Distributed Motion Control of Multiple Mobile Manipulators for Reducing Interaction Wrench in Object Manipulation

TL;DR

This paper introduces a distributed motion control method for multiple mobile manipulators that reduces interaction forces during object manipulation, emphasizing practicality, robustness, and validation through simulations and physical experiments.

Contribution

The proposed control approach relies solely on local information and joint velocity control, eliminating the need for dynamic models or torque control, and accounts for communication delays.

Findings

Effective in reducing interaction wrenches in simulations

Robust against communication delays and network topology changes

Validated in physical experiments with improved convergence speed

Abstract

In real-world cooperative manipulation of objects, multiple mobile manipulator systems may suffer from disturbances and asynchrony, leading to excessive interaction wrenches and potentially causing object damage or emergency stops. Existing methods often rely on torque control and dynamic models, which are uncommon in many industrial robots and settings. Additionally, dynamic models often neglect joint friction forces and are not accurate. These methods are challenging to implement and validate in physical systems. To address the problems, this paper presents a novel distributed motion control approach aimed at reducing these unnecessary interaction wrenches. The control law is only based on local information and joint velocity control to enhance practical applicability. The communication delays within the distributed architecture are considered. The stability of the control law is rigorously proven by the Lyapunov theorem. In the simulations, the effectiveness is shown, and the impact of communication graph connectivity and communication delays has been studied. A comparison with other methods shows the advantages of the proposed control law in terms of convergence speed and robustness. Finally, the control law has been validated in physical experiments. It does not require dynamic modeling or torque control, and thus is more user-friendly for physical robots.