Cooperative Manipulation Tasks Research Articles

Real-Time Kinematically Synchronous Planning for Cooperative Manipulation of Multi-Arms Robot Using the Self-Organizing Competitive Neural Network.

This paper presents a real-time kinematically synchronous planning method for the collaborative manipulation of a multi-arms robot with physical coupling based on the self-organizing competitive neural network. This method defines the sub-bases for the configuration of multi-arms to obtain the Jacobian matrix of common degrees of freedom so that the sub-base motion converges along the direction for the total pose error of the end-effectors (EEs). Such a consideration ensures the uniformity of the EE motion before the error converges completely and contributes to the collaborative manipulation of multi-arms. An unsupervised competitive neural network model is raised to adaptively increase the convergence ratio of multi-arms via the online learning of the rules of the inner star. Then, combining with the defined sub-bases, the synchronous planning method is established to achieve the synchronous movement of multi-arms robot rapidly for collaborative manipulation. Theory analysis proves the stability of the multi-arms system via the Lyapunov theory. Various simulations and experiments demonstrate that the proposed kinematically synchronous planning method is feasible and applicable to different symmetric and asymmetric cooperative manipulation tasks for a multi-arms system.

A Metaheuristic Optimization Approach to Solve Inverse Kinematics of Mobile Dual-Arm Robots

This work presents an approach to solving the inverse kinematics of mobile dual-arm robots based on metaheuristic optimization algorithms. First, a kinematic analysis of a mobile dual-arm robot is presented. Second, an objective function is formulated based on the forward kinematics equations. The kinematic analysis does not require using any Jacobian matrix nor its estimation; for this reason, the proposed approach does not suffer from singularities, which is a common problem with conventional inverse kinematics algorithms. Moreover, the proposed method solves cooperative manipulation tasks, especially in the case of coordinated manipulation. Simulation and real-world experiments were performed to verify the proposal’s effectiveness under coordinated inverse kinematics and trajectory tracking tasks. The experimental setup considered a mobile dual-arm system based on the KUKA® Youbot® robot. The solution of the inverse kinematics showed precise and accurate results. Although the proposed approach focuses on coordinated manipulation, it can be implemented to solve non-coordinated tasks.

A Review on Cooperative Robotic Arms with Mobile or Drones Bases

This review paper focuses on cooperative robotic arms with mobile or drone bases performing cooperative tasks. This is because cooperative robots are often used as risk-reduction tools to human life. For example, they are used to explore dangerous places such as minefields and disarm explosives. Drones can be used to perform tasks such as aerial photography, military and defense missions, agricultural surveys, etc. The bases of the cooperative robotic arms can be stationary, mobile (ground), or drones. Cooperative manipulators allow faster performance of assigned tasks because of the available “extra hand”. Furthermore, a mobile base increases the reachable ground workspace of cooperative manipulators while a drone base drastically increases this workspace to include the aerial space. The papers in this review are chosen to extensively cover a wide variety of cooperative manipulation tasks and industries that use them. In cooperative manipulation, avoiding self-collision is one of the most important tasks to be performed. In addition, path planning and formation control can be challenging because of the increased number of components to be coordinated.

Decentralized force and motion control of multiple cooperative manipulators

Decentralized force and motion control of a heavy object with lathing tools, in a cooperative multiple manipulator system is studied in this paper. Interaction of the object with robot manipulators and the environment and interaction of manipulators with each other that produce internal forces in the object are complexities in the system dynamics of cooperative manipulation tasks. In this paper, impedance behaviour between manipulator end-effectors (EE) and the object is developed to achieve springy links that lead to impedance effect between the object and the environment. Then, by focusing on the interaction effects in the system as well as the role of imposed kinematics and force constraints, each manipulator will have its own controller for coordination tasks, environment force regulation and internal forces minimization, without force/torque sensors in the EE. The relevance of the theoretical findings is illustrated using simulation results involving three robots manipulating a common object.

