论文标题
自动学习具有冗余DOF的机器人臂的反向运动学的学习
Automating the Learning of Inverse Kinematics for Robotic Arms with Redundant DoFs
论文作者
论文摘要
逆运动学（IK）解决了从笛卡尔空间到机器人臂的关节配置空间的映射问题。它在计算机图形，蛋白质结构预测和机器人技术等领域具有广泛的应用。随着人工神经网络（NNS）的巨大进步，许多研究人员最近转向了解决IK问题的数据驱动方法。不幸的是，NNS因冗余度（DOF）的机器人臂而变得不足。这是因为这样的臂可能具有多个角度解决方案来达到相同所需的姿势，而典型的NNS仅实现一对一的映射功能，这仅将一个一致的输出与给定输入相关联。为了训练可用的NNS解决IK问题，大多数现有作品都采用自定义的培训数据集，其中每个所需的姿势只有一个角度解决方案。这不可避免地限制了拟议方法的概括和自动化。本文在两个方面突破了：（1）一种系统的机械方法，用于培训数据收集，涵盖机器人臂的整个工作空间，并且可以完全自动化，并且在手臂开发后只能完成一次； （2）一种基于NN的新型框架，可以利用冗余DOF为任何给定所需的机器人臂的姿势产生多角度解。后者对于机器人应用特别有用，例如避免障碍物和模仿姿势。
Inverse Kinematics (IK) solves the problem of mapping from the Cartesian space to the joint configuration space of a robotic arm. It has a wide range of applications in areas such as computer graphics, protein structure prediction, and robotics. With the vast advances of artificial neural networks (NNs), many researchers recently turned to data-driven approaches to solving the IK problem. Unfortunately, NNs become inadequate for robotic arms with redundant Degrees-of-Freedom (DoFs). This is because such arms may have multiple angle solutions to reach the same desired pose, while typical NNs only implement one-to-one mapping functions, which associate just one consistent output for a given input. In order to train usable NNs to solve the IK problem, most existing works employ customized training datasets, in which every desired pose only has one angle solution. This inevitably limits the generalization and automation of the proposed approaches. This paper breaks through at two fronts: (1) a systematic and mechanical approach to training data collection that covers the entire working space of the robotic arm, and can be fully automated and done only once after the arm is developed; and (2) a novel NN-based framework that can leverage the redundant DoFs to produce multiple angle solutions to any given desired pose of the robotic arm. The latter is especially useful for robotic applications such as obstacle avoidance and posture imitation.
