Efficient human–robot collaborative manipulation of planar deformable objects

This paper presents an efficient method to improve the productivity and the accuracy of human–robot collaboration in transporting large, planar, deformable objects, specifically during the production of parts made of advanced composite materials. The proposed approach utilises an industrial robot to assist operators in transporting and handling carbon fibre and fibreglass plies during draping. A consumer vision system feeds a data-driven model that estimates the material's deformation from depth images. This deformation data, transformed into force/torque information via a virtual spring, informs a Human–Robot Role Arbitration (RA) algorithm that dynamically adjusts leadership between humans and robots based on context, enhancing safety and efficiency. Inspired by game theory, the approach adapts to cooperative and non-cooperative scenarios, demonstrating significant productivity gains over traditional algorithms used for the same scope. The paper also compares the use of the RA algorithms with the current industrial practice, which relies entirely on manual production. Company operators, working in a production site, performed the experimental comparison producing a real boat propeller.