Deformable linear objects (DLOs) manipulation presents significant challenges due to DLOs' inherent high-dimensional state space and complex deformation dynamics. The wide-populated obstacles in realistic workspaces further complicate DLO manipulation, necessitating efficient deformation planning and robust deformation tracking. In this work, we propose a novel framework for DLO manipulation in constrained environments. This framework combines hierarchical deformation planning with neural tracking, ensuring reliable performance in both global deformation synthesis and local deformation tracking. Specifically, the deformation planner begins by generating a spatial path set that inherently satisfies the homotopic constraints associated with DLO keypoint paths. Next, a path-set-guided optimization method is applied to synthesize an optimal temporal deformation sequence for the DLO. In manipulation execution, a neural model predictive control approach, leveraging a data-driven deformation model, is designed to accurately track the planned DLO deformation sequence. The effectiveness of the proposed framework is validated in extensive constrained DLO manipulation tasks.

翻译：可变形线性物体的操控因其固有的高维状态空间与复杂变形动力学而面临重大挑战。现实工作空间中广泛分布的障碍物进一步增加了DLO操控的复杂性，需要高效的变形规划与鲁棒的变形跟踪。本研究提出一种适用于受限环境的DLO操控新框架，该框架将分层变形规划与神经跟踪相结合，确保在全局变形合成与局部变形跟踪中均能实现可靠性能。具体而言，变形规划器首先生成满足DLO关键点路径同伦约束的空间路径集；随后采用路径集引导的优化方法合成DLO的最优时序变形序列。在执行操控时，基于数据驱动变形模型设计的神经模型预测控制方法，可精确跟踪规划好的DLO变形序列。所提框架的有效性在大量受限环境DLO操控任务中得到验证。