Guibas, Leonidas J., Latombe, Jean-Claude, Lavalle, Steven M., Lin, David, Motwani, Rajeev, and Agarwal, P. K.
International Journal of Computational Geometry & Applications. Aug1999, Vol. 9 Issue 4/5, p471. 23p.
Subjects
Robotics and Motion
Abstract
This paper addresses the problem of planning the motion of one or more pursuers in a polygonal environment to eventually “see” an evader that is unpredictable, has unknown initial position, and is capable of moving arbitrarily fast. This problem was first introduced by Suzuki and Yamashita. Our study of this problem is motivated in part by robotics applications, such as surveillance with a mobile robot equipped with a camera that must find a moving target in a cluttered workspace. A few bounds are introduced, and a complete algorithm is presented for computing a successful motion strategy for a single pursuer. For simply-connected free spaces, it is shown that the minimum number of pursuers required is Θ(lg n). For multiply-connected free spaces, the bound is Θ(√h+lg n) pursuers for a polygon that has n edges and h holes. A set of problems that are solvable by a single pursuer and require a linear number of recontaminations is shown. The complete algorithm searches a finite graph that is constructed on the basis of critical information changes. It has been implemented and computed examples are shown. [ABSTRACT FROM AUTHOR]
International Journal of Computational Geometry & Applications. Oct2004, Vol. 14 Issue 4, p261-276. 16p.
Subjects
Estimation theory, Least squares, Set theory, Manifolds (Mathematics), Curvature, and Geometric surfaces
Abstract
In this paper we describe and analyze a method based on local least square fitting for estimating the normals at all sample points of a point cloud data (PCD) set, in the presence of noise. We study the effects of neighborhood size, curvature, sampling density, and noise on the normal estimation when the PCD is sampled from a smooth curve in ℝ2 or a smooth surface in ℝ3, and noise is added. The analysis allows us to find the optimal neighborhood size using other local information from the PCD. Experimental results are also provided. [ABSTRACT FROM AUTHOR]