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.
翻译:反之亦然, 反之亦然, 许多研究人员最近转而采用数据驱动的方法来解决IK问题。 不幸的是, QND(IK), 这是因为这样的武器可能具有多重角度的解决方案, 以达到所期望的同一姿势, 而典型的NNP则只能执行一对一的映射功能, 仅将一个一致的输出用于特定输入。 为了培训可用的NND(NF)来解决IK问题, 多数现有的工程都采用定制的培训数据集, 每一个理想的外形都只有一个角度解决方案。 这不可避免地限制了拟议方法的通用和自动化。 本文分两个方面:(1) 系统而机械地培训涵盖整个机器人臂工作空间的数据收集, 并且只有在手臂发展后才能完全自动化和完成, 将一个一致的映射功能连接到一个特定输入输入的输入。 为了培训可用的NNP(NF), 并且 将一个新式的模型变成一个常规的,, 并且 将一个新式的机械化的模型变成一个常规的模型。