解决复杂环境里柔性线状物体的变形规划与跟踪难题
Hierarchical Deformation Planning and Neural Tracking for DLOs in Constrained Environments
- 分层规划先生成满足拓扑约束的路径集
- 优化路径集得到最优时间变形序列
- 神经模型预测控制实现精准变形追踪
柔性线状物体（DLOs）操作因状态空间高维且形变动态复杂而极具挑战，现实工作空间中密集障碍物进一步加剧难度，亟需高效变形规划与鲁棒形变跟踪。本文提出一种面向受限环境的DLO操作新框架，结合分层变形规划与神经追踪，确保全局形变合成与局部形变追踪的可靠性能。具体而言，变形规划器首先生成满足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的最优时间形变序列。在执行阶段，设计基于数据驱动形变模型的神经模型预测控制方法，精确追踪预设的形变序列。该框架在大量受限DLO操作任务中验证了有效性。
原文摘要 · Abstract (English)
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.
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