Nonlinear Model Predictive Control for Robotic Pushing of Planar Objects With Generic Shape

Robotic manipulation of objects in cluttered dynamic scenes is challenging for a twofold reason. Object detection and localization are complex due to partial occlusions and high variability in the object classes and manipulation in tight spaces is difficult due to potential collisions. The present letter focuses on the low-level control of the non-prehensile pushing action aimed at moving planar objects of generic shape along a given path with an assigned time law. Based on the continuous and nonlinear dynamics of the system, we propose a nonlinear model predictive controller (NMPC), which avoids the need for linearization and, thus, the hybrid dynamics arising from it. An extensive comparison with a state-of-the-art linear MPC demonstrates that the NMPC can successfully react to more general disturbances, outperforming the linear one. Experimental results confirm the effectiveness of the method in a task where a robot is required to grasp fruits in a container with other obstructing objects (shown in the attached video).