Real-Time Trajectory Adaptation for Tossing Robots Using Soft Switching Multiple Model Predictive Control

Abstract

This paper addresses the challenge of adapting the trajectory of robotic arms performing tosses of objects in dynamic conditions, where target locations can shift during operation. The high-speed, continuous nature of the tossing task necessitates precise and efficient adjustments to ensure that the objects reach their target location. Although nonlinear model predictive controllers (MPCs) are a valuable tool to calculate trajectories, it is difficult to assess in advance how long the calculations will take. They are, hence, difficult to use in tossing under dynamic conditions. To achieve accurate tossing under varying target locations within restricted computational time, we propose a soft switching multiple MPC strategy that enables smooth trajectory updates, while adhering to system constraints. By switching between multiple linear model predictive controllers, our approach ensures rapid trajectory generation, enabling seamless operation in unpredictable conditions. Experimental validation was conducted using a 6-degrees-of-freedom industrial robot arm, demonstrating the effectiveness of the proposed method in reducing computational load by a factor of 1300 compared to a benchmark based on nonlinear MPC. These results confirm its effectiveness for high-speed robotic tossing and suggest broader applicability in tasks requiring real-time trajectory adaptation.