Sequential Manipulation of Deformable Linear Object Networks with Endpoint Pose Measurements using Adaptive Model Predictive Control

Robotic manipulation of deformable linear objects (DLOs) is an active area of research, though emerging applications, like automotive wire harness installation, introduce constraints that have not been considered in prior work. Confined workspaces and limited visibility complicate prior assumptions of multi-robot manipulation and direct measurement of DLO configuration (state). This work focuses on single-arm manipulation of stiff DLOs (StDLOs) connected to form a DLO network (DLON), for which the measurements (output) are the endpoint poses of the DLON, which are subject to unknown dynamics during manipulation. To demonstrate feasibility of output-based control without state estimation, direct input-output dynamics are shown to exist by training neural network models on simulated trajectories. Output dynamics are then approximated with polynomials and found to contain well-known rigid body dynamics terms. A composite model consisting of a rigid body model and an online data-driven residual is developed, which predicts output dynamics more accurately than either model alone, and without prior experience with the system. An adaptive model predictive controller is developed with the composite model for DLON manipulation, which completes DLON installation tasks, both in simulation and with a physical automotive wire harness.

可变形线性物体（DLO）的机器人操作是一个活跃的研究领域，然而新兴的应用，比如汽车线束安装，带来了在先前工作中未被考虑的限制条件。受限的工作空间和有限的可视性使多机器人操作以及DLO形态（状态）的直接测量的先前假设变得复杂。这项工作专注于对连接形成DLO网络（DLON）的刚性DLO（StDLO）进行单臂操作，对于这种操作，测量值（输出）是DLON的端点位姿，在操作过程中其受到未知动力学的影响。为了证明在没有状态估计的情况下基于输出的控制的可行性，通过在模拟轨迹上训练神经网络模型，表明直接的输入 - 输出动力学是存在的。然后用多项式近似输出动力学，并且发现其中包含众所周知的刚体动力学项。开发了一种由刚体模型和在线数据驱动残差组成的复合模型，该模型比单独使用任何一个模型都能更准确地预测输出动力学，而且不需要对系统有先验经验。利用该复合模型为DLON操作开发了一种自适应模型预测控制器，它在模拟环境以及实际的汽车线束中都能完成DLON安装任务。