澤井 秀次郎

In the modern space development, small-scale deep space mission should be realized to promote frequent and challenging deep space mission. Therefore, the efficient and quick design method to construct Earth escape trajectory with high flexibility in the boundary condition such as escape velocity, direction and timing is strongly demanded. In this paper, the families of Moon-to-Moon transfers with sequential lunar swing-by on a hyperbolic orbit are computed and stored in a database. These families are useful to enhance the Earth escape energy and to change escape direction which could lead a spacecraft to further destinations.

In the modern space development, small-scale deep space mission should be realized to promote frequent and challenging deep space mission. Therefore, the efficient and quick design method to construct Earth escape trajectory with high flexibility in the boundary condition such as escape velocity, direction and timing is strongly demanded. In this paper, the families of Moon-to-Moon transfers with sequential lunar swing-by on a hyperbolic orbit are computed and stored in a database. These families are useful to enhance the Earth escape energy and to change escape direction which could lead a spacecraft to further destinations.

<p>In this study, a crater detection method for a moon-landing system with low computational resources is proposed. The proposed method is applied to the Smart Lander for Investigating Moon (SLIM), which aims for a pin-point landing on the moon. According to this plan, surface images of the moon will be captured by a camera mounted on the space probe, and the craters are to be detected from the images. Based on the positional relationship between detected craters, the method estimates the exact flight position of the space probe. Because the computational resources of SLIM are limited, rapid and accurate crater detection must be performed using fixed-point arithmetic on a field-programmable gate array (FPGA). This study proposes a crater detection method that uses principal component analysis (PCA). The computational processing for crater detection by PCA is performed by product-sum operations, which are suitable for fixed-point arithmetic. Moreover, this method is capable of parallel processing; hence high-speed processing is expected. This study not only introduces a crater detection method using PCA but also evaluates the properties of this method.</p>