• Journal of KIISE:Computing Practices and Letters
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• v.9 no.6
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• pp.674-682
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• 2003

Ship autonomous navigation is designated as what computerizes mental faculties possessed of navigation experts, which are building navigation plans, grasping the situation, forecasting the fluctuation, and coping with the situation. An autonomous navigation system, which consists of several subsystems such as navigation system, a collision avoidance system, several data fusion systems, and a motion control system, is based on an intelligent control architecture for the sake of integrating the systems. The motion control system, which is one of the most essential system in autonomous navigation system, controls its propulsion and steering gears to move the ship satisfying its hydrodynamic characteristics. This paper is the study on the ship movement control system and its implementation which are totally developed and run on virtual-world system. Receiving the high-level control values such as a waypoint presented from the collision avoidance system, the motion control system generates them to low-level control values for propulsion and steering devices. In the paper, we develop a ship motion controller using Oldenburger's theory based on mathematical fundamentals, and simulate it with various scenarios in order to verify its performance.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.4
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• pp.305-316
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• 2014

As avionics and mechanical devices have been developed, the size of unmanned aerial vehicle (UAV) is getting smaller. However, the complicated and accurate missions are provided to the UAV. Among various types of UAVs, quadrotors are widely used for their availability by virtue of simple structure and hovering function. However, the control of quadrotor is highly constrained, because the quadrotor is an under-actuated system which has only 4 actuator inputs. To deal with this under-actuated problem, a new quadrotor model with two more actuators in addition to the 4 propeller inputs is provided to make the system fully-actuated. For the proposed model, a controller is designed using feedback linearization methods. To validate the model and to verify the performance of the proposed controller, numerical simulation is performed.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.164-172
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• 2019

In this paper, we implement a wheeled mobile robot that accurately and autonomously finds the optimal route from the starting point to the destination point based on computer vision in a complex indoor environment. We get a number of waypoints from the starting point to get the best route to the target through deep reinforcement learning. However, in the case of autonomous driving, the majority of cases do not reach their destination accurately due to external factors such as surface curvature and foreign objects. Therefore, we propose an algorithm to deepen the waypoints and destinations included in the planned route and then correct the route through the waypoint recognition while driving to reach the planned destination. We built an autonomous wheeled mobile robot controlled by Arduino and equipped with Raspberry Pi and Pycamera and tested the planned route in the indoor environment using the proposed algorithm through real-time linkage with the server in the OSX environment.

• Aerospace Engineering and Technology
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• v.5 no.2
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• pp.1-7
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• 2006

This paper presents a new point navigation guidance law which is useful for unmanned helicopters. Predicting the future position, the guidance law generates velocity and heading commands, which are used as input to autopilot. This method differs from conventional guidance law in that it reorients the direction of flight velocity vector directly, not by bank angle indirectly. For flight tests, we have developed a flight control system for a R/C helicopters. The system consists of a flight control computer, navigation sensors, and a ground station The results of the test show that the proposed law guides a unmanned helicopter along a line path within a given area. In the future, we are planning to extend the guidance law to the mission of path following. i.e., waypoint navigation.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.170-178
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• 2017

This paper proposes an improved path-planning technique based on the $A^*$ algorithm. The conventional $A^*$ algorithm only considers the optimality of the planned path and sometimes produces a path that an unmanned underwater vehicle (UUV) cannot navigate due to its dynamic constraint such as the limit of the radius of gyration. It is because that the previous method evaluate the moving cost based on the straight distance between nodes. We enhance the conventional method by evaluating the moving cost on the basis of the practically navigable trajectory, which is generated by the waypoint-tracking control of the UUV dynamics. The simulation examples indeed show the effectiveness of the proposed technique.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.3
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• pp.293-300
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• 2009

The rotorcraft-based unmanned aerial vehicle(UAV) capable of performing close-range surveillance and reconnaissance has been developed. Trade studies on mission feasibility led to the adoption of a coaxial rotorcraft with twin rotors counter-rotating in one axis and driven by electric motors. A commercial off-the-shelf flight control computer(FCC) and a radio frequency modem were adopted for autonomous navigation. In order to achieve an aerial view, commercial charge-coupled device camera was also integrated into the vehicle. The performance of the completed vehicle was proved with manual flight test, and mission capability was verified through waypoint navigation flight after being equipped with FCC. This paper treats the whole process of design and system integration for development of the coaxial rotorcraft UAV.

