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).