| Summary: | Deep space exploration is a significant area of space missions in the modern world,and future large-scale deep space exploration will be greatly impacted by autonomous deep space exploration technologies.The autonomous technology of deep space exploration faces severe challenges because of the complicated and uncharted deep space environment,lengthy deep space communication time,and constrained on-board computing capacity.To address this issue,a cloud-edge-end cooperation architecture for deep space exploration tasks using digital twins is developed,which can offer more efficient resource services for deep space exploration autonomous technologies.Firstly,the complex deep space exploration task is decomposed into multiple sub-modules with dependencies.Secondly,the orbiter coverage time model,the collaborative computing model,and the task dependency model are established in the virtual space layer.Finally,based on the aforementioned models,the corresponding target optimization pro-blem is proposed.The optimization objective is to minimize the energy consumption and time of the landing rover for completing the deep space exploration mission under the constraints of module dependence,the effective communication service time of the orbiter and the transmit power control of the landing rover.In order to solve this optimization problem,an adaptive genetic algorithm is proposed,so that the optimal execution strategy for the landing rover in the physical space layer can be determined.Si-mulation results show that the proposed adaptive genetic algorithm can effectively reduce the mission completion time and energy consumption.Additionally,the proposed cloud-edge-end cooperation computing model is contrasted with the other three computing models,and the results reveal that,when it is used to achieve the same objective,the proposed cloud-edge-end cooperation framework has a greater resource utilization.
|
|---|