Task Offloading and Trajectory Optimization in UAV Networks:A Deep Reinforcement Learning Method Based on SAC and A-Star

作     者：Jianhua Liu Peng Xie Jiajia Liu Xiaoguang Tu 

作者机构：Institute of Electronics and Electrical EngineeringCivil Aviation Flight University of ChinaDeyang618307China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2024年第141卷第11期

页      面：1243-1273页

核心收录：

学科分类：08[工学] 082503[工学-航空宇航制造工程] 0825[工学-航空宇航科学与技术] 

基　　金：supported by the Central University Basic Research Business Fee Fund Project(J2023-027) Open Fund of Key Laboratory of Flight Techniques and Flight Safety,CAAC(No.FZ2022KF06) China Postdoctoral Science Foundation(No.2022M722248) 

主　　题：Mobile edge computing SAC communication security A-Star UAV 

摘      要：In mobile edge computing,unmanned aerial vehicles(UAVs)equipped with computing servers have emerged as a promising solution due to their exceptional attributes of high mobility,flexibility,rapid deployment,and terrain *** attributes enable UAVs to reach designated areas,thereby addressing temporary computing swiftly in scenarios where ground-based servers are overloaded or ***,the inherent broadcast nature of line-of-sight transmission methods employed by UAVs renders them vulnerable to eavesdropping ***,there are often obstacles that affect flight safety in real UAV operation areas,and collisions between UAVs may also *** solve these problems,we propose an innovative A*SAC deep reinforcement learning algorithm,which seamlessly integrates the benefits of Soft Actor-Critic(SAC)and A*(A-Star)*** algorithm jointly optimizes the hovering position and task offloading proportion of the UAV through a task offloading ***,our algorithm incorporates a path-planning function that identifies the most energy-efficient route for the UAV to reach its optimal hovering *** approach not only reduces the flight energy consumption of the UAV but also lowers overall energy consumption,thereby optimizing system-level energy *** simulation results demonstrate that,compared to other algorithms,our approach achieves superior system ***,it exhibits an average improvement of 13.18%in terms of different computing task sizes,25.61%higher on average in terms of the power of electromagnetic wave interference intrusion into UAVs emitted by different auxiliary UAVs,and 35.78%higher on average in terms of the maximum computing frequency of different auxiliary *** for path planning,the simulation results indicate that our algorithm is capable of determining the optimal collision-avoidance path for each auxiliary UAV,enabling them to safely reach their designated endpoints in div