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 via this https://zh.engr.tamu.edu/wp-content/uploads/sites/310/2024/03/KGPlanner.mp4link. Note to Practitioners—This paper was motivated by the problem of human-robot collaboratively working on remanufacturing processes such as disassembly that require human operators and collaborative robots to work closely with each other. The robots need to plan their trajectories efficiently enough to avoid collision with humans and the trajectories need to be short enough to reduce the cycle time. Traditional motion planners usually struggle with finding a balance between efficiency and optimality, which limits wide applications of collaborative robots in remanufacturing systems that are usually less structured than manufacturing systems. This paper suggests a new planning approach that integrates the workspace’s physical information into a graph and leverages deep learning to obtain safe and near-optimal solutions quickly. Experimental studies and observations demonstrated some advantages of this approach including learning capability, efficiency, and optimality, which makes it a great potential approach to be applied to real remanufacturing processes.

中文翻译：

---

KG-Planner：协作机械手的基于知识的图神经规划


本文提出了一种新颖的基于知识的图神经规划器（KG-Planner），以解决在高维空间中有效规划机器人无碰撞运动的挑战，同时考虑涉及人类的静态和动态环境。与努力在效率和最优性之间寻找平衡的传统运动规划器不同，KG-Planner 采用了不同的方法。我们的 KG-Planner 不是仅仅依赖神经网络或模仿预言规划器的运动，而是集成了工作空间中的显式物理知识。知识的整合有两个关键方面：1）我们提出了一种设计图形的方法，可以全面建模工作空间的组成结构。设计的图表明确包含了机器人关节、障碍物及其互连等关键元素。这种表示方式使我们能够捕捉这些元素之间复杂的关系； 2）我们训练了一个图神经网络（GNN），它擅长生成近乎最佳的机器人运动。特别是，GNN 采用分层传播规则来促进工作空间元素之间基于其连接的信息交换和更新。这种传播强调了这些元素在整个规划过程中的影响。为了验证 KG-Planner 的功效和效率，我们在静态和动态环境中进行了广泛的实验。这些实验包括有人类工人和没有人类工人的场景。我们的方法的结果与现有方法进行了比较，展示了 KG-Planner 的卓越性能。有关这项工作的简短视频介绍可通过 https://zh.engr.tamu.edu/wp-content/uploads/sites/310/2024/03/KGPlanner.mp4link 获取。 从业者注意事项——本文的动机是人机协作进行再制造过程（例如拆卸），需要人类操作员和协作机器人彼此密切合作。机器人需要足够有效地规划其轨迹以避免与人类发生碰撞，并且轨迹需要足够短以减少循环时间。传统的运动规划器通常难以在效率和最优性之间找到平衡，这限制了协作机器人在通常不如制造系统结构化的再制造系统中的广泛应用。本文提出了一种新的规划方法，将工作空间的物理信息集成到图表中，并利用深度学习快速获得安全且接近最优的解决方案。实验研究和观察证明了这种方法的一些优点，包括学习能力、效率和优化性，这使其成为应用于实际再制造过程的巨大潜力方法。