顾及动态物体感知的增强型视觉SLAM系统

传统的同步定位与制图（Simultaneous localization and mapping, SLAM）系统在复杂环境下工作时，无法分辨环境中的物体是否存在运动状态，图像中运动的物体可能导致特征关联错误，引起定位的不准确和地图构建的偏差。为了提高SLAM系统在动态环境下的鲁棒性和可靠性，本文提出了一种顾及动态物体感知的增强型视觉SLAM系统。首先，使用深度学习网络对每一帧图像的动态物体进行初始检测，然后使用多视图几何方法更加精细地判断目标检测无法确定的动态物体区域。通过剔除属于动态物体上的特征跟踪点，提高系统的鲁棒性。本文方法在公共数据集TUM和KITTI上进行了测试，结果表明在动态场景中定位结果的准确度有了明显提升，尤其在高动态序列中相对于原始算法的精度提升在92%以上。与其他顾及动态场景的SLAM系统相比，本文方法在保持精度优势的同时，提高了运行结果的稳定性和时间效率。     

Downwash modelling for three-lifting-surface aircraft configuration design

This paper introduces a semi-empirical model to predict the downwash gradient at the horizontal tail of a three-lifting-surface aircraft. The superposition principle applied to well established formulations valid for two lifting surfaces is not a reasonable approach to calculate the downwash of a canard-wing-tail layout, and this paper demonstrates that such a basic technique leads to incorrect results. Therefore, an ad hoc prediction model is proposed that considers the combined nonlinear effec...     

Optimization and verification of single pilot operations model for commercial aircraft based on biclustering method

Commercial aircraft crews have experienced a trend from five-person crew to dual-pilot crew. Arised from both technological and market demands, Single Pilot Operations (SPO) is considered an important development direction in modern aviation technology. In this paper, starting from Dual-Pilot Operations (DPO), the piloting process, decision-making process and decision-making mode of DPO for commercial aircraft are studied to obtain the operational requirements of SPO. Then, based on above analysis, the operational mechanism of SPO is studied and the core technology of SPO mode is proposed. Next, a new closed frequent bicluster mining algorithm named FsCluster is proposed for the optimization of the SPO model, and the other efficient bicluster mining algorithm named TsCluster is proposed for the analysis and verification of the SPO model. Finally, a typical flight phase scenario is modelled by Magic System of System, and combined with the proposed algorithms for analysis and verification to determine whether the SPO mode can meet the DPO requirements.     

A Robotic TeleoperationSystem for Precise Robotic Manipulation by Human-Machine Interaction

Teleoperation is of great importance in the area of robotics， especially when people are unavailable in the robot workshop. It provides a way for people to control robots remotely using human intelligence. In this paper， a robotic teleoperation system for precise robotic manipulation is established. The data glove and the 7-degrees of freedom (DOFs) force feedback controller are used for the remote control interaction. The control system and the monitor system are designed for the remote precise manipulation. The monitor system contains an image acquisition system and a human-machine interaction module， and aims to simulate and detect the robot running state. Besides，a visual object tracking algorithm is developed to estimate the states of the dynamic system from noisy observations. The established robotic teleoperation systemis applied to a series of experiments， and high-precision results are obtained， showing the effectiveness of the physical system.     

Orbital error propagation considering atmospheric density uncertainty

Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty).     

多空间机器人协同工作空间的数值求解方法

针对未来在轨服务过程中，可能遇到单个空间机器人难以独立胜任的应用场景，需要多个空间机器人协同操作，但目前针对多空间机器人协同工作空间的研究很少。考虑到解析法计算工作空间难度很大，提出了一种基于蒙特卡洛法的一般协同工作空间数值求解方法。再进一步针对空间机器人协同的特殊性，提出了广义协同工作空间的概念，并给出了数值求解方法。仿真结果表明，基座姿态约束会引起协同工作空间的缩小，同时广义协同工作空间的范围明显大于一般协同工作空间。所得结论可以为空间机器人任务设计与规划提供参考。