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Cheongju, South Korea

Yang K.,Korea Air Force Academy
2013 International Conference on Unmanned Aircraft Systems, ICUAS 2013 - Conference Proceedings | Year: 2013

Planning in a cluttered environment under differential constraints is a difficult problem because the planner must satisfy the external constraints that arise from obstacles in the environment and the internal constraints due to the kinematic/dynamic limitations of the robot. This paper proposes a novel Spline-based Rapidly-exploring Random Tree (SRRT) algorithm which treats both the external and internal constraints simultaneously and efficiently. The proposed algorithm removes the need to discretize the action space as is common with conventional RRT, thus improving path quality. In addition, computationally expensive numerical integration of the system dynamics is replaced by an efficient spline curve parameterization. Finally, the SRRT guarantees continuity of curvature along the path satisfying any upper-bounded curvature constraints. This paper presents the underlying theory to the SRRT algorithm and presents simulation results of a mobile robot efficiently navigating through cluttered environments. © 2013 IEEE. Source


Choi J.,Korea Air Force Academy
Modern Physics Letters A | Year: 2014

In the framework of Lorentzian multiply warped products we study the magnetically charged Gibbons-Maeda-Garfinkle-Horowitz-Strominger (GMGHS) interior spacetime in the string frame. We also investigate geodesic motion in various hypersurfaces, and compare their solutions of geodesic equations with the ones obtained in the Einstein frame. © World Scientific Publishing Company. Source


Moon S.,Korea Air Force Academy | Oh E.,Korea Aerospace Research Institute | Shim D.H.,KAIST
Journal of Intelligent and Robotic Systems: Theory and Applications | Year: 2013

In this paper, a hierarchical framework for task assignment and path planning of multiple unmanned aerial vehicles (UAVs) in a dynamic environment is presented. For multi-agent scenarios in dynamic environments, a candidate algorithm should be able to replan for a new path to perform the updated tasks without any collision with obstacles or other agents during the mission. In this paper, we propose an intersection-based algorithm for path generation and a negotiation-based algorithm for task assignment since these algorithms are able to generate admissible paths at a smaller computing cost. The path planning algorithm is also augmented with a potential field-based trajectory replanner, which solves for a detouring trajectory around other agents or pop-up obstacles. For validation, test scenarios for multiple UAVs to perform cooperative missions in dynamic environments are considered. The proposed algorithms are implemented on a fixed-wing UAVs testbed in outdoor environment and showed satisfactory performance to accomplish the mission in the presence of static and pop-up obstacles and other agents. © 2012 Springer Science+Business Media B.V. Source


Yang K.,Korea Air Force Academy | Kang Y.,Kookmin University | Sukkarieh S.,University of Sydney
International Journal of Control, Automation and Systems | Year: 2013

This paper presents an adaptive Nonlinear Model Predictive Control (NMPC) for the path-following control of a fixed-wing unmanned aerial vehicle (UAV). The objective is to minimize the mean and maximum errors between the reference path and the UAV. Navigating in a cluttered environment requires accurate tracking. However, linear controllers cannot provide good tracking performance due to nonlinearities that arise in the system dynamics and physical limitations such as actuator saturation and state constraints. NMPC provides an alternative since it can combine multiple objectives and constraints, which minimize the objective function. However, it is difficult to decide appropriate control horizon since the path-following performance depends on the profile of the path. Therefore, a fixed-horizon NMPC cannot guarantee accurate tracking performance. An adaptive NMPC that varies the control horizon according to the path curvature profile for tight tracking is proposed in this paper. Simulation results show that the proposed adaptive NMPC controller can follow the path more accurately than a conventional, fixed-horizon NMPC. © 2013 Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg. Source


Yang K.,Korea Air Force Academy
International Journal of Control, Automation and Systems | Year: 2011

A new synchronized biased-greedy RRT is proposed which leverages the strengths of the biased and greedy RRTs. It combines the advantage of the biased RRT that grows trees towards the goal location, with the ability of the greedy RRT that makes trees traverse the environment in a single iteration. The proposed method achieves performance improvements compared to other RRT variants, not only in computational time but also in the quality of the path. Two enhancements are made to the initial path to relax the sub-optimality of the RRT path; first a path pruning algorithm is executed to eliminate redundant nodes and an anytime strategy is adapted to continuously enhance the quality of the path within the deliberation time. © 2011 ICROS, KIEE and Springer. Source

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