论文标题
节点IK：用神经常规微分方程求解逆运动学以进行路径计划
NODE IK: Solving Inverse Kinematics with Neural Ordinary Differential Equations for Path Planning
论文作者
论文摘要
本文提出了一种新颖的逆运动学（IK）索引机器人系统的求解器，用于路径计划。 IK是机器人操纵的传统但必不可少的问题。最近，已经提出了数据驱动的方法来快速解决IK进行路径计划。这些方法可以通过GPU的优势立即处理大量的IK请求。但是，准确性仍然很低，模型需要大量的培训时间。因此，我们提出了一个IK求解器，该求解器通过利用神经ode的连续隐藏动力学来提高准确性和记忆效率。使用多个机器人比较性能。
This paper proposes a novel inverse kinematics (IK) solver of articulated robotic systems for path planning. IK is a traditional but essential problem for robot manipulation. Recently, data-driven methods have been proposed to quickly solve the IK for path planning. These methods can handle a large amount of IK requests at once with the advantage of GPUs. However, the accuracy is still low, and the model requires considerable time for training. Therefore, we propose an IK solver that improves accuracy and memory efficiency by utilizing the continuous hidden dynamics of Neural ODE. The performance is compared using multiple robots.
