基于遗传算法的海上起重机和机器人通用控制体系结构

F. Sanfilippo, L. I. Hatledal, H. G. Schaathun, K. Pettersen, Houxiang Zhang
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引用次数: 16

摘要

本文介绍了一个灵活和通用的控制系统架构,允许建模,仿真和控制不同型号的海上起重机,更一般地说,机械臂通过使用相同的通用输入装置,而不管它们的尺寸,运动结构,自由度,身体形态,约束和可视性的差异。通过定义相应的Denavit-Hartenberg表及其关节极限,可以简单地将待控制的机械手添加到系统中。模型可以在三维可视化环境中进行模拟,为用户提供直观的视觉反馈。该体系结构为研究通用输入设备与待控机械手之间的柔性映射过程奠定了基础。作为一个案例研究,我们还介绍了实现这种映射算法的第一次尝试。这种方法是受生物启发的,它基于遗传算法(GA)的使用。利用该方法,系统能够自动学习不同模型的逆运动学特性。通过仿真验证了所提架构和映射方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A universal control architecture for maritime cranes and robots using genetic algorithms as a possible mapping approach
This paper introduces a flexible and general control system architecture that allows for modelling, simulation and control of different models of maritime cranes and, more generally, robotic arms by using the same universal input device regardless of their differences in size, kinematic structure, degrees of freedom, body morphology, constraints and affordances. The manipulators that are to be controlled can be added to the system simply by defining the corresponding Denavit-Hartenberg table and their joint limits. The models can be simulated in a 3D visualisation environment, which provides the user with an intuitive visual feedback. The presented architecture represents the base for the research of a flexible mapping procedure between a universal input device and the manipulators to be controlled. As a case study, our first attempt of implementing such a mapping algorithm is also presented. This method is bio-inspired and it is based on the use of Genetic Algorithms (GA). Using this approach, the system is able to automatically learn the inverse kinematic properties of different models. Related simulations were carried out to validate the efficiency of proposed architecture and mapping method.
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