Low-dimensional Euclidean Space Research Articles

Calculating a minimal sphere containing a polytope defined by a system of linear inequalities

We will propose an algorithm for calculating a minimal sphere containing a polytope defined by a system of linear inequalities in low dimensional Euclidean space. This algorithm is a straightforward application of the algorithm for maximizing a convex quadratic function over a polytope. It will be shown that this algorithm successfully generates a minimal sphere when the dimensions of the underlying space is up to five.

Robot motion planning and the single cell problem in arrangements

Robot motion planning has become a central topic in robotics and has been studied using a variety of techniques. One approach, followed mainly in computational geometry, aims to develop combinatorial, nonheuristic solutions to motion-planning problems. This direction is strongly related to the study of arrangements of algebraic curves and surfaces in low-dimensional Euclidean space. More specifically, the motion-planning problem can be reduced to the problem of efficiently constructing a single cell in an arrangement of curves or surfaces. We present the basic terminology and the underlying ideas of the approach. We describe past work and then survey a series of recent results in exact motion planning with three degrees of freedom and the related issues of the complexity and construction of a single cell in certain arrangements of surface patches in three-dimensional space.

Annihilator structure of a principal ideal: Relation to optimal compensators

Given a linear, time-invariant, discrete-time plant, we consider the optimal control problem of minimizing, by choice of a stabilizing compensator, the seminorm of a selected closed-loop map in a basic feedback system. The seminorm can be selected to reflect any chosen performance feature and must satisfy only a mild condition concerning finite impulse responses. We show that if the plant has no poles or zeros on the unit circle, then the calculation of the minimum achievable seminorm is equivalent to the maximization of a linear objective over a convex set in a low-dimensional Euclidean space. Hence, for a wide variety of optimal control problems, one can compute the answer to an infinite-dimensional optimization by a finite-dimensional procedure. This allows the use of effective numerical methods for computation.