Cooperative Manipulation Research Articles

Physical Human–Robot Cooperation Based on Robust Motion Intention Estimation

SUMMARYCooperative transportation by human and robotic coworkers constitutes a challenging research field that could lead to promising technological achievements. Toward this direction, the present work demonstrates that, under a leader–follower architecture, where the human determines the object’s desired trajectory, complex cooperative object manipulation with minimal human effort may be achieved. More specifically, the robot estimates the object’s desired motion via a prescribed performance estimation law that drives the estimation error to an arbitrarily small residual set. Subsequently, the motion intention estimation is utilized in the object dynamics to determine the interaction force between the human and the object. Human effort reduction is then achieved via an impedance control scheme that employs the aforementioned estimations. The feedback relies exclusively on the robot’s force/torque, position as well as velocity measurements at its end effector, without incorporating any other information on the task. Moreover, an adaptive control scheme is adopted to relax the need for exact knowledge of the object dynamics. Finally, an extension for multiple robotic coworkers is studied and verified via simulation, while extensive experimental results for the single robot case clarify the proposed method and corroborate its efficiency.

Dynamic modeling of cooperative manipulators with frictional contact at the end effectors

The kinematic and dynamic constraints of a closed kinematic chain as well as the motion equations of a cooperative robotic chain are derived in the present article. In its structure, this system employs friction to bring the end effector of cooperative arm with the common load into contact. This results in complex and time-varying constraints on the load and the end effector of cooperative arm as slip can occur on the object surface. On the contrary, one can easily change the connection loop of arms in face of obstacles. To this end, the specified objective is considered in the design of robot path and solution algorithms are defined based on the possibility of slip. The results are then used to implement the optimization algorithm of load distribution and calculate the dynamic load-carrying capacity. Hence, not only is the inverse dynamics studied in detail, the robot performance is evaluated in various conditions. For this purpose, the results of a cooperative robotic chain carrying a common load are compared for different values of surface friction coefficient and maximum permissible internal force. This is followed by a comparison with appropriate simulations in ANSYS. Moreover, the dynamic load-carrying capacity of the system in terms of the changes in maximum permissible internal force and the surface friction coefficient between the arm's end effector and the load is obtained. The results indicate that when appropriate conditions for the friction between end effector and load in addition to an internal force proportionate to the path are provided, the dynamic load-carrying capacity of any system can be improved. For instance, on a certain path and for the friction coefficient of 0.8 and internal force of 0.2, the dynamic load-carrying capacity of a single-arm system with grasp contact in end effector, after adding the auxiliary arm with frictional contact in end effector, reached 9.34 kg which shows an increase of 5.09 kg.

Predefined‐time control of cooperative manipulators

SummaryA cooperative controller that enforces an exact sliding motion in predefined‐time, assuring cooperativeness, and stable manipulation, is proposed in this article. Alternatively, a continuous controller is also proposed, which induces in predefined‐time that a hybrid force/position error converges into a predefined‐bounded vicinity of the origin. Numerical simulations highlight the effectiveness of the proposed scheme.

Skill Learning Strategy Based on Dynamic Motion Primitives for Human–Robot Cooperative Manipulation

This article presents a skill learning-based hierarchical control strategy for human-robot cooperative manipulation, which constitutes a novel learning-control system. The high-level learning strategy aims to learn the motor skills from human demonstrations by fusion with dynamic motion primitives (DMPs) and the Gaussian mixture model (GMM). The lower level control strategy guarantees the compliance of the robot movement under human interaction using admittance control and integral barrier Lyapunov function (IBLF)-based adaptive neural controller. First, the robot learns the motor skills from observing the successful execution of tasks by a demonstrator through DMP-GMM methods. Then, the robot reproduces the complex skills and executes the interactive task by demonstrations. Finally, the effectiveness of the proposed learning-control strategy is demonstrated with experimental results. The results show that the developed hierarchical strategy has good performance in cooperation by learning and control that reacts compliantly to robot interaction with human subjects.

Autonomous Underwater Intervention

There is a growing interest and literature on autonomous underwater intervention systems. The purpose of this paper is to provide a review of the recent literature on control systems for autonomous or semi-autonomous underwater manipulation activities, classifying the results based on the type of task executed and the testing environment (simulation, pool, or sea). Amongst underwater intervention tasks, the grasping of objects lying on the seafloor is one of the most studied topics. Several results are given both in pool and sea environments. The perception of such objects might still need further improvements before the system can be considered robust enough. Manipulation of valves while docked has been demonstrated in sea environments. Results on floating valve manipulation or floating inspection through force regulation are still limited to pool environments. Finally, cooperative transportation by multiple agents is still limited to numerical simulation results only. A review of the state of the art of underwater manipulation is presented. First an introduction is given, recalling the fundamental milestones reached in the past on this topic. Then, recent findings on control systems for (semi-)autonomous intervention are presented, subdivided in grasping, valve manipulation and force regulation tasks and cooperative manipulation. Some unconventional systems are also presented.

