The Cable-Driven Parallel Robots (CDPRs) have gained significant attention due to their high payload capacity and large workspace. When deploying CDPRs in practice, one of the challenges is kinematic modeling. Unlike serial mechanisms, CDPRs have a simple inverse kinematics problem but a complex forward kinematics (FK) issue. Therefore, the development of accurate and efficient FK solvers has been a prominent research focus in CDPR applications. By observing the topology within CDPRs, in this paper, we propose a graph-based representation to model CDPRs and introduce CafkNet, a fast and general FK solver, leveraging Graph Neural Network (GNN). CafkNet is extensively tested on 3D and 2D CDPRs in different configurations, both in simulators and real scenarios. The results demonstrate its ability to learn CDPRs' internal topology and accurately solve the FK problem. Then, the zero-shot generalization from one configuration to another is validated. Also, the sim2real gap can be bridged by CafkNet using both simulation and real-world data. To the best of our knowledge, it is the first study that employs the GNN to solve FK problem for CDPRs.

翻译：索驱动并联机器人因其高负载能力和大工作空间而受到广泛关注。在实际部署索驱动并联机器人时，运动学建模是主要挑战之一。与串联机构不同，索驱动并联机器人的逆运动学问题较为简单，但正运动学问题却十分复杂。因此，开发精确高效的正运动学求解器一直是索驱动并联机器人应用领域的研究重点。通过观察索驱动并联机器人的拓扑结构，本文提出了一种基于图的表示方法来建模索驱动并联机器人，并引入了CafkNet——一种快速且通用的正运动学求解器，它利用图神经网络实现。CafkNet在不同配置的三维和二维索驱动并联机器人上进行了广泛测试，涵盖模拟器和真实场景。结果表明，CafkNet能够学习索驱动并联机器人的内部拓扑结构，并精确求解正运动学问题。同时，验证了从一种配置到另一种配置的零样本泛化能力。此外，通过使用仿真和真实数据，CafkNet能够弥合仿真与真实之间的差距。据我们所知，这是首次利用图神经网络解决索驱动并联机器人正运动学问题的研究。