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链路可靠性约束下蜂窝连接无人机的三维全局路径规划优化

3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint.

作者信息

Behjati Mehran, Nordin Rosdiadee, Zulkifley Muhammad Aidiel, Abdullah Nor Fadzilah

机构信息

Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.

出版信息

Sensors (Basel). 2022 Nov 19;22(22):8957. doi: 10.3390/s22228957.

Abstract

This paper proposes an effective global path planning technique for cellular-connected UAVs to enhance the reliability of unmanned aerial vehicles' (UAVs) flights operating beyond the visual line of sight (BVLOS). Cellular networks are considered one of the leading enabler technologies to provide a ubiquitous and reliable communication link for UAVs. First, this paper investigates a reliable aerial zone based on an extensive aerial drive test in a 4G network within a suburban environment. Then, the path planning problem for the cellular-connected UAVs is formulated under communication link reliability and power consumption constraints. To provide a realistic optimization solution, all constraints of the optimization problem are defined based on real-world scenarios; in addition, the presence of static obstacles and no-fly zones is considered in the path planning problem. Two powerful intelligent optimization algorithms, the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, are used to solve the defined optimization problem. Moreover, a combination of both algorithms, referred to as PSO-GA, is used to overcome the inherent shortcomings of the algorithms. The performances of the algorithms are compared under different scenarios in simulation environments. According to the statistical analysis of the aerial drive test, existing 4G base stations are able to provide reliable aerial coverage up to a radius of 500 m and a height of 85 m. The statistical analysis of the optimization results shows that PSO-GA is a more stable and effective algorithm to rapidly converge to a feasible solution for UAV path planning problems, with a far faster execution time compared with PSO and GA, about two times. To validate the performance of the proposed solution, the simulation results are compared with the real-world aerial drive test results. The results comparison proves the effectiveness of the proposed path planning method in suburban environments with 4G coverage. The proposed method can be extended by identifying the aerial link reliability of 5G networks to solve the UAV global path planning problem in the current 5G deployment.

摘要

本文提出了一种有效的蜂窝连接无人机全局路径规划技术,以提高无人机在超视距(BVLOS)飞行时的可靠性。蜂窝网络被认为是为无人机提供无处不在且可靠通信链路的主要使能技术之一。首先,本文基于在郊区环境的4G网络中进行的广泛空中驾驶测试,研究了一个可靠的空中区域。然后,在通信链路可靠性和功耗约束下,对蜂窝连接无人机的路径规划问题进行了公式化。为了提供一个现实的优化解决方案,优化问题的所有约束都是基于实际场景定义的;此外,路径规划问题中考虑了静态障碍物和禁飞区的存在。使用两种强大的智能优化算法,即遗传算法(GA)和粒子群优化(PSO)算法,来解决定义的优化问题。此外,将这两种算法结合起来,称为PSO - GA,以克服算法的固有缺点。在模拟环境中的不同场景下比较了算法的性能。根据空中驾驶测试的统计分析,现有的4G基站能够提供半径达500米、高度达85米的可靠空中覆盖。优化结果的统计分析表明,PSO - GA是一种更稳定、有效的算法,能够快速收敛到无人机路径规划问题的可行解,与PSO和GA相比,执行时间快得多,大约快两倍。为了验证所提解决方案的性能,将模拟结果与实际空中驾驶测试结果进行了比较。结果比较证明了所提路径规划方法在4G覆盖的郊区环境中的有效性。通过识别5G网络的空中链路可靠性,可以扩展所提方法,以解决当前5G部署中的无人机全局路径规划问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c37/9695336/7cfed5057f2a/sensors-22-08957-g001.jpg

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