Deep Learning for Planetary Rover Localization

2022财年，我们在计算科学国际会议（ICCS）上发表了题为《Physics Informed RNN-DCT Networks for Time-Dependent Partial Differential Equations》的论文。这项工作将信号处理领域的技术融入到物理学信息神经网络中，大大提高了流体模拟的准确性和速度，我们还在《空间研究进展》上发表了一篇论文“使用RQ定律进行多目标主动空间碎片清除的低推力会合轨道生成”。我们使用路径优化算法来寻找航天器清除空间碎片物体的最佳轨道。

We are continuing to apply machine learning to problems in space research/exploration and establish deep learning computational hardware capabilities at NAOJ.In FY2022, we presented a paper "Physics Informed RNN-DCT Networks for Time-Dependent Partial Differential Equations" at the International Conference on Computational Science (ICCS). This work incorporates techniques from the signal processing domain into physics-informed neural networks, greatly improving accuracy and speed in fluid simulations.We also published a paper "Low-thrust rendezvous trajectory generation for multi-target active space debris removal using the RQ-Law" to Advances in Space Research. We use path optimization algorithms to find the best orbital trajectory for a spacecraft to remove space debris objects.