In recent years, there has been a growing interest in robotic manipulation of deformable objects. In order to perform certain tasks, the robot must control the shape of the object while taking care not to apply excessive stresses so as not to deform it irreversibly. This is the case when extracting elasto-plastic objects in strips from an industrial reel. In order to control the mechanical stresses within the object, we propose a vision-based control scheme to minimize tension by regulating the angular velocity of a motorized reel on which they are wound. In this paper, we propose a method, based on a catenary model and visual feedback from a low-cost RGB-D camera, to estimate the tension distribution along a rubber strip. Thus, the control strategy aims to achieve a desired tension value by varying the length of the suspended portion of the manipulated strip. Simulation and experimental results validate the proposed approach for strip-like objects of various dimensions.

Robots are nowadays faced with the challenge of handling deformable objects in industrial operations. In particular, the problem of shape control, which aims at giving a specific deformation state to an object, has gained interest recently in the research community. Among the proposed solutions, approaches based on finite elements proved accurate and reliable but also complex and computationally-intensiveIn order to mitigate these drawbacks, we propose a scheme for shape control that does not require to run a real-time simulation or to solve an implicit optimization problem for computing the control outputs. It is based on a partition of the nodal coordinates that allows deriving a control law directly from tangent stiffness matrices. This formulation is also coupled with the introduction of reduced finite element models. Simulation and experimental results in the context of linear deformable object manipulation demonstrate the interest of the proposed approach.