Cooperative multi-robot manipulation requires control strategies that achieve precise object trajectory tracking and minimize internal object forces under model uncertainties and external disturbances. This paper proposes a distributed adaptive super-twisting sliding mode impedance (STSMI) control framework for cooperative manipulation. The approach integrates adaptive super-twisting sliding mode control with impedance-based force regulation in task space to ensure robustness, compliance, and stability. Quaternion-based control ensures smooth and stable orientation tracking. The proposed controller balances tracking accuracy and internal force minimization compared to conventional controllers. Simulation results with two 7-degree-of-freedom (DOF) manipulators show improved tracking accuracy, reduced internal forces, and adaptability to various configurations. Experimental validation confirms the controller’s robustness and real-world applicability.

In situations involving teams of diverse robots, assigning appropriate roles to each robot and evaluating their performance is crucial. These roles define the specific characteristics of a robot within a given context. The stream of actions exhibited by a robot based on its assigned role are referred to as the process role. Our research addresses the depiction of process roles using a multivariate probabilistic function. The main aim of this study is to develop a role engine for collaborative multirobot systems and optimize the behavior of the robots. The role engine is designed to assign suitable roles to each robot, generate approximately optimal process roles, update them on time, and identify instances of robot malfunction or trigger replanning when necessary. The environment considered is dynamic, involving obstacles and other agents. The role engine operates hybrid, with central initiation and decentralized action, and assigns unlabeled roles to agents. We employ the Gaussian process (GP) inference method to optimize process roles based on local constraints and constraints related to other agents. Furthermore, we propose an innovative approach that utilizes the environment’s skeleton to address initialization and feasibility evaluation challenges. We successfully demonstrated the proposed approach’s feasibility, and efficiency through simulation studies and real-world experiments involving diverse mobile robots.

This paper presents a novel approach to time-varying formation for multi-agent systems for operation in an unknown environment. The time-varying formation uses a leader-follower system with a dynamic leader selection process. Leaders are determined by calculating the center of formation, and determining the position of a predefined goal point relative to the center. By incorporating a distributed protocol in which each agent calculates an estimate of the center based on incomplete information, the agents are able to determine their roles without requiring a central control system. A role negotiation process is developed for resolving edge cases. Two sets of simulations are conducted; the first set showing the capabilities and limitations of the center of formation estimation method, and the second set showcasing a team of robots navigating a series of environments using the proposed time-varying formation algorithm.

In this paper, the bilateral teleoperation of cooperative manipulators is achieved and experimentally analyzed. The master and slave robots are asymmetrical, and only the master end effector’s task space velocity signals are transmitted through the communication network, while the task space force signals of slave robot are relayed back. A power-based time domain passivity control (PTDPC) approach is employed for the controller design to ensure the passivity of the communication channel in the presence of time-varying delays and are applied to each side of the communication channel at every time constant. This model-free method does not require the dynamic models of the master or slave systems to be known. The slave robot acts as the leader of the remote dual-arm cooperative manipulator system that is used to manipulate a common rigid object. This leader robot is controlled using position control mode to track the trajectory of the master, while the follower robot employs an admittance control method to follow the leader’s motion trend. The follower robot is not required to transmit or receive any communication data, which simplifies the network communication topology. Experimental results are presented to verify the effectiveness and simplicity of the designed framework in the presence of large, time-varying and asymmetric delays.

In this paper, an adaptive mixed feedback controller using fuzzy logic control (FLC) is proposed to improve the performance of the synchronization of a group of leader-follower agents with unknown time-varying communication delays. With the aim to improve the overall system performance while ensuring the stability under delays, Lyapunov-based methods and linear matrix inequality (LMI) techniques are applied to design a distributed control policy that uses agent state information with and without estimated self-delays. FLC is applied to online tune the control gains and weight of the self-delayed state in the controller as a nonlinear function of the total consensus error. Numerical simulations of a leader-follower group of five and seven DC motors are carried out to demonstrate the effectiveness and improvement in overall performance of the proposed controller.