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The distributed coordinated tracking control problem for multiple robotic manipulators under intermittent communications is researched, respectively, with a stationary leader and a dynamic leader in this note. First, three auxiliary variables are introduced and a new distributed coordinated tracking controller is established with a stationary leader, and then, a multi-step design algorithm is proposed to calculate the controller gain matrix and coupling gain. The stability of the controller is further proved, and the range of communication rate is obtained by using Lyapunov stability and switching system theories. Second, with considering a dynamic leader whose joint velocity is unavailable, the distributed velocity estimator is constructed to estimate the unknown joint velocity, and then, a distributed coordinated tracking control strategy-based velocity estimator is designed, whose unknown parameters are resolved by the new multi-step design algorithm accordingly. It is proved that the stability of the controller can be achieved, and the threshold value of the communication rate obtained by utilizing Lyapunov stability theory and LMI technology. Finally, two simulation examples and quantitative comparison are provided to demonstrate the validity and correctness of the obtained methods, and the experimented results show that the distributed tracking controllers of this work can effectively solve the distributed coordinated tracking problem for multiple two-link manipulator systems under intermittent communications.

In multi-manipulator systems operating within shared workspaces, achieving collision-free posture control is challenging due to high degrees of freedom and complex inter-manipulator interactions. Traditional motion planning methods often struggle with scalability and computational efficiency in such settings, motivating the need for learning-based approaches. This paper presents a multi-agent deep reinforcement learning (MADRL) framework for real-time collision-free posture control of multiple manipulators. The proposed method employs a line-segment representation of manipulator links to enable efficient interlink distance computation to guide cooperative collision avoidance. Employing a centralized training and decentralized execution (CTDE) framework, the approach leverages global state information during training, while enabling each manipulator to rely on local observations for real-time collision-free trajectory planning. By integrating efficient state representation with a scalable training paradigm, the proposed framework provides a principled foundation for addressing coordination challenges in dense industrial workspaces. The approach is implemented and validated in NVIDIA Isaac Sim across various overlapping workspace scenarios. Compared to conventional state representations, the proposed method achieves faster learning convergence and superior computational efficiency. In pick-and-place tasks, collaborative multi-manipulator control reduces task completion time by over 50% compared to single-manipulator operation, while maintaining high success rates (>83%) under dense workspace conditions. These results confirm the effectiveness and scalability of the proposed framework for real-time, collision-free multi-manipulator control.

In this article, the model of a family of underwater vehicle multi-manipulator systems (UVMMS) is obtained by considering all its elements as parts of a unique system, the model includes the forces produced on the manipulators by the movement of the vehicle, as well as the reaction forces on the vehicle produced by the movement of the manipulators. The modeling process is completed using the Newton–Euler approach through the mobile arborescent kinematic chain. This work also presents different approaches to the use of numerical implementations of the proposed model, and simulation results are included to demonstrate that the model is capable to represent the interaction between the vehicle and the manipulators. The proposed model and simulations are important because they allow the design of control strategies that consider all the elements of the system instead of neglecting the interaction forces or considering the vehicle and the arms as uncoupled elements.

Analogous to the definition of human–robot interaction, the case of multiple manipulators with shared workspace, asynchronous manufacturing tasks, and independent objects is named as a multi-manipulator cooperative manufacturing system. Multi-manipulator cooperation is increasingly used in modern industrial manufacturing systems and requires collision-free path planning as a vital issue in terms of safety and efficiency. This study proposes a novel method called Sampling-based Operation Space Map Search method, which combines a map search method with a time-sampling-based method. Two candidate position determination methods are proposed to establish local planning maps for all manipulators individually during each sampling time interval. After a specific search map simplification process, the optimal local path fragments within each sampling period can be determined by the map search method. Then, all chronologically planned path segments can be glued together to generate collision-free paths for all manipulators in a multi-manipulator cooperative manufacturing system. The simulation results demonstrated that the proposed strategy could successfully achieve collision avoidance of dual manipulator system whilst meeting the real-time requirements for cooperative assembling scenarios. Compared with the conventional map search method, this proposal is highly effective at the cost of forgoing the global optimum. Further satisfactory simulation results for triple manipulators indicate that our algorithm can be extended to multi-manipulator cooperative manufacturing applications.

In this article a robust and simple procedure for the on-line concurrent motion planning of multi-manipulators is presented. The approach is based on solving for each manipulator a linear system of equations taking into account a vector for motion planning, and an original scheme for the appropriate perturbation of the pseudoinverse matrix. This method can pursue simultaneously both motion coordination and singularities prevention in real time in a sensor based environment. These properties make it suitable for fully autonomous or telerobotic systems operations.

Constrained motion and redundant degrees of freedom control exist in a multi-manipulator collaboration system. In other words, the multi-manipulator collaboration technology must solve the problems of uncertain environment interaction and coordinated control. Few studies have been conducted on the coordination control of a multi-manipulator, and the control effect is not good. To solve the coordinated motion problem of the multi-manipulator cooperative system, this study divides the multi-manipulator coordinated motion into two forms, namely coupled and superimposed motions, and proposes an adaptive coordinated motion constraint scheme under different motion forms. The coupled and superimposed motions are investigated through coordinated handling and coordinated drawing circle tasks, respectively. The proposed coordinated control scheme has a good effect. Without position detection and positioning, the kinematic constraint algorithm can maintain the relative motion relationship between end-effectors. When an external disturbance occurs, the slave manipulator can automatically adjust based on the position of the main manipulator, avoiding error accumulation. The experimental results show a maximum trajectory tracking error of 2.131 mm and maximum attitude error of 0.176°, indicating that the proposed control scheme has strong adaptive ability and high control accuracy.

This paper reports the design of an iterative-learning-scheme-based fault-estimation method for interconnected nonlinear multi-flexible manipulator systems with arbitrary initial value. For state estimation, observers are employed to reconstruct the state. The proposed scheme ensures that each flexible manipulator subsystem’s states can track their desired reference signals within a finite time. In the next step, an iterative learning fault-estimation law is proposed to track the actual fault signal. In contrast to the previous literature, this approach utilizes potential information from previous iterations to enhance the accuracy of the estimation in the current iteration. Based on these efforts, the obstacle caused by the arbitrary initial value is circumvented, and addressing the fault-estimation errors of each flexible manipulator subsystem are uniformly ultimately bounded is successfully achieved. Then, the λ-norm is developed to explore the convergence conditions of the presented methods. Finally, the effectiveness and feasibility of the proposed approach are verified through assessment of simulation results.

In this paper, the predefined-time control problem with prescribed performance is investigated for bilateral teleoperation systems with a single master and multiple slaves. The difficulty lies in the realization of predefined time stability and synchronization control of single-master and multi-slave manipulators. A prescribed performance function is introduced and an improved error conversion mechanism for matrix transformation is designed to ensure that joint displacement tracking error is constrained. A novel coordination position error transformation is defined such that the positional synchronization of the single-master-multiple-slave manipulators is achieved. Based on graph theory and Lyapunov stability theorem, a novel adaptive neural control approach is developed with predefined time interval via the backstepping technique, such that the convergence time can be predefined by users specification, the tracking error can be limited within a prescribed bound and in the meantime can converge to zero within predefined time interval. Simulation results are provided to prove the effectiveness of the presented scheme.