Communication-based Decentralized Cooperative Object Transportation Using Nonlinear Model Predictive Control
This work addresses the problem of safe and coordinated multi-robot manipulation for applications like logistics or manufacturing, though it appears incremental as it builds on existing NMPC methods.
The paper tackles cooperative object transportation by multiple robots using a decentralized Nonlinear Model Predictive Control (NMPC) scheme, ensuring obstacle avoidance and input constraints, with simulation results validating its efficiency.
This paper addresses the problem of cooperative transportation of an object rigidly grasped by N robotic agents. 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. The control scheme is based on inter-agent communication and is decentralized in the sense that each agent calculates its own control signal. 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.