The overall hardware construction of autopilot within micro aerial vehicle is presented. The boot process of VxWorks real time operating system as well as the conception and function of board support package （BSP） i...The overall hardware construction of autopilot within micro aerial vehicle is presented. The boot process of VxWorks real time operating system as well as the conception and function of board support package （BSP） is described. And the transplantation process of the VxWroks operat ing system into the hardware platform mentioned above is highlighted. It is shown from the final re sults that VxWorks has high stability and real time performance, ensuring accurate flight control and a smooth flight of the micro aerial vehicle.展开更多
Micro-UAV swarms usually generate massive data when performing tasks. These data can be harnessed with various machine learning(ML) algorithms to improve the swarm’s intelligence. To achieve this goal while protectin...Micro-UAV swarms usually generate massive data when performing tasks. These data can be harnessed with various machine learning(ML) algorithms to improve the swarm’s intelligence. To achieve this goal while protecting swarm data privacy, federated learning(FL) has been proposed as a promising enabling technology. During the model training process of FL, the UAV may face an energy scarcity issue due to the limited battery capacity. Fortunately, this issue is potential to be tackled via simultaneous wireless information and power transfer(SWIPT). However, the integration of SWIPT and FL brings new challenges to the system design that have yet to be addressed, which motivates our work. Specifically,in this paper, we consider a micro-UAV swarm network consisting of one base station(BS) and multiple UAVs, where the BS uses FL to train an ML model over the data collected by the swarm. During training, the BS broadcasts the model and energy simultaneously to the UAVs via SWIPT, and each UAV relies on its harvested and battery-stored energy to train the received model and then upload it to the BS for model aggregation. To improve the learning performance, we formulate a problem of maximizing the percentage of scheduled UAVs by jointly optimizing UAV scheduling and wireless resource allocation. The problem is a challenging mixed integer nonlinear programming problem and is NP-hard in general. By exploiting its special structure property, we develop two algorithms to achieve the optimal and suboptimal solutions, respectively. Numerical results show that the suboptimal algorithm achieves a near-optimal performance under various network setups, and significantly outperforms the existing representative baselines. considered.展开更多
基金Supported by the Ministerial Level Foundation(A222006450)
文摘The overall hardware construction of autopilot within micro aerial vehicle is presented. The boot process of VxWorks real time operating system as well as the conception and function of board support package （BSP） is described. And the transplantation process of the VxWroks operat ing system into the hardware platform mentioned above is highlighted. It is shown from the final re sults that VxWorks has high stability and real time performance, ensuring accurate flight control and a smooth flight of the micro aerial vehicle.
基金supported by the National Natural Science Foundation of China (No. 61971077)the Natural Science Foundation of Chongqing, China (No. cstc2021jcyj-msxmX0458)+3 种基金the open research fund of National Mobile Communications Research Laboratory, Southeast University (No. 2022D06)the Fundamental Research Funds for the Central Universities (No. 2020CDCGTX074)the Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu (No. BK20212001)the Natural Science Research Project of Jiangsu Higher Education Institutions (No. 21KJB510034)。
文摘Micro-UAV swarms usually generate massive data when performing tasks. These data can be harnessed with various machine learning(ML) algorithms to improve the swarm’s intelligence. To achieve this goal while protecting swarm data privacy, federated learning(FL) has been proposed as a promising enabling technology. During the model training process of FL, the UAV may face an energy scarcity issue due to the limited battery capacity. Fortunately, this issue is potential to be tackled via simultaneous wireless information and power transfer(SWIPT). However, the integration of SWIPT and FL brings new challenges to the system design that have yet to be addressed, which motivates our work. Specifically,in this paper, we consider a micro-UAV swarm network consisting of one base station(BS) and multiple UAVs, where the BS uses FL to train an ML model over the data collected by the swarm. During training, the BS broadcasts the model and energy simultaneously to the UAVs via SWIPT, and each UAV relies on its harvested and battery-stored energy to train the received model and then upload it to the BS for model aggregation. To improve the learning performance, we formulate a problem of maximizing the percentage of scheduled UAVs by jointly optimizing UAV scheduling and wireless resource allocation. The problem is a challenging mixed integer nonlinear programming problem and is NP-hard in general. By exploiting its special structure property, we develop two algorithms to achieve the optimal and suboptimal solutions, respectively. Numerical results show that the suboptimal algorithm achieves a near-optimal performance under various network setups, and significantly outperforms the existing representative baselines. considered.
