Youeyun Jung

Mars precision landing guidance based on model predictive control approach

A precision landing guidance design for the Mars powered descent phase is proposed based on model predictive control (MPC) approach. Dynamics model used for the formulation are convexificated and linearized to adopt the convex optimization technique, which has been suggested by researchers of Jet Propulsion Laboratory. To employ the receding horizon frame and reduce the number of control inputs, the convex optimization problem is augmented with Laguerre functions. To represent the minimum fuel consumption or minimum landing error precisely unlike the optimal control theory, new cost function is designed by combining them with weighting factors. Moreover, the stability of the proposed guidance design for the independent control inputs calculated from each time step is verified by using Lyapunov stability analysis. Finally, numerical simulations are conducted to examine the suggested guidance formulation and to compare the performance with an optimal solution.

Robust marker tracking algorithm for precise UAV vision-based autonomous landing

The robust marker tracking and relative navigation algorithms are presented for precise UAV vision-based autonomous landing. To recognize the marker in close-range, the concentric circles are adopted as the marker with ellipse fitting algorithm based on Direct Least Square. We assume that IMU provides vehicles attitude and altitude so that we consider GPS-denied situation. Also multiple ellipses are used to estimate its center pixel coordinate makes UAV land more accurately. To verify the vision-based relative navigation algorithm we suggest, numerical simulations are obtained by using virtual reality toolbox in MATLAB™. We predetermine the true position and attitude of UAV, and the result of the relative position calculated from vision software including the filter is compared. The simulation results show that the algorithm is robust and very accurate.

Development and Verification for Flight Model of CubeSat LINK

Little Intelligent Nanosatellite of KAIST(LINK) is a 2U-size CubeSat which is developed by Aerospace Systems & Control Lab.(ASCL) of KAIST as a part of the international cooperation project QB50. The objective of the QB50 project is to carry out atmospheric research within the lower thermosphere and ionosphere and CubeSats are planned to be deployed at the International Space Station(ISS) from the first quarter of 2017. To implement this objective, a flight model(FM) of LINK has been successfully developed and the design and performance of the satellite have been verified by performing environment and function tests in accordance with acceptance requirement level. This paper describes the development of flight model and the results of vibration and thermal vacuum tests.Little Intelligent Nanosatellite of KAIST(LINK) is a 2U-size CubeSat which is developed by Aerospace Systems & Control Lab.(ASCL) of KAIST as a part of the international cooperation project QB50. The objective of the QB50 project is to carry out atmospheric research within the lower thermosphere and ionosphere and CubeSats are planned to be deployed at the International Space Station(ISS) from the first quarter of 2017. To implement this objective, a flight model(FM) of LINK has been successfully developed and the design and performance of the satellite have been verified by performing environment and function tests in accordance with acceptance requirement level. This paper describes the development of flight model and the results of vibration and thermal vacuum tests.

Terrain relative navigation for precise lunar landing using crater matching algorithm

Under GPS-denied conditions, an inertial navigation diverges easily due to integration drift in the measurement of acceleration and angular velocity. Therefore, a terrain relative navigation (TRN) is applied to correct the drift error for precise lunar landing mission. Diverse sensors are used for the TRN in these days, such as a Light and Detection and Ranging (LIDAR), laser altimeter, and optical camera. TRN algorithms can improve navigation performance by combining inertial navigation data and terrain measurement data from those sensors. In this paper, a simulation of TRN with an optical camera, IMU by using crater matching algorithm is performed. The craters made by colliding with other solid body can remain longer on the lunar surface without being eroded, because there is no air and liquid water in the Moon. Therefore, it can be utilized as appropriate features for TRN even in lost in space.