Cooperative Manipulation of an Unknown Object via Omnidirectional Unmanned Aerial Vehicles

This paper addresses the problem of cooperative manipulation and transportation of large and/or heavy unknown objects via a team of aerial robots. In particular, the chosen aerial platform is an omnidirectional quadrotor, which, by exploiting two additional actuators, has the capability to produce an omnidirectional total thrust by changing the orientation of the plane of the rotors. Since it is assumed that the object geometry and dynamic parameters are not known, the devised strategy includes a first stage in which the robots cooperatively estimate the object parameters and a second stage in which such parameters are adopted in an impedance control, aimed at limiting both the contact wrenches, due to the object/environment interaction, and the internal wrenches, due to the robot/object interaction. More in detail, two admittance filters are designed to determine the reference trajectory for the object (external impedance) and for the aerial vehicles (internal impedance). Finally, a simulation case study is developed to provide further insights on the proposed approach.

Trajectory coordination for a cooperative multi-manipulator system and dynamic simulation error analysis

To achieve temporal and spatial correspondence between multiple robotic manipulators, the system must correctly analyze the coordinated path required for a specific task. Based on manipulator kinematics analysis, we first studied the kinematic constraints between the end-effectors of cooperative manipulators, and deduced the multi-manipulator cooperative kinematics constraint equations in the Cartesian coordinate system space under different motion modes. Then, we created two MD-6 manipulator models to simulate the trajectory simulation of the synchronous and relative motion of the manipulator. This allowed us to verify the correctness of the proposed trajectory coordination method, and analyze the influence of external loads on the position and posture of the end-effector of the manipulator, to effectively predict the cooperative motion error of the manipulator system. Finally, in order to verify the effectiveness of the proposed trajectory coordination method, we established a robotic experimental platform and conducted experimental research. The results show that the multi-manipulator trajectory coordination method studied in this paper can make multi-manipulators effectively achieve the target requirements of tasks such as time and space cooperative handling and circular drawing operations.

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.

Dual feedforward neural networks based synchronized sliding mode controller for cooperative manipulator system under variable load and uncertainties

The problem of simultaneous position and internal force control is discussed with cooperative manipulators system under variable load and dynamic uncertainties in this study. A position synchronized sliding mode controller is proposed in the presence of variable load, as well as modeling uncertainties, joint friction, and external disturbances. To deal with the complex situation brought by variable load, virtual synchronization coupled errors are introduced for internal force tracking control and joint synchronization in the meantime. Dual feedforward neural networks are adopted, where a radial basis function-neural network based dynamic compensator and a radial basis function-neural network based internal force estimator are established, respectively, so that precise dynamic knowledge and force measurement are out of demand through their cooperation. Together with simulation studies and analysis, the position and internal force errors are shown to converge asymptotically to zero. Using Lyapunov stability approach, the proposed controller is proven to be robust in face of variable external load and the aforementioned uncertainties.

Workspace Analysis and Dynamics Simulation of Manipulator based on MATLAB

Workspace analysis and joint moment calculation are the main contents of manipulator’s design and calculation. According to the joint layout characteristics of the light cooperative manipulator, the workspace of the manipulator is divided into two parts: the arm workspace and the wrist workspace. The geometric method and Monte Carlo method are used to calculate the workspace of the manipulator and to guide the determination of the structural parameters of the manipulator. Based on Simulink/Simmechanics library, the dynamics model of six-axis serial manipulator is built and the joint rotational moment is calculated. The simulation results are in good agreement with the results of Newton Euler method. The simulation model can be used to replace Newton Euler method in the design process of the manipulator to improve the design efficiency.

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.

Cooperative Manipulation for a Mobile Dual-Arm Robot Using Sequences of Dynamic Movement Primitives

In order to extend promising robot applications in human daily lives, robots need to perform dextrous manipulation tasks, particularly for a mobile dual-arm robot. This paper propose a novel control strategy, which consists of a first trial process and a learning phase, to enable a mobile dual-arm robot to complete a grasp-and-place task which can be decomposed into movement sequences, such as reaching, grasping, and cooperative manipulation of a grasped object. Under the guidance of vision system, the robot with physical constraints successfully fulfills the task by tracking trajectories generated by redundancy resolution online using a neural-dynamic optimization. Then a reinforcement learning algorithm called the policy improvement with path integrals for sequences of dynamic movement primitives is applied to learn and adjust the recorded trajectories. Experimental results of the developed mobile dual-arm robot verified that the proposed strategy is able to successfully and optimally complete a grasp-and-place task.

Adaptive Tracking Control of Cooperative Robot Manipulators With Markovian Switched Couplings

Many cooperative robotic systems have not only modeling heterogeneity and uncertainty but also switched couplings, causing control difficulties. Here, we develop a neural network adaptive control framework for cooperative robot manipulators with unknown Euler–Lagrange dynamics and Markovian switched couplings. Second-order Markovian switching networks are used for modeling such cooperative robotic systems, which admit a hybrid neural network control with a desired tracking performance. The hybrid neural network control scheme contains a distributed adaptive controller and a hybrid adaptation law, enabling learning in the closed-loop system. The position and velocity tracking errors are shown to be practically uniformly exponentially stable in the mean-square sense, respectively, guaranteeing the second-order practical tracking. The results also suggest that the neural weight evolves with practical convergence to the ideal, showing the effect of network structure on the adaptation capacity.

