A Nonlinear Model Predictive Control Scheme for Cooperative Manipulation with Singularity and Collision Avoidance
This work addresses the challenge of safe and efficient multi-robot manipulation in cluttered environments, representing an incremental improvement in control methods for robotics.
The paper tackles the problem of cooperative transportation of an object by multiple robotic agents, proposing a Nonlinear Model Predictive Control scheme that ensures navigation to a desired pose while avoiding singularities and collisions, with simulation results demonstrating its validity and efficiency.
This paper addresses the problem of cooperative transportation of an object rigidly grasped by $N$ robotic agents. In particular, we propose a Nonlinear Model Predictive Control (NMPC) scheme that guarantees the navigation of the object to a desired pose in a bounded workspace with obstacles, while complying with certain input saturations of the agents. Moreover, the proposed methodology ensures that the agents do not collide with each other or with the workspace obstacles as well as that they do not pass through singular configurations. The feasibility and convergence analysis of the NMPC are explicitly provided. Finally, simulation results illustrate the validity and efficiency of the proposed method.