Leader/Follower Force Control of Aerial Manipulators

This article focuses on the modeling and controlling of Unmanned Aerial Manipulators (UAMs) in a leader/follower configuration performing a cooperative manipulation task. Each UAM consists of an Unmanned Aerial Vehicle (UAV) with an attached serial-link robotic manipulator. The Recursive Newton-Euler dynamics formulation is employed to account for the interaction between the UAV and its manipulator. The overall system consists of a couple UAMs with a carrying load. The coupling between these systems is due to the exerted forces by their manipulators through the object characterized by its stiffness matrix. A leader/follower control scheme is employed with a stability-analysis tailored to the UAM-pair. The leader UAM defines the trajectory of the moving object while the follower acts so as to reduce the system’s internal reaction forces. Simulation studies are employed to validate the controller’s performance while comparing the system’s response against that derived from a classical nonlinear tracking controller.

Modeling and Emulating a Physiotherapist's Role in Robot‐Assisted Rehabilitation

In home‐based rehabilitation, one possible approach is haptic teleoperation in which a hospital‐based therapist is haptically linked and tele‐presented to a home‐based patient to effectively simulate traditional in‐hospital therapies over a distance. In this context, this article proposes a learn‐and‐replay (LAR) paradigm that consists of two phases: a therapist‐in‐loop (interactive) phase where the therapist interacts through the haptic teleoperation loop with the patient to perform the cooperative therapy task, and a therapist‐out‐of‐loop (standalone) phase where the therapist's task is played by the patient‐side robot in future repetitions. During the interactive phase, the therapist demonstrates impedance during cooperating with the patient. During the standalone phase, the patient‐side robot is automatically controlled to mimic the therapist's demonstrated impedance which is learned in the interactive phase. The direct force reflection (DFR) architecture is utilized as the control method for the bilateral telerehabilitation system. Case studies involving 1‐degree‐of‐freedom and 2‐degree‐of‐freedom cooperative manipulation tasks are tested for proof of concept. The results show that the impedance of the therapist's arm can be replicated by the patient‐side robot for both tasks and proposed LAR telerehabilitation paradigm that assists the therapist in the rehabilitation procedure to take care of other tasks or attend to other patients.

Kinematic and dynamic manipulability analysis for free-floating space robots with closed chain constraints

This paper presents the manipulability analysis of free-floating multi-arm space robots. Evaluation of manipulator capability is useful both in the design and in the operation phase. After capturing a target, closed kinematic chains are formed with multi-arm cooperative manipulating a common object. Owing to the dynamic coupling effect, the manipulability analysis of free-floating systems is more complex than that of ground-fixed closed chain systems. To analyze the cooperative manipulability, kinematic and dynamic formulations for the free-floating closed chain systems are firstly derived. The formulations describe the mapping of joint velocities and torques, respectively, to task velocities and forces, as well as joint torques to task accelerations and forces, by using the generalized Jacobian matrices. Next, the well-known concepts of manipulability ellipsoid, manipulability measure and task compatibility of the free-floating closed chain system are formally extended. Besides, a new approach called scaling factor method is used in the analysis of the task compatibility, which is more accurate compared with the manipulability ellipsoid method. Three applications of the performance indices are considered: (1) the feasibility analysis for a given task, (2) the trajectory planing giving a desired task path, and (3) configuration optimization with different task requirements. The proposed index is proved a very efficient tool that can be utilized in the cooperative manipulation tasks for free-floating space robotic systems.

Distributed Control for Cooperative Manipulation With Event-Triggered Communication

Cooperative manipulation tasks can be divided into the subtasks of object trajectory tracking and grasp maintenance. Both subtasks typically pose individual requirements on the underlying control objective in terms of accuracy and robustness to disturbances. In this article, we propose a novel distributed impedance control scheme, resulting in a flexible control design to meet the individual-potentially conflicting-impedance goals of the respective subtasks. In order to achieve more efficient use of the communication resource, we propose an event-triggered strategy for the communication between the robotic agents. Robust, stability also in interaction with unknown objects and unknown environments, is guaranteed via passivity-based control design. Simulations and experimental results show that, with the proposed communication strategy, the number of transmissions between agents can be significantly reduced while maintaining the flexibility introduced by the distributed controller.

