论文标题
用于双臂操纵可变形线性对象的双臂操纵的粗到精细框架，并避免全身障碍物
A Coarse-to-Fine Framework for Dual-Arm Manipulation of Deformable Linear Objects with Whole-Body Obstacle Avoidance
论文作者
论文摘要
操纵可变形的线性对象（DLOS）在有障碍的受约束环境中实现所需的形状是一项有意义但具有挑战性的任务。对于这项高度约束的任务是必要的；但是，由于其可变形性质，计划人员要求的准确模型很难获得，并且不可避免的建模错误会显着影响计划结果，如果机器人只是以开环的方式执行计划的路径，可能会导致任务失败。在本文中，我们提出了一个粗到精细的框架，以结合全球计划和本地控制，以进行双臂操纵DLO，能够精确实现所需的配置并避免DLO，机器人和障碍物之间的潜在碰撞。具体而言，全球规划师是指一个简单而有效的DLO能量模型，并计算出有效找到可行解决方案的粗糙路径。然后，本地控制器遵循该路径作为指导，并通过闭环反馈进一步塑造它，以补偿计划错误并提高任务准确性。仿真和现实世界实验都表明，我们的框架可以通过不精确的DLO模型在受约束的环境中稳健地实现所需的DLO配置，而DLO模型只能仅通过计划或控制才能可靠地实现。
Manipulating deformable linear objects (DLOs) to achieve desired shapes in constrained environments with obstacles is a meaningful but challenging task. Global planning is necessary for such a highly-constrained task; however, accurate models of DLOs required by planners are difficult to obtain owing to their deformable nature, and the inevitable modeling errors significantly affect the planning results, probably resulting in task failure if the robot simply executes the planned path in an open-loop manner. In this paper, we propose a coarse-to-fine framework to combine global planning and local control for dual-arm manipulation of DLOs, capable of precisely achieving desired configurations and avoiding potential collisions between the DLO, robot, and obstacles. Specifically, the global planner refers to a simple yet effective DLO energy model and computes a coarse path to find a feasible solution efficiently; then the local controller follows that path as guidance and further shapes it with closed-loop feedback to compensate for the planning errors and improve the task accuracy. Both simulations and real-world experiments demonstrate that our framework can robustly achieve desired DLO configurations in constrained environments with imprecise DLO models, which may not be reliably achieved by only planning or control.
