ロボット能力の信頼性計算のための効率的な集合ベース手法
Efficient Set-Based Approaches for the Reliable Computation of Robot Capabilities
区間線形方程式系を用いてロボットの能力を集合的に表現し、ポリトープ・n-立方体・n-球の内部近似を効率的に計算する新しい定理を提案。冗長平面マニピュレータの速度・加速度・静力能力評価に応用。
著者: Joshua Pickard, Vincent Padois, Milan Hladík, David Daney
分類: cs.RO
原文アブストラクト
To reliably model real robot characteristics, interval linear systems of equations allow to describe families of problems that consider sets of values. This allows to easily account for typical complexities such as sets of joint states and design parameter uncertainties. Inner approximations of the solutions to the interval linear systems can be used to describe the common capabilities of a robotic manipulator corresponding to the considered sets of values. In this work, several classes of problems are considered. For each class, reliable and efficient polytope, n-cube, and n-ball inner approximations are presented. The interval approaches usually proposed are inefficient because they are too computationally heavy for certain applications, such as control. We propose efficient new inner approximation theorems for the considered classes of problems. This allows for usage with real-time applications as well as rapid analysis of potential designs. Several applications are presented for a redundant planar manipulator including locally evaluating the manipulator's velocity, acceleration, and static force capabilities, and evaluating its future acceleration capabilities over a given time horizon.
