Human-Robot Co-Transport of Flexible Materials Using Deformation Constraints

The co-transport (collaborative-transport) of deformable materials such as fabrics, composite materials, cables or wires and so on, is a challenging task for robotic applications in industry. The main difficulty lies in the deformability of the material, which can slide, stretch and deform during handling and transport. Common approaches in the literature either take advantage of force sensors to act on the fabric and restore its ideal state, or estimate the deformation state of the fabric with depth images and neural networks (NN). In both cases, issues may arise regarding the effects of force control on the material and the industrial reliability of the NNs, respectively. This paper proposes a method based on the estimation of the deformability constraints of the flexible material to obtain geometric parameters that allow planning the trajectory of a collaborative manipulator for co-transport that guarantees the deformation constraints during transport. By identifying a representative point on the side of the fabric held by the man, the action necessary to restore the desired deformation of the fabric that is initially estimated is planned. Once the material assumes the desired state, the robot's movements are generated to track the human's movement and act in safety conditions. A near-time-optimal control strategy is proposed. Finally, the system is tested in a simulation scenario.