arXiv:2503.04007cs.RO2025-03被引 8

用标准规划工具实现复杂环境下绳索类物体的高效安全操控

Planning and Control for Deformable Linear Object Manipulation

  • 将绳索抽象为刚性链接+球铰结构,结合现成全局规划器快速生成路径
  • 局部控制器用控制屏障函数保障安全,100%成功率且规划时间显著降低
  • 无需定制算法或大量数据,适合移动机械臂在真实场景中快速部署

操纵如电线、电缆、绳索等变形线性物体(DLO)是常见但极具挑战的任务,因其高自由度和复杂的形变行为,尤其在障碍物环境中。现有局部控制方法效率高但易失败,精确全局规划计算量大且难部署。本文提出一种高效、易部署的无碰撞DLO操控框架,利用标准规划工具实现高维空间下的操控,无需定制规划器或大规模数据驱动模型。方法结合现成全局规划器与实时局部控制器:全局规划器将DLO近似为由球铰连接的刚性链段,实现快速路径规划;局部控制器采用控制屏障函数(CBFs)确保安全约束,维持物体完整性,防止过应力,并处理避障;通过基于位置的动力学技术补偿建模误差,近似材料属性如杨氏模量和剪切模量。我们在大量仿真与真实实验中验证了该框架的有效性。在包含帐篷杆搬运、走廊导航及不同刚度需求的复杂障碍场景中,方法在数千次试验中达到100%成功率,规划时间显著优于当前最优技术。真实实验包括使用移动机械臂运输帐篷杆和绳索。我们开源了基于ROS的实现,便于在各类应用中推广。

原文摘要 · Abstract (English)

Manipulating a deformable linear object (DLO) such as wire, cable, and rope is a common yet challenging task due to their high degrees of freedom and complex deformation behaviors, especially in an environment with obstacles. Existing local control methods are efficient but prone to failure in complex scenarios, while precise global planners are computationally intensive and difficult to deploy. This paper presents an efficient, easy-to-deploy framework for collision-free DLO manipulation using mobile manipulators. We demonstrate the effectiveness of leveraging standard planning tools for high-dimensional DLO manipulation without requiring custom planners or extensive data-driven models. Our approach combines an off-the-shelf global planner with a real-time local controller. The global planner approximates the DLO as a series of rigid links connected by spherical joints, enabling rapid path planning without the need for problem-specific planners or large datasets. The local controller employs control barrier functions (CBFs) to enforce safety constraints, maintain the DLO integrity, prevent overstress, and handle obstacle avoidance. It compensates for modeling inaccuracies by using a state-of-the-art position-based dynamics technique that approximates physical properties like Young's and shear moduli. We validate our framework through extensive simulations and real-world demonstrations. In complex obstacle scenarios-including tent pole transport, corridor navigation, and tasks requiring varied stiffness-our method achieves a 100% success rate over thousands of trials, with significantly reduced planning times compared to state-of-the-art techniques. Real-world experiments include transportation of a tent pole and a rope using mobile manipulators. We share our ROS-based implementation to facilitate adoption in various applications.

DLO操控路径规划控制屏障函数移动机械臂

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