The trajectory optimization challenge, especially with multiple no-fly zones (NFZs), often leads to many local optima. Using mixed-integer programming (MIP) improves chances for finding global optima but at the cost of increased computation due to many binary variables. To speed up solving, this study uses variable substitution, which changes NFZ constraints into simpler upper and lower bounds and uses binary variables for path decision, matching the number of NFZs. Inspired by sequential convex programming (SCP), the issue with a mix of strategies that make the problem easier to solve is tackled, leading to the creation of the hybrid iterative convex programming (HICP) algorithm. Testing in different situations verify the effectiveness of the HICP algorithm. The HICP algorithm demonstrates robust adaptability and significant computational efficiency, capable of producing trajectories that navigate around the general NFZ in under 5 seconds, underscoring its substantial practical value.
With the continuous development of artificial intelligence, the use of deep learning to achieve intelligent space object detection has become a new research trend. Space-based observation platforms are affected by the space environment with many problems such as small scale of space object, large amount of noise, low recognition and little extractable information. To address the above issues, an improved fully convolutional one-stage object detection (FCOS) model based on adaptive feature texture enhancement and receptive field adjustment is proposed. To address the problem of pixel smoothing and detail loss caused by up sampling in convolutional neural networks (CNN), this paper proposes a texture detail enhancement module (TDEM), which is based on sub-pixel convolution to achieve effective scaling of the feature map by automatically learning the interpolation function and enhance the correlation between the pixels of the image while suppressing irrelevant features. In addition, in order to obtain more dense features and appropriate receptive fields, an adaptive receptive field adjustment module (ARFAM) is proposed by using densely connected dilated convolution and attention mechanism to enrich the contextual information around the object and improve the detection capability of the model. This paper constructs the SDM dataset, which contains 6842 images and three categories of satellites, debris, and meteorites. The experimental results on the SDM dataset show that our method achieves the mAP of 73.9%, which illustrates detection performance is significantly better than the mainstream algorithms.
To reduce costs and improve payload capacity, modern solid launch vehicles usually use the depleted shutdown solid motors. Because of the incapacity to control thrust, fuel burning rate and shutdown time, the energy management guidance method is the key technology for this kind of launch vehicles to complete high precision orbit injection and overcome various dispersions in the meantime. To solve this problem, a guidance method with iteration is proposed for launch vehicles with depleted shutdown solid motors by energy management attitude in yaw channel, the method is able to assess the vehicle's orbit injection capability in real-time before the injection stage ignition and implement direct injection. Numerical simulation result showed its adequately high accuracy of orbit injection and considerable value for the depleted shutdown solid launch vehicles.
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