Uncertainty-Aware Trajectory Planning and Nonlinear Model Predictive Control for Non-Prehensile Robotic Manipulation

Abstract

This paper presents a dual-layered computational framework for the robust trajectory planning and active stabilization of a robotic manipulator transporting a non-fixed payload. The primary challenge addresses the transport of a tray containing multiple objects prone to sliding, exacerbated by significant uncertainties in the system’s dynamic parameters, such as objects’ mass and inertia. The first contribution is an optimal closed-loop sensitivity-based trajectory planning algorithm that generates energy-efficient paths while minimizing the possibility of object sliding. The second contribution is an active sliding control strategy based on Nonlinear Model Predictive Control (NMPC). This algorithm dynamically adjusts the orientation of the tray, mounted to the robot’s end effector, usefully exploiting a dynamic model including inertial forces and gravity to move the object to given positions. Simulation and experimental results demonstrate that the integrated approach allows the robot to set the objects in desired positions with an average steady-state error of 6.3×10−3 m across a set of ten experiments, while limiting the sliding to less than 3% of the tray dimensions during successive transportation in face of a 10% uncertainty about objects’ masses. The synergy between the uncertainty-aware planner and the NMPC controller supports robust tray-transport tasks in unstructured environments where precise dynamic modeling is unavailable.