Controlling two-dimensional collective formation and cooperative behavior of magnetic microrobot swarms

Magnetically actuated mobile microrobots can access distant, enclosed, and small spaces, such as inside microfluidic channels and the human body, making them appealing for minimally invasive tasks. Despite their simplicity when scaling down, creating collective microrobots that can work closely and cooperatively, as well as reconfigure their formations for different tasks, would significantly enhance their capabilities such as manipulation of objects. However, a challenge of realizing such cooperative magnetic microrobots is to program and reconfigure their formations and collective motions with under-actuated control signals. This article presents a method of controlling 2D static and time-varying formations among collective self-repelling ferromagnetic microrobots (100 m to 350 m in diameter, up to 260 in number) by spatially and temporally programming an external magnetic potential energy distribution at the air–water interface or on solid surfaces. A general design method is introduced to program external magnetic potential energy using ferromagnets. A predictive model of the collective system is also presented to predict the formation and guide the design procedure. With the proposed method, versatile complex static formations are experimentally demonstrated and the programmability and scaling effects of formations are analyzed. We also demonstrate the collective mobility of these magnetic microrobots by controlling them to exhibit bio-inspired collective behaviors such as aggregation, directional motion with arbitrary swarm headings, and rotational swarming motion. Finally, the functions of the produced microrobotic swarm are demonstrated by controlling them to navigate through cluttered environments and complete reconfigurable cooperative manipulation tasks.

Human-Robot Team Interaction Through Wearable Haptics for Cooperative Manipulation.

The interaction of robot teams and single human in teleoperation scenarios is beneficial in cooperative tasks, for example, the manipulation of heavy and large objects in remote or dangerous environments. The main control challenge of the interaction is its asymmetry, arising because robot teams have a relatively high number of controllable degrees of freedom compared to the human operator. Therefore, we propose a control scheme that establishes the interaction on spaces of reduced dimensionality taking into account the low number of human command and feedback signals imposed by haptic devices. We evaluate the suitability of wearable haptic fingertip devices for multi-contact teleoperation in a user study. The results show that the proposed control approach is appropriate for human-robot team interaction and that the wearable haptic fingertip devices provide suitable assistance in cooperative manipulation tasks.

Dual-arm cooperative manipulation based on differential evolution

Cooperative manipulation in dual-arm robotic systems is a fundamental capability to perform many problems such as human-like tasks. In this article, we present an approach to solve dual-arm cooperative manipulation tasks using the differential evolution algorithm. In this work, manipulator kinematics are represented using the Denavit–Hartenberg model. The proposed method is able to avoid singularities because it does not require the inversion of any Jacobian matrix. In addition, the proposed approach handles joint limit constraints based on penalty functions. As a final remark, this approach is suitable for robotic systems of redundant or non-redundant serial manipulators composed of revolute or prismatic joints. Simulation experiments illustrate the effectiveness of the proposed approach under different dual-arm configurations. Furthermore, real experiments were performed using a dual-arm KUKA Youbot system to show the applicability of the proposed approach.

Safety in human-multi robot collaborative scenarios: a trajectory scaling approach

In this paper, a strategy to handle the human safety in a multi-robot scenario is devised. In the presented framework, it is foreseen that robots are in charge of performing any cooperative manipulation task which is parameterized by a proper task function. The devised architecture answers to the increasing demand of strict cooperation between humans and robots, since it equips a general multi-robot cell with the feature of making robots and human working together. The human safety is properly handled by defining a safety index which depends both on the relative position and velocity of the human operator and robots. Then, the multi-robot task trajectory is properly scaled in order to ensure that the human safety never falls below a given threshold which can be set in worst conditions according to a minimum allowed distance. Simulations results are presented in order to prove the effectiveness of the approach.

