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Smart city article details
| Title | Kinodynamic Model-Based Uav Trajectory Optimization For Wireless Communication Support Of Internet Of Vehicles In Smart Cities |
|---|---|
| ID_Doc | 34565 |
| Authors | Eskandari M.; Savkin A.V.; Deghat M. |
| Year | 2024 |
| Published | Drones, 8, 10 |
| DOI | http://dx.doi.org/10.3390/drones8100574 |
| Abstract | Unmanned aerial vehicles (UAVs) are utilized for wireless communication support of Internet of Intelligent Vehicles (IoVs). Intelligent vehicles (IVs) need vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) wireless communication for real-time perception knowledge exchange and dynamic environment modeling for safe autonomous driving and mission accomplishment. UAVs autonomously navigate through dense urban areas to provide aerial line-of-sight (LoS) communication links for IoVs. Real-time UAV trajectory design is required for minimum energy consumption and maximum channel performance. However, this is multidisciplinary research including (1) dynamic-aware kinematic (kinodynamic) planning by considering UAVs’ motion and nonholonomic constraints; (2) channel modeling and channel performance improvement in future wireless networks (i.e., beyond 5G and 6G) that are limited to beamforming to LoS links with the aid of reconfigurable intelligent surfaces (RISs); and (3) real-time obstacle-free crash avoidance 3D trajectory optimization in dense urban areas by modeling obstacles and LoS paths in convex programming. Modeling and solving this multilateral problem in real-time are computationally prohibitive unless extensive computational and overhead processing costs are imposed. To pave the path for computationally efficient yet feasible real-time trajectory optimization, this paper presents UAV kinodynamic modeling. Then, it proposes a convex trajectory optimization problem with the developed linear kinodynamic models. The optimality and smoothness of the trajectory optimization problem are improved by utilizing model predictive control and quadratic state feedback control. Simulation results are provided to validate the methodology. © 2024 by the authors. |
| Author Keywords | autonomous navigation; intelligent vehicles (IVs); Internet of Things (IoTs); Internet of Vehicles (IoVs); kinematic-aware path planning; reconfigurable intelligent surface (RIS); sensor networks; trajectory optimization; unmanned aerial vehicles (UAVs); wireless communication |
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