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.

翻译：可变形线性对象（DLO）的机器人操控是一个活跃的研究领域，但新兴应用（如汽车线束安装）引入了以往研究未考虑的约束条件。受限的工作空间和有限的可见性使得多机器人操控以及直接测量DLO构型（状态）等先前假设变得复杂。本研究聚焦于单臂操控连接成DLO网络（DLON）的刚性可变形线性对象（StDLO），其测量值（输出）为DLON的端点位姿，且这些位姿在操控过程中受未知动力学影响。为验证无需状态估计的基于输出的控制可行性，通过训练模拟轨迹上的神经网络模型证明了直接输入-输出动力学的存在性。随后利用多项式近似输出动力学，并发现其包含经典的刚体动力学项。本文开发了一种由刚体模型与在线数据驱动残差组成的复合模型，该模型无需系统先验经验即可比任一单独模型更准确地预测输出动力学。基于该复合模型设计了自适应模型预测控制器用于DLON操控，并在仿真及实体汽车线束上完成了DLON安装任务。