This paper presents a novel knowledge-informed graph neural planner (KG-Planner) to address the challenge of efficiently planning collision-free motions for robots in high-dimensional spaces, considering both static and dynamic environments involving humans. Unlike traditional motion planners that struggle with finding a balance between efficiency and optimality, the KG-Planner takes a different approach. Instead of relying solely on a neural network or imitating the motions of an oracle planner, our KG-Planner integrates explicit physical knowledge from the workspace. The integration of knowledge has two key aspects: (1) we present an approach to design a graph that can comprehensively model the workspace's compositional structure. The designed graph explicitly incorporates critical elements such as robot joints, obstacles, and their interconnections. This representation allows us to capture the intricate relationships between these elements. (2) We train a Graph Neural Network (GNN) that excels at generating nearly optimal robot motions. In particular, the GNN employs a layer-wise propagation rule to facilitate the exchange and update of information among workspace elements based on their connections. This propagation emphasizes the influence of these elements throughout the planning process. To validate the efficacy and efficiency of our KG-Planner, we conduct extensive experiments in both static and dynamic environments. These experiments include scenarios with and without human workers. The results of our approach are compared against existing methods, showcasing the superior performance of the KG-Planner. A short video introduction of this work is available (video link provided in the paper).

翻译：本文提出一种新颖的知识驱动图神经规划器（KG-Planner），旨在解决高维空间中机器人在包含人类的静态与动态环境下高效规划无碰撞运动的难题。与传统运动规划器在效率与最优性之间寻求平衡的困境不同，KG-Planner采取差异化策略——并非单纯依赖神经网络或模仿先知规划器的运动，而是将来自工作空间的显式物理知识融入规划过程。知识融合包含两个关键层面：(1) 提出一种可全面建模工作空间组合结构的图设计方法。该图显式纳入机器人关节、障碍物及其相互连接等关键要素，从而捕捉元素间的复杂关联；(2) 训练一种擅长生成近优机器人运动的图神经网络（GNN）。具体而言，GNN通过层级传播规则实现工作空间元素基于连接关系的信息交互与更新，强化元素对规划过程的影响。为验证KG-Planner的有效性与效率，我们在包含有无人工人的静态及动态环境中开展大量实验，将结果与现有方法对比，证明了所提方法的优越性能。本工作的短视频介绍见论文内嵌链接。