Jinwook Huh

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This paper presents a new approach for fast collision detection in high dimensional configuration spaces for Rapidly-exploring Random Trees (RRT) motion planning. The proposed method is based upon Gaussian Mixture Models (GMM) that are learned using an incremental Expectation Maximization clustering algorithm trained online using exemplars provided by a(More)
This paper addresses the localization and navigation problem using invisible two dimensional barcodes on the floor. Compared with other methods using natural/artificial landmark, the proposed localization method has great advantages in cost and appearance, since the location of the robot is perfectly known using the barcode information after the mapping is(More)
A localization algorithm for topological maps with dynamics is proposed in this paper. Especially, this algorithm considers a localization when some nodes are deleted by door closing and has two main features. First, lots of edge information are used in efficient way. Second, the algorithm calculates probability of a node to be the current location in a(More)
This paper presents an optimal path planning algorithm that adopts the rapidly-exploring random tree(RRT) as a path planner. The RRT generates a valid path quickly, but it does not have the ability to control the quality of the path. In this paper, the nonholonomic path planning and the optimal path planning are tackled simultaneously within the RRT(More)
Seung-Joon Yi GRASP Lab, University of Pennsylvania, Philadelphia, Pennsylvania 19104 e-mail: yiseung@seas.upenn.edu Stephen G. McGill GRASP Lab, University of Pennsylvania, Philadelphia, Pennsylvania 19104 e-mail: smcgill3@seas.upenn.edu Larry Vadakedathu GRASP Lab, University of Pennsylvania, Philadelphia, Pennsylvania 19104 e-mail: vlarry@seas.upenn.edu(More)
This paper presents a novel adaptive approach to fast sampling-based motion planning by learning models of collision and collision-free regions in configuration spaces in an online manner. The proposed approach incrementally learns Gaussian Mixture Models (GMMs) for collision detection in high dimensional configuration spaces. In practical applications for(More)
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