Abstract
In the 6G-IoT convergence ecosystem, UAV path planning for static environments is systematically investigated as a resource coordination problem where communication demands and terrain constraints are balanced through intelligent trajectory optimization. The innovation of this paper lies in the proposal of an interactive cylinder vector teaching-learning-based optimization (ICVTLBO) algorithm, where UAV trajectory points are represented in cylindrical coordinates, and targeted interactive strategies are proposed during the teacher and learner phases to address uncertainty challenges, such as terrain elevation fluctuations and communication link instability caused by obstacles in static environments. The ICVTLBO is compared with other classical and novel algorithms on the CEC2022 benchmark function suite, demonstrating its effectiveness and reliability in solving complex optimization problems. Subsequently, real digital elevation model (DEM) maps are utilized to establish nine diverse terrain scenarios for the simulation of 3D UAV path planning challenges, and experimental results show that the ICVTLBO outperforms other methods, providing high-quality paths for UAVs in complex environments.