• Tunnel and Underground Space
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• v.32 no.3
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• pp.231-242
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• 2022

In this study, we developed a robot operating system (ROS)-based autonomous driving robot that estimates the robot's position in underground mines and drives and returns through multiple waypoints. Autonomous driving robots utilize SLAM (Simultaneous Localization And Mapping) technology to generate global maps of driving routes in advance. Thereafter, the shape of the wall measured through the LiDAR sensor and the global map are matched, and the data are fused through the AMCL (Adaptive Monte Carlo Localization) technique to correct the robot's position. In addition, it recognizes and avoids obstacles ahead through the LiDAR sensor. Using the developed autonomous driving robot, experiments were conducted on indoor experimental sites that simulated the underground mine site. As a result, it was confirmed that the autonomous driving robot sequentially drives through the multiple waypoints, avoids obstacles, and returns stably.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.198-208
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• 2022

In this paper, an autonomous driving system is implemented for the Segye AI Robot Race Competition that multiple vehicles drive simultaneously. By utilizing the ERP42-racing platform, RTK-GPS, and LiDAR sensors provided in the competition, we propose an autonomous driving system that can drive safely and quickly in a road environment with multiple vehicles. This system consists of a recognition, judgement, and control parts. In the recognition stage, vehicle localization and obstacle detection through waypoint-based LiDAR ROI were performed. In the judgement stage, target velocity setting and obstacle avoidance judgement are determined in consideration of the straight/curved section and the distance between the vehicle and the neighboring vehicle. In the control stage, adaptive cruise longitudinal velocity control based on safe distance and lateral velocity control based on pure-pursuit are performed. To overcome the limited experimental environment, simulation and partial actual experiments were conducted together to develop and verify the proposed algorithms. After that, we participated in the Segye AI Robot Race Competition and performed autonomous driving racing with verified algorithms.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.1-9
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• 2023

This paper presents an optimal path planning strategy for aerial searching and surveying of a user-designated area using multiple Unmanned Aerial Vehicles (UAVs). The method is designed to deal with a single unseparated polygonal area, regardless of polygonal convexity. By defining the search area into a set of grids, the algorithm enables UAVs to completely search without leaving unsearched space. The presented strategy consists of two main algorithmic steps: cellular decomposition and path planning stages. The cellular decomposition method divides the area to designate a conflict-free subsearch-space to an individual UAV, while accounting the assigned flight velocity, take-off and landing positions. Then, the path planning strategy forms paths based on every point located in end of each grid row. The first waypoint is chosen as the closest point from the vehicle-starting position, and it recursively updates the nearest endpoint set to generate the shortest path. The path planning policy produces four path candidates by alternating the starting point (left or right edge), and the travel direction (vertical or horizontal). The optimal-selection policy is enforced to maximize the search efficiency, which is time dependent; the policy imposes the total path-length and turning number criteria per candidate. The results demonstrate that the proposed cellular decomposition method improves the search-time efficiency. In addition, the candidate selection enhances the algorithmic efficacy toward further mission time-duration reduction. The method shows robustness against both convex and non-convex shaped search area.

• The Journal of Korean Medical History
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• v.34 no.1
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• pp.31-46
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• 2021

Shin Hong-Gyun and his family have been in medicine for many generations as Korean Medical Doctors (KMDs). In 1919 when Shin Hong-Gyun was participating in an independence movement in Jang-Baek-Hyun, his younger brother Shin Dong-Gyun was killed by Japanese military police forces. This tragic incident triggered Shin Hong-Gyun to establish an army for national independence called , holding 200 young men, to serve in armed struggle against the Japanese Government with Kim Jung-Geon in May, 1920. In March 1933, Shin Hong-Gyun, as a military surgeon, led his men to the Korean Independence Army to fight a battle. Once he became a member of the Korean Independence Army, he, in fact, participated in few battles: Sadohaja, Dong-Kyung-Sung, Deajeonjayeong. Daejeonjayeong was a waypoint that the Japanese military needed to pass through in order to reach the Wangcheong area. Shin Hong-Gyun's independence forces had to endure painful starvation and heavy rain while hiding in ambush for long periods of time until the Japanese military would appear. Due to its summer rainy season, rainwater overflowed into their trenches and was filled up to the waist. Even worse, food stockpiles were low and the Japanese army did not appear for longer time. Shin Hong-Gyun's entire team suffered severe hunger and extreme cold. At this critical moment, Shin Hong-Gyun used his expertise as a KMD to find edible black mushrooms that grow wild in the mountains and use them to feed his men. This event led to the victory of the independence army at the battle of Daejeonjayeong. The purpose of the paper is to inform and highlight the forgotten history of Shin Hong-Gyun who was, both, a Korean Medical Doctor and a military surgeon.