Calibrationless Bimanual Deformable Linear Object Manipulation With Recursive Least Squares Filter

摘要

In recent years, the domain of robotic manipulation has broadened its focus from rigid objects to more complex tasks involving deformable objects. Compared to rigid bodies, the model-based manipulation of deformable objects requires online adaptation of the model, as it may change during manipulation. This challenge is also evident in the case of Defromable Linear Objects (DLOs), such as wires, hoses, or pipes. In this paper, we introduce a novel model-based method for manipulating DLO that eliminates the need for calibration between the robots and the RGBD camera. We achieve this by utilizing a local linear DLO model, represented by a Jacobian, which maps the movement of the robot’s grippers to the observed DLO displacement. We propose updating this model online using a Recursive Least Squares (RLS) adaptive filter and three distinct Jacobian update strategies. We assess the efficiency of the proposed approaches in nine different real-world manipulation scenarios using three types of wires. The experiments conducted demonstrate that all the proposed strategies provide accurate DLO shape control. However, the update strategy that employs a single action per step, combined with an intermediate DLO state prediction, emerges as the most efficient.