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Manipulating deformable linear objects (DLOs) to achieve desired shapes inconstrained 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 totheir deformable nature, and the inevitable modeling errors significantlyaffect the planning results, probably resulting in task failure if the robotsimply executes the planned path in an open-loop manner. In this paper, wepropose a coarse-to-fine framework to combine global planning and local controlfor dual-arm manipulation of DLOs, capable of precisely achieving desiredconfigurations and avoiding potential collisions between the DLO, robot, andobstacles. Specifically, the global planner refers to a simple yet effectiveDLO energy model and computes a coarse path to find a feasible solutionefficiently; then the local controller follows that path as guidance andfurther shapes it with closed-loop feedback to compensate for the planningerrors and improve the task accuracy. Both simulations and real-worldexperiments demonstrate that our framework can robustly achieve desired DLOconfigurations in constrained environments with imprecise DLO models, which maynot be reliably achieved by only planning or control.

A Coarse-to-Fine Framework for Dual-Arm Manipulation of Deformable Linear Objects with Whole-Body Obstacle Avoidance