基于二次规划的机器人机械臂最小冗余解

Kene Li, Yunong Zhang
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引用次数: 2

摘要

将具有关节物理极限的机器人机械臂的最小努力冗余解统一为一个二次规划(QP)问题,该问题具有针对不同方案定义的不同系数矩阵和向量。这样的一般QP公式同时受到等式、不等式和有界约束的约束。为了实时求解这类机器人反运动学问题,本文研究了基于线性变分不等式的标准QP优化方法和基于线性变分不等式的原始对偶神经网络(由于其分段线性动力学简单,计算效率高)。通过PUMA560、PA10和平面臂的计算机模拟,验证了基于qp的机器人冗余分辨率统一。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Minimum-effort redundancy resolution of robot manipulators unified by quadratic programming
This paper presents the latest result that the minimum-effort redundancy resolution of robot manipulators with joint physical limits is unified into a quadratic-programming (QP) problem formulation with different coefficient matrices and vectors defined for different schemes. Such a general QP formulation is subject to equality, inequality and bound constraints, simultaneously. Motivated by the realtime solution to such robotic inverse-kinematics problems, the standard QP optimization routines and primal-dual neural network based on linear variational inequalities (due to its simple piecewise-linear dynamics and higher computational efficiency) are investigated in this paper. The QP-based unification of robots' redundancy resolution is substantiated by a number of computer-simulations of PUMA560, PA10, and planar arms.
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