Interactive Plan Execution during Human-Robot Cooperative Manipulation

Abstract Collaborative robots (Cobots) are robotic systems designed to physically interact with humans during task execution in a shared industrial workspace. In this paper, we consider a scenario in which a human operator can be supported by a lightweight robotic arm in order to accomplish complex manipulation tasks. Specifically, we assume that manipulation tasks are explicitly represented as hierarchical task networks to be interactively executed exploiting the human physical guidance. In this setting, the human interventions are continuously interpreted by the robotic system in order to infer whether the human guidance is aligned or not with respect to the planned activities. The interpreted human interventions are then exploited by the robotic system to adapt its cooperative behavior during the execution of the shared plan. Depending on the estimated operator intentions, the robotic system can adjust tasks or motions, while regulating the robot compliance in order to follow or lead the human co-worker. The proposed approach is demonstrated in a testing scenario consisting of a human operator that interacts with a Kuka LBR iiwa arm in order to perform a cooperative manipulation task. The collected experimental results show the feasibility and the effectiveness of the approach.

A Framework for Distributed Control Based on Overlapping Estimation for Cooperative Tasks

We present a framework for distributed control where the subsystems estimate overlapping components of the state of the overall system. This enables the implementation of decentralized state feedback controllers, which depend on the overlapping state estimates. For a distributed framework, communication can be added. By chosing the amount of communication and the degree of overlap in the state estimates, a trade-off between increasing computational effort, communication, and performance can be made. This approach is especially suited for systems with strongly coupled subsystems, which are restricted in communication during their operation. The computational effort of each subsystem depends only on the chosen degree of overlap in the estimates and thus stays constant with an increasing number of subsystems, which makes the approach convenient for the operation of large systems. The design problem of the distributed control and estimation is formulated with bilinear matrix inequalities in an augmented state space. A numerical example of a cooperative manipulation task illustrates the performance of the distributed control and estimation scheme.

Which impedance strategy is the most effective for cooperative object manipulation?

PurposeThe purpose of this paper is to present a comparison study of cooperative object manipulation control algorithms. To this end, a full comprehensive survey of the existing control algorithms in this field is presented.Design/methodology/approachCooperative manipulation occurs when manipulators are mechanically coupled to the object being manipulated, and the manipulators may not be treated as an isolated system. The most important and basic impedance control (IC) strategies for an assumed cooperative object manipulation task are the Augmented Object Model (AOM) control and the multiple impedance control (MIC) which are found based on the IC, where the former is designed based on the object movement, and the latter is designed based on the whole robot movement. Thus, the basis of these two algorithms are fully studied.FindingsThe results are fully analyzed, and it is practically verified that the MIC algorithm has the better performance. In fact, the results reveal that the MIC system could successfully perform the object manipulation task, as opposed to the AOM controller: for the same controller gains, the MIC strategy showed better performance than the AOM strategy. This means that because there is no control on the robot base with the AOM algorithm, the object manipulation task cannot be satisfactorily performed whenever the desired path is not within the robot work space. On the other hand, with the MIC algorithm, satisfactory object manipulation is achieved for a mobile robotic system in which the robot base, the manipulator endpoints and the manipulated object shall be moved.Practical implicationsA simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs.Originality/valueThe basis of these two aspects or these two algorithms is fully studied and compared which is the foundation of this paper. For this purpose, a case study is considered, in which a space free-flying robotic system, which contains two 2-degrees of freedom planar cooperative manipulators, is simulated to manipulate an object using the above control strategies. The system also includes a rotating antenna and camera as its third and fourth arm. Finally, a simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs.

