Deformable Linear Object Surface Placement Using Elastica Planning and Local Shape Control
This addresses a specific challenge in robot manipulation for constrained environments, such as food handling, with incremental improvements in control and error recovery.
The paper tackles the problem of manipulating deformable linear objects (DLOs) on flat surfaces using a two-layered approach with elastica planning and local shape control, achieving recovery from planner failures in simulations and experiments with silicon mock-ups for food applications.
Manipulation of deformable linear objects (DLOs) in constrained environments is a challenging task. This paper describes a two-layered approach for placing DLOs on a flat surface using a single robot hand. The high-level layer is a novel DLO surface placement method based on Euler's elastica solutions. During this process one DLO endpoint is manipulated by the robot gripper while a variable interior point of the DLO serves as the start point of the portion aligned with the placement surface. The low-level layer forms a pipeline controller. The controller estimates the DLO current shape using a Residual Neural Network (ResNet) and uses low-level feedback to ensure task execution in the presence of modeling and placement errors. The resulting DLO placement approach can recover from states where the high-level manipulation planner has failed as required by practical robot manipulation systems. The DLO placement approach is demonstrated with simulations and experiments that use silicon mock-up objects prepared for fresh food applications.