Browsing by Author "Oh, Hyondong"
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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 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 Distributed estimation of stochastic multiagent systems for cooperative control with a virtual network(IEEE, 2022-10-19) Song, Yeongho; Lee, Hojin; Kwon, Cheolhyeon; Shin, Hyo-Sang; Oh, HyondongThis article proposes a distributed estimation algorithm that uses local information about the neighbors through sensing or communication to design an estimation-based cooperative control of the stochastic multiagent system (MAS). The proposed distributed estimation algorithm solely relies on local sensing information rather than exchanging estimated state information from other agents, as is commonly required in conventional distributed estimation methods, reducing communication overhead. Furthermore, the proposed method allows interactions between all agents, including non-neighboring agents, by establishing a virtual fully connected network with the MAS state information independently estimated by each agent. The stability of the proposed distributed estimation algorithm is theoretically verified. Numerical simulations demonstrate the enhanced performance of the estimation-based linear and nonlinear control. In particular, using the virtual fully connected network concept in the MAS with the sensing/communication range, the flock configuration can be tightly controlled within the desired boundary, which cannot be achieved through the conventional flocking methods.Item Open Access Receding horizon-based infotaxis with random sampling for source search and estimation in complex environments(IEEE, 2022-06-21) Park, Minkyu; Ladosz, Pawel; Kim, Jongyun; Oh, HyondongThis paper proposes a receding horizon-based information-theoretic source search and estimation strategy for a mobile sensor in an urban environment in which an invisible harmful substance is released into the atmosphere. The mobile sensor estimates the source term including its location and release rate by using sensor observations based on Bayesian inference. The sampling-based sequential Monte Carlo method, particle filter, is employed to estimate the source term state in a highly nonlinear and stochastic system. Infotaxis, the information-theoretic gradient-free search strategy is modified to find the optimal search path that maximizes the reduction of the entropy of the source term distribution. In particular, receding horizon Infotaxis is introduced to avoid falling into the local optima and to find more successful information gathering paths in obstacle-rich urban environments. Besides, a random sampling method is introduced to reduce the computational load of the receding horizon Infotaxis for real-time computation. The random sampling method samples the predicted future measurements based on current estimation of the source term and computes the optimal search path using sampled measurements rather than considering all possible future measurements. To demonstrate the benefit of the proposed approach, comprehensive numerical simulations are performed for various conditions. The proposed algorithm increases the success rate by about 30% and reduces the mean search time by about 40% compared with the existing information-theoretic search strategy.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.Item Open Access Towards monocular vision-based autonomous flight through deep reinforcement learning(Elsevier, 2022-03-09) Kim, Minwoo; Kim, Jongyun; Jung, Minjae; Oh, HyondongThis paper proposes an obstacle avoidance strategy for small multi-rotor drones with a monocular camera using deep reinforcement learning. The proposed method is composed of two steps: depth estimation and navigation decision making. For the depth estimation step, a pre-trained depth estimation algorithm based on the convolutional neural network is used. On the navigation decision making step, a dueling double deep Q-network is employed with a well-designed reward function. The network is trained using the robot operating system and Gazebo simulation environment. To validate the performance and robustness of the proposed approach, simulations and real experiments have been carried out using a Parrot Bebop2 drone in various complex indoor environments. We demonstrate that the proposed algorithm successfully travels along the narrow corridors with the texture free walls, people, and boxes.Item Open Access Using lazy agents to improve the flocking efficiency of multiple UAVs(Springer, 2021-10-27) Song, Yeongho; Gu, Myeonggeun; Choi, Joonwon; Oh, Hyondong; Lim, Seunghan; Shin, Hyo-Sang; Tsourdos, AntoniosA group of agents can form a flock using the augmented Cucker-Smale (C-S) model. The model autonomously aligns them to a common velocity and maintains a relative distance among the agents in a distributed manner by sharing the information among neighbors. This paper introduces the concept of inactiveness to the augmented C-S model for improving the flocking performance. It involves controlling the energy and convergence time required to form a stable flock. Inspired by the natural world where a few lazy (or inactive) workers are helpful to the group performance in social insect colonies. In this study, we analyzed different levels of inactiveness as a degree of control input effectiveness for multiple fixed-wing UAVs in the flocking algorithm. To find the appropriate inactiveness level for each flock member, the particle swarm optimization-based approach is used as the first step, based on the initial condition of the flock. However, as the significant computational burden may cause difficulties in implementing the optimization-based approach in real time, we also propose a heuristic adaptive inactiveness approach, which changes the inactivity level of selected agents adaptively according to their position and heading relative to the flock center. The performance of the proposed approaches using the concept of lazy (or inactive) agents is verified with numerical simulations by comparing them with the conventional flocking algorithm in various scenarios.