Optimal scheduling and non-cooperative distributed model predictive control for multiple robotic manipulators

Application of multiple robotic manipulators in a shared workspace is still restricted to repetitive tasks limiting their flexible deployment for production systems. Still, existing motion control algorithms cannot be performed online for arbitrary environments in case of multiple manipulators cooperating with each other. In this work we propose a scalable and real-time capable motion control algorithm based on non-cooperative distributed model predictive control. Furthermore, we propose an optimal scheduling algorithm, which provides optimal setpoints to each robot's motion controller that prevents possible deadlocks beforehand. We validate our approach on a simulative setup of four robotic manipulators for multiple pick and place scenarios.