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.
翻译：本文提出了用于路径规划的清晰机器人系统的新颖的反动动能解答器。 IK是机器人操纵的一个传统但必不可少的问题。 最近,提出了数据驱动方法,以快速解决路径规划所需的 IK 。 这些方法可以同时处理大量的 IK 请求,同时利用 GPUs 。 然而, 准确性仍然很低, 模型需要相当长的培训时间。 因此, 我们提出一个 IK 解答器, 利用 Neural ODE 的连续隐藏动态来提高准确性和内存效率。 性能是使用多个机器人比较的。
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