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Build a Real-Time Flight Control Algorithm for the Lighter than Air Indoor Robot Using PX4 Autopilots Support from UAV Toolbox |
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Ahmed Elsharkawy(GIST, Korea), Khawar Naheem, Mun Sang Kim(Gwangju Institute of Science and Technology, Korea) |
| We seek to show the flexibility offered by using MathWorks provided PX4 autopilot support to build customized real-time control flight and vehicle management algorithms. We show the pipeline of autopilot's algorithm design to stably fly an in-house developed lighter than air indoor robot (LAIDR). In our study, we deal with highly nonlinear problems to fly LAIDR due to the properties of the physical body. However, the “PX4 autopilot support for UAV toolbox” allows dealing with these challenges smoothly through developing the algorithm in a modular but integrated form. We evaluated LAIDR by broadcasting live clips of an indoor (theater) event. |
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Experimental Studies on Reducing Tracking Errors of Autonomous Backward Driving Control of a Vehicle |
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Hyun WOO kIM, Dong Wook Kwon(Chungnam National University, Korea), Jeong Ku Kim(Hyundai MOBIS, Korea), Seul Jung(Chungnam National University, Korea) |
| In this paper, a minimizing technique of the deviated tracking error for an autonomous backward driving of a vehicle is presented. Based on the steering sensor for the estimation of a heading angle, the autonomous backward tracking error was about 1m, which is larger for a commercial vehicle. The goal of the paper is to propose the method to minimize the deviated backward tracking error by a half under the limitation of use of local sensors. Only local sensors such as a steering sensor, rear wheel encoders, and a yaw rate sensor on a car are allowed to estimate the heading angle. To have more accurate backward tracking performance, sensed signals from those sensors are fused together. Experimental studies on a real car are conducted to confirm the better control performance. |
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Research on an Effective Jamming Technique for Infrared Guided Missiles |
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Wei Sun(Xijing University, China) |
| Reasonable and effective jamming strategy is the key to jamming infrared guided missile in the process of
using non-point source infrared decoys. The identification of seeker leader of infrared guided missile is the most important part of simulation. The anti-jamming technology of missile is also reflected in the target identification method. When the infrared imaging missile attacks after the tail, the defense jamming strategy that the target aircraft should take is obtained through theoretical analysis and simulation. In this paper, infrared imaging guided missile is taken as an example. |
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Mathematical Modeling and Simulation of a Two-stage Reciprocating Air Compressor Considering Heat Transfer Effect |
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Sung-Sub Kim, Myeong-Joon Kim, Joon-Hyun Lee, Jin-Seok Lee(Konkuk University, Korea), Hyun-Jik Cho(Hyundai-Rotem Co., Korea), Chul-Goo Kang(Konkuk University, Korea) |
| To obtain normal and abnormal data of the air compressor of a railway vehicle for fault prognostics, we propose a mathematical model of a two-stage reciprocating air compressor including kinematic equations of crank mechanism, thermodynamic equations of cylinders, valve flow equations, and heat transfer equations of cylinder blocks. Simulation results show that the mathematical model is valid and heat transfer effect at cylinder blocks is minimal to cylinder pressure but affects cylinder temperature significantly. |
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Comparison Studies of Two Major Force Control Algorithms for a Single Axis Force Control of a Robot Manipulator |
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Do-jin Jeong, Seul Jung(Chungnam National University, Korea) |
| This paper presents the comparison studies of two major force control algorithms such as hybrid force control and impedance force control for a single axis force control application. Hybrid force control regulates the desired force by minimizing a force error directly, while impedance force control regulates the contact force indirectly. The performances of two force control methods are tested and compared empirically for two different environments with soft and hard stiffness. The characteristics of two force control algorithms are experimentally compared. |
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Describing Environmental Information in Videos Using Machine Learning |
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Yoon-Jin Jeong, Soe-Sandi Htun, Ji-Hyeong Han(Seoul National University of Science and Technology, Korea) |
| The previous researches of video captioning task have focused on human actions or objects in videos, however, environmental information such as place, time, weather among others is also important information to understand videos. Therefore, in this paper, we create a new dataset which adds environmental information labels to MSVD dataset and train the machine learning model to analyze environmental information from videos. We apply R(2+1)D which is a 3D CNN model to extract video features and S2VT which is a RNN model to encode the video features and to decode the environmental information. |
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