Browsing by Author "Kim, Seungkeun"
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Item Open Access Airborne behaviour monitoring using Gaussian processes with map information(Institution of Engineering and Technology, 2013-07-31T00:00:00Z) Oh, Hyondong; Shin, Hyo-Sang; Kim, Seungkeun; Tsourdos, Antonios; White, Brian A.This paper proposes an airborne behaviour monitoring methodology of ground vehicles based on a statistical learning approach with domain knowledge given by road map information. To monitor and track the moving ground target using UAVs aboard a moving target indicator, an interactive multiple model (IMM) filter is firstly applied. {\color{red}The IMM filter consists of an on-road moving mode using a road-constrained filter and an off-road moving mode using a conventional filter.} Mode probability is also calculated from the IMM filter, and it provides deviation of the vehicle from the road. Then, a novel hybrid algorithm for anomalous behaviour recognition is developed using a Gaussian process regression on velocity profile along the one-dimensionalised position of the vehicle, as well as the deviation of the vehicle. To verify the feasibility and benefits of the proposed approach, a numerical simulation is performed using realistic car trajectory data in a city traffic.Item Open Access Collision avoidance strategies for unmanned aerial vehicles in formation flight(IEEE, 2017-06-13) Seo, Joongbo; Kim, Youdan; Kim, Seungkeun; Tsourdos, AntoniosCollision avoidance strategies for multiple UAVs (Unmanned Aerial Vehicles) based on geometry are investigated in this study. The proposed strategies allow a group of UAVs to avoid obstacles and separate if necessary through a simple algorithm with low computation by expanding the collision-cone approach to formation of UAVs. The geometric approach uses line-of-sight vectors and relative velocity vectors where dynamic constraints are included in the formation. Each UAV can determine which plane and direction are available for collision avoidance. An analysis is performed to define an envelope for collision avoidance, where angular rate limits and obstacle detection range limits are considered. Based on the collision avoidance envelope, each UAV in a formation determines whether the formation can be maintained or not while avoiding obstacles. Numerical simulations are performed to demonstrate the performance of the proposed strategies.Item Open Access Communication-aware trajectory planning for unmanned aerial vehicles in urban environments(AIAA, 2018-07-16) Oh, Hyondong; Shin, Hyo-Sang; Kim, Seungkeun; Chen, Wen-HuaIntroduction: Maintaining communication among mobile agents in a networked team is challenging due to limited bandwidth, maximum communication range, transmission power, and physical obscuration or occlusion in the mission environment. With the advent of lightweight, robust, and autonomous platforms as well as wireless networking technologies, it becomes feasible to use small unmanned aerial vehicles (UAVs) as communication relay nodes under limited satellite communication environments [1]. This communication relay UAV could allow a ground operator/system to have a sufficient data link to effectively see beyond the communication range and over the horizon/buildings where traditional methods fail. The relay UAV can also be used to transmit/share critical information efficiently from an operator to an end user or between vehicles.Item Open Access Data-driven diagnosis of multicopter thrust fault using supervised learning with inertial sensors(AIAA, 2023-09-25) Kim, Taegyun; Kim, Seungkeun; Shin, Hyo-SangThis study proposes a data-driven fault diagnosis for multicopter unmanned aerial vehicles that uses the principal direction vector of inertial measurement unit (IMU) sensor signals calculated by principal component analysis. The main idea comes from the fact that a normal sphere-shaped distribution of the sensor data changes to a specific elliptical shape under a certain thrust fault situation. The fault diagnosis is based on classification and regression using supervised learning with the gyroscope and accelerometer datasets of an IMU. We analyze the performance of the proposed approach by depending on different learning algorithms. To verify the diagnostic performance, ground experiments with a hexacopter on the gimbaled jig are performed for various cases of damaged propellers. Then, the applicability of the proposed data-driven fault diagnosis is confirmed by analyzing the accuracy of the fault’s location and degree.Item Open Access Design and flight testing of the ducted-fan UAV flight array system(Springer, 2023-02-24) Kim, Taegyun; Jeaong, Hoijo; Kim, Seongyoung; Kim, Inrae; Kim, Seungkeun; Suk, Jinyoung; Shin, Hyo-SangThis study proposes a ducted-fan flight array (DFA) system that can change the array based on the mission environment and validate the feasibility through ground and flight tests. This DFA can carry out normal formation flight as separated UAV members and can also cooperate as a single entity by physical connection. The ducted-fan unmanned aerial vehicle (UAV) is manufactured in-house and equipped with a connected surface and an assembly device on the side to perform connection and separation tasks. Moreover, the control system was designed using an open-source autopilot environment, and the communication environment for multi-UAV flight was constructed using the Robot Operating System (ROS). Then, ground and preliminary experimental tests verified the feasibility and performance of the DFA system for connected and separated flight.Item Open Access An integrated decision-making framework of a heterogeneous aerial robotic swarm for cooperative tasks with minimum requirements(SAGE, 2018-05-15) Jang, Inmo; Shin, Hyo-Sang; Tsourdos, Antonios; Jeong, Junho; Kim, Seungkeun; Suk, JinoungGiven a cooperative mission consisting of multiple tasks spatially distributed, an aerial robotic swarm’s decision-making issues include team formation, team-to-task assignment, agent-to-work-position assignment and trajectory optimisation with collision avoidance. The problem becomes even more complicated when involving heterogeneous agents, tasks’ minimum requirements and fair allocation. This paper formulates all the combined issues as an optimisation problem and then proposes an integrated framework that addresses the problem in a decentralised fashion. We approximate and decouple the complex original problem into three subproblems (i.e. coalition formation, position allocation and path planning), which are sequentially addressed by three different proposed modules. The coalition formation module based on game theories deals with a max-min problem, the objective of which is to partition the agents into disjoint task-specific teams in a way that balances the agents’ work resources in proportion to the task’s minimum workload requirements. For agents assigned to the same task, given reasonable assumptions, the position allocation subproblem can be efficiently addressed in terms of computational complexity. For the trajectory optimisation, we utilise a Model Predictive Control and Sequential Convex Programming algorithm, which reduces the size of the problem so that the agents can generate collision-free trajectories on a real-time basis. As a proof of concept, we implement the framework into an unmanned aerial vehicle swarm’s cooperative stand-in jamming mission scenario and show its feasibility, fault tolerance and near-optimality based on numerical experiment.Item Open Access Road-map-assisted standoff tracking of moving ground vehicle using nonlinear model predictive control(IEEE, 2015-04-30) Oh, Hyondong; Kim, Seungkeun; Tsourdos, AntoniosThis paper presents road-map-assisted standoff tracking of a ground vehicle using nonlinear model predictive control. In model predictive control, since the prediction of target movement plays an important role in tracking performance, this paper focuses on utilizing road-map information to enhance the estimation accuracy. For this, a practical road approximation algorithm is first proposed using constant curvature segments, and then nonlinear road-constrained Kalman filtering is followed. To address nonlinearity from road constraints and provide good estimation performance, both an extended Kalman filter and unscented Kalman filter are implemented along with the state-vector fusion technique for cooperative unmanned aerial vehicles. Lastly, nonlinear model predictive control standoff tracking guidance is given. To verify the feasibility and benefits of the proposed approach, numerical simulations are performed using realistic car trajectory data in city traffic.