论文标题
NTFIELDS：物理知识机器人运动计划的神经时间领域
NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning
论文作者
论文摘要
神经运动计划者（NMP）已成为解决复杂环境中的机器人导航任务的有前途的工具。但是，这些方法通常需要专家数据进行学习，这将其应用程序限制在数据生成耗时的情况下。最近的发展还导致了物理知识的深层神经模型，能够代表复杂的动力学偏微分方程（PDE）。受这些发展的启发，我们建议在混乱的场景中为机器人运动计划提供神经时间领域（NTFIELDS）。我们的框架代表了一个波传播模型，该模型生成连续到达时间，以找到由非线性一阶PDE告知的路径解，称为Eikonal方程。我们在包括Gibson数据集在内的各种混乱的3D环境中评估了我们的方法，并证明了其为4-DOF和6-DOF机器人操纵器解决运动计划问题的能力，而传统的基于网格的Eikonal Planners通常会面临维度的诅咒。此外，结果表明，与最新方法相比，我们的方法表现出很高的成功率，计算时间明显降低，包括需要经典规划师培训数据的NMP。
Neural Motion Planners (NMPs) have emerged as a promising tool for solving robot navigation tasks in complex environments. However, these methods often require expert data for learning, which limits their application to scenarios where data generation is time-consuming. Recent developments have also led to physics-informed deep neural models capable of representing complex dynamical Partial Differential Equations (PDEs). Inspired by these developments, we propose Neural Time Fields (NTFields) for robot motion planning in cluttered scenarios. Our framework represents a wave propagation model generating continuous arrival time to find path solutions informed by a nonlinear first-order PDE called Eikonal Equation. We evaluate our method in various cluttered 3D environments, including the Gibson dataset, and demonstrate its ability to solve motion planning problems for 4-DOF and 6-DOF robot manipulators where the traditional grid-based Eikonal planners often face the curse of dimensionality. Furthermore, the results show that our method exhibits high success rates and significantly lower computational times than the state-of-the-art methods, including NMPs that require training data from classical planners.