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.

Development of a virtual reality simulator for a strategy for coordinating cooperative manipulator robots using cloud computing

This article deals with the development of a simulator that recreates, in virtual reality, a team of Selectively Compliance Assembly Robot Arms (SCARA). This team works cooperatively to fulfill the task of stacking rectangular objects coordinated through a strategy that includes a cloud server responsible for communication between the robots.The execution of the task is based on a leader/follower configuration. In this configuration, the leader performs a computed trajectory constantly reporting its position to the remote server. The remote server, in turn, sends this information back to the follower so this can follow the leader.The application combines MatLab® and Java. The latter is specifically used for communication routines, since its versatility makes it easy to be incorporated into any type of machine.This paper seeks to demonstrate the advantages of incorporating cloud resources into a multi-robot system and how its performance can be tested by means of the application developed.

Collision detection and avoidance method for two cooperative robot manipulators

The paper examines a collisions detection and avoidance problem for two cooperative robot manipulators. The proposed collision detection method is based on the division of workspace into small discrete volume spaces. Each of the discrete volumes have the status “occupied” or “free”. The control system generates restriction signals when the motion along the calculated path will bring the manipulator into occupied volume spaces. A step-by-step algorithm of the collision detection system is given.

Positioning Control of a Human-Machine Cooperative Grafting Manipulator for Unstructured Environments

HighlightsPositioning of a human-machine cooperative grafting manipulator for high-crown grafting of fruit trees is analyzed.PID control based on feedforward compensation of a dynamic model can realize high-precision position control of the braking process in unstructured agricultural environments.A manipulator based on the proposed control method can realize accurate position control and time-varying operating forces and can provide energy savings to meet the requirements of field operations.Abstract. Crown grafting of fruit trees has the disadvantages of high labor intensity and reduced graft survival. Therefore, a human-machine cooperative manipulator that relies on passive joint braking was designed to realize position control. The manipulator can replace manual operations to solve the problem of different positions in the grafting process and provide positioning and force support for canopy grafting. This study determined that the working space of the manipulator can cover the canopy area of fruit trees. Dynamic equations were established for motion simulation and feedforward compensation control of the manipulator. According to the dynamic model, the joint braking process was simulated. The simulation results showed that the joint braking torque needs to be dynamically controlled to ensure positioning accuracy of the manipulator. A process of passive joint braking was designed based on the proposed ideal braking curve. By comparing the position control accuracy of independent proportional integral derivative (PID) control, dynamic model feedforward compensation control, and PID control based on feedforward compensation of the dynamic model in simulations, it was determined that PID control based on feedforward compensation of the dynamic model was suitable for application in the braking torque control system. Finally, prototype tests showed that PID control based on feedforward compensation of the dynamic model can realize high-precision joint braking and position control of the manipulator. The positioning error was less than 5%, and the maximum vibration acceleration amplitude was reduced by 26.7% to 68.5%. The control system of the manipulator, using PID control based on feedforward compensation of the dynamic model, can provide adaptability for unstructured environments and reduce power consumption for application in field operations. Keywords: Controls, Dynamics, Grafting, Positioning, Simulation models, Unstructured agricultural environment.

Manipulability Optimization for Coordinated Motion Control of Multi-arm Space Robots

Abstract By maximizing manipulability, the coordination of multi-arm can be enhanced. In this paper, a method to optimize the manipulability index of cooperative manipulation for a free-floating multi-arm space robot is proposed. Firstly, the manipulability optimization is formulated as a nonlinear optimize problem at position level which is hard to solve online. By redefining constraint equation and manipulability index, it is transformed to a constrained quadratic program problem at velocity level incorporating joint velocity physical limits, which generates joint velocity commands to control the multi-arm to complete predefined tasks. Owing to dynamic coupling effects and closed chain constraints formed by cooperative manipulation, the manipulability index is more complex than that of fixed-base or mobile-base manipulators. Hence, the gradient of the index is approximated by numerical algorithms. Simulations based on a dual-arm space robot model are conducted and the results prove that the proposed method is efficient to optimize the manipulability index.

Synchronized mobile manipulators for kinematic cooperative tasks: control design and analysis

Abstract In this paper we present a synchronization feedback control scheme for kinematic cooperative mobile manipulator robots performing a global task of manipulation and transportation, where an object is taken to a desired 6D pose. We define the motion of the object which is translated to each end-effector as desired coordinates, that trajectory is generated on-line and is a function of every robot in the scene. The group of robots follow the object’s motion where the control of each robot is also a function of every other robot, meanwhile they are constrained to a common object. This leads to a complicated interaction scheme, so we thoroughly analyze the effects of the interactions that alter the behavior of each robot, and determine under what conditions those effects help to increase the performance of the proposed control scheme, such that the motions are coordinated to minimize the total energy and stabilize themselves as the common object reaches its objective. For this we performed a stability analysis and tested our control scheme through simulations.