Optimization of potential field method parameters through networks for swarm cooperative manipulation tasks

An interesting current research field related to autonomous robots is mobile manipulation performed by cooperating robots (in terrestrial, aerial and underwater environments). Focusing on the underwater scenario, cooperative manipulation of Intervention-Autonomous Underwater Vehicles (I-AUVs) is a complex and difficult application compared with the terrestrial or aerial ones because of many technical issues, such as underwater localization and limited communication. A decentralized approach for cooperative mobile manipulation of I-AUVs based on Artificial Neural Networks (ANNs) is proposed in this article. This strategy exploits the potential field method; a multi-layer control structure is developed to manage the coordination of the swarm, the guidance and navigation of I-AUVs and the manipulation task. In the article, this new strategy has been implemented in the simulation environment, simulating the transportation of an object. This object is moved along a desired trajectory in an unknown environment and it is transported by four underwater mobile robots, each one provided with a seven-degrees-of-freedom robotic arm. The simulation results are optimized thanks to the ANNs used for the potentials tuning.

A Model for Human–Human Collaborative Object Manipulation and Its Application to Human–Robot Interaction

During collaborative object manipulation, the interaction forces provide a communication channel through which humans coordinate their actions. In order for the robots to engage in physical collaboration with humans, it is necessary to understand this coordination process. Unfortunately, there is no intrinsic way to define the interaction forces. In this study, we propose a model that allows us to compute the interaction force during a dyadic cooperative object manipulation task. The model is derived directly from the existing theories on human arm movements. The results of a user study with 22 human subjects prove the validity of the proposed model. The model is then embedded in a control strategy that enables the robot to engage in a cooperative task with a human. The performance evaluation of the controller through simulation shows that the control strategy is a promising candidate for a cooperative human–robot interaction.

Model and Analysis of the Interaction Dynamics in Cooperative Manipulation Tasks

Efficient coordination of a multirobot team is the key challenge in robotic application domains such as manufacturing, construction, and service robotics. In cooperative manipulation tasks, the system dynamics result from the complex interaction of several manipulators handling a common object. A comprehensive model is indispensable for a sophisticated model-based control design. An open problem is the modeling and analysis of the overall system dynamics including the manipulators’ interaction wrenches. Based on the apparent end-effector dynamics in task space, in this paper we focus on the characterization of the interaction effects when manipulating a common object. We note the central role of the imposed kinematic constraints for the emerging system dynamics, their significance for the manipulator coordination in terms of control design, and the analysis of internal wrenches applied to the object. We derive fundamental properties of the cooperative manipulator system relevant to the manipulation task such as the apparent impedance with respect to external disturbances. An experimental study is conducted with two cooperating anthropomorphic manipulators supporting the relevance of our findings.

Classification of human-robot team interaction paradigms

Abstract: Human-robot team interaction is challenging in terms of system complexity and control synthesis. Classifying different interaction paradigms between a human and a robot team eases the formal analysis. The challenge is to classify the paradigms appropriately, w.r.t. the setting and the task to be performed. In this paper three interaction paradigms are formally defined and analyzed using controllability. It is shown that a straightforward classification of interaction paradigms, based on the mapping properties of the input space to the tangent of the state space is possible. Specific examples in the human-robot team interaction for cooperative manipulation tasks validate the proposed classification methods.

Internal Force Analysis and Load Distribution for Cooperative Multi-Robot Manipulation

The load distribution strategy in cooperative manipulation tasks allocates suitable force and torque setpoints to an ensemble of manipulators in order to implement a desired action on the manipulated object. Due to the manipulator redundancy, the load distribution computed by means of a generalized inverse of the grasp matrix is not uniquely determined. Controversial results on the nonsqueezing property of specific load distributions exist in the literature. In this paper, we propose a new paradigm for the analysis of internal wrenches based on the kinematic constraints imposed to the manipulator ensemble. We unify previous results by showing that there exists no unique nonsqueezing load distribution and illustrate the consequences of our findings by means of several examples. In particular, the presented results provide a new perspective on the decomposition of interaction forces into internal and external components as required for cooperative multimanipulator control schemes.