, DRONE aircraft, TRANSPORTATION industry, MATHEMATICAL optimization, PARTICLE swarm optimization, GENETIC algorithms, OPTIMIZATION algorithms
Abstract: Unmanned Aerial Vehicles (UAVs) offer transformative potential for urban logistics, but their deployment is challenged by complex 3D path planning in constrained environments. This study introduces GS-PSO, a novel multi-stage hybrid framework designed to address this challenge by sequentially integrating Particle Swarm Optimization (PSO), Sparrow Search Algorithm (SSA), and a Genetic Algorithm (GA). Key innovations include dynamic chaotic inertia weights in PSO for enhanced exploration and specialized GA operators for fine-grained refinement. The efficacy of GS-PSO was validated on the CEC2020 benchmarks, where it found the global optimum in 8/10 functions, showing superior accuracy and stability over standard PSO. In high-fidelity UAV simulations, GS-PSO generated paths up to 22.9% shorter than the AVOA, significantly outperforming competitors. Statistical tests (p < 0.05) confirmed its superior solution quality and robustness. This work provides an effective, reliable solution for complex UAV trajectory planning. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract:Unmanned Aerial Vehicles (UAVs) offer transformative potential for urban logistics, but their deployment is challenged by complex 3D path planning in constrained environments. This study introduces GS-PSO, a novel multi-stage hybrid framework designed to address this challenge by sequentially integrating Particle Swarm Optimization (PSO), Sparrow Search Algorithm (SSA), and a Genetic Algorithm (GA). Key innovations include dynamic chaotic inertia weights in PSO for enhanced exploration and specialized GA operators for fine-grained refinement. The efficacy of GS-PSO was validated on the CEC2020 benchmarks, where it found the global optimum in 8/10 functions, showing superior accuracy and stability over standard PSO. In high-fidelity UAV simulations, GS-PSO generated paths up to 22.9% shorter than the AVOA, significantly outperforming competitors. Statistical tests (p < 0.05) confirmed its superior solution quality and robustness. This work provides an effective, reliable solution for complex UAV trajectory planning. [ABSTRACT FROM AUTHOR]
ISSN:10106049
DOI:10.1080/10106049.2025.2538099