Dual-arm Coordination Research Articles

Nanorobot-Based Direct Implantation of Flexible Neural Electrode for BCI.

Brain-Computer Interface (BCI) has gained remarkable prominence in biomedical community. While BCI holds vast potential across diverse domains, the implantation of neural electrodes poses multifaceted challenges to fully explore the power of BCI. Conventional rigid electrodes face the problem of foreign body reaction induced by mechanical mismatch to biological tissue, while flexible electrodes, though more preferential, lack controllability during implantation. Researchers have explored various strategies, from assistive shuttle to biodegradable coatings, to strike a balance between implantation rigidity and post-implantation flexibility. Yet, these approaches may introduce complications, including immune response, inflammations, and raising intracranial pressure. To this end, this paper proposes a novel nanorobot-based technique for direct implantation of flexible neural electrodes, leveraging the high controllability and repeatability of robotics to enhance the implantation quality. This approach features a dual-arm nanorobotic system equipped with stereo microscope, by which a flexible electrode is first visually aligned to the target neural tissue to establish contact and thereafter implanted into brain with well controlled insertion direction and depth. The key innovation is, through dual-arm coordination, the flexible electrode maintains straight along the implantation direction. With this approach, we implanted CNTf electrodes into cerebral cortex of mouse, and captured standard spiking neural signals.

Dual arm coordination of redundant space manipulators mounted on a spacecraft

AbstractThe paper addresses a significant challenge in on-orbit robotics servicing and assembly, which is to overcome the saturation setback of force/torque on robot joint and spacecraft actuators during the post-capture stage while controlling a target spacecraft with uncontrolled large angular and linear momentums. The authors propose a novel solution based on two robust and efficient control algorithms: Optimal Control Allocation (OCA) and Non-linear Model Predictive Control (NMPC). Both algorithms aim to minimize joint torques, spacecraft actuator moments, contact forces, and moments of a compound redundant system that includes a common payload (target spacecraft) grasped by dual n-degree space robotics manipulators mounted on a chaser spacecraft. The OCA algorithm minimizes a quadratic cost function using only the current states and the system dynamics, but the NMPC also considers the future state estimates and the control inputs over a specified prediction horizon. It is computationally more involved but provides superior results in reducing joint torques. The literature to date in application of MPC to robotics mainly focuses on linear models but the dual arm coordination is highly non-linear and there is no MPC application on dual arm coordination. The proposed discretized technique offers exact realizations (of a non-linear model) with elegance and simplicity and yet considers the full non-linear model of the dual arm coordinating system. It is computationally very efficient. The computer simulation results show that the proposed algorithms work efficiently, and the minimum torques, contact forces, and moments are realized. The developed algorithm also is very efficient in tracking problems.

A humanoid robot teleoperation approach based on waist–arm coordination

PurposeIn the current teleoperation system of humanoid robots, the control between arms and the control between the waist and arms are individual and lack coordinated motion. This paper aims to solve the above problem and proposes a teleoperation control approach for a humanoid robot based on waist–arm coordination (WAC).Design/methodology/approachThe teleoperation approach based on WAC comprises dual-arm coordination (DAC) and WAC. The DAC method realizes the coordinated motion of both arms through one hand by establishing a mapping relationship between a single hand controller and the manipulated object; the WAC method realizes the coordinated motion of both arms and waist by calculating the inverse kinematic input of robotic arms based on the desired velocity of the waist and the end of both arms. An integrated teleoperation control framework provides interfaces for the above methods, and users can switch control modes online to adapt to different tasks.FindingsAfter conducting experiments on the dual-arm humanoid robot through the teleoperation control framework, it was found that the DAC method can save 27.2% of the operation time and reduce 99.9% of the posture change of the manipulated object compared with the commonly used individual control. The WAC method can accomplish a task that cannot be done by individual control. The experiments proved the improvement of both methods in terms of operation efficiency, operation stability and operation capability compared with individual control.Originality/valueThe DAC method better maintains the constraints of both arms and the manipulated object. The WAC method better maintains the constraints of the manipulated object itself. Meanwhile, the teleoperation framework integrates the proposed methods and enriches the teleoperation modes and control means.

Study on Musical Saw Bowing Motion with an Industrial Cooperative Humanoid Robot

The objective of this study was to automate the dual-arm coordination of an industrial humanoid robot when using a musical saw as a percussion instrument to produce a target sound. Especially, this report focuses on a humanoid robot with a characteristic of the real-time control to replace the traditional teaching and playback motion control. Meanwhile, it is assumed that the humanoid robot works in the environment that is near to a human being. To adapt in such situation, we aim to achieve the complex work using an advanced tool with its robot, as we perform a skillful motion. In the present report, we examine the performance of a musical saw using a bow because it is a kind of complex work using an unknown and advanced tool. Moreover, both the control method of the arm of the robot and the performance of the bow are discussed to construct a sound feedback control approach. As a result, it is demonstrated that the proposed approach has potential for both industrial and recreational applications of humanoid robots in human occupied environments.

Dual Arm Coordination with Coordination Diagram Based on Teleoperation Demonstration

Efficient and collision-free coordination of two robot arms is increasingly needed in various service-oriented robotic applications. This paper proposes a dual arm coordination algorithm to improve the efficiency of coordination by considering both robot’s actions and operating sequences of the tasks that need to use two arms to complete complex operations. Teleoperation demonstration is first performed to obtain the robot’s human-like motion trajectories, so as to reduce the probability of the collisions between the two arms. The coordination diagram in time domain is then designed to more clearly represent the situations of trajectory collisions and find the collision-free coordination action law. A Coordination Pair Generator (CPG) is designed to reorganize the operating sequences according to the characteristics of input trajectories and the action coordination. The effectiveness and efficiency of our method are verified on the simulation and physical experiments which execute the drug sorting task in nursing homes, respectively, on the ABB YuMi robot model and self-developed robot system. According to the experiment results, the operation time has been reduced by 9% and the collision area has been reduced by 7.5%.

An algorithm for trajectory optimization of dual-arm coordination based on arm angle constraints

In this paper, the motion planning of a dual-arm robot with kinematic constraints is studied based on arm-angle constraints. When a dual-arm robot moves a common object, a closed kinematic chain is formed between the dual-arm and the object. The standard sampling-based trajectory planning algorithm solves the problem with closed-chain constraint, but this causes other problems; the running time increases, the success rate decreases, and the motion trajectory of the end effector is not smooth resulting in large output error. Therefore, this paper proposes a dual-arm coordinated trajectory optimization algorithm based on arm-angle constraints. Firstly, the kinematics of the dual-arm robot is modeled and analyzed, and the definition of the arm-angle in a seven-axis robot is proposed, the workspace of the dual-arm coordinated operation is considered to constrain it, the kinematics equation combined with the single/multi-objective optimization algorithm is used to optimize the end output error, and the joint trajectory is parameterized. This paper solves the problems that the slave arm lags behind the main arm, the motion trajectory of the dual-arm is not smooth, and the dual-arm are squeezed due to internal force during the coordinated movement of the dual-arm. The trajectory optimization improves the synchronization of the coordinated operation of the dual-arm, reduces the output error of the velocity and acceleration at the end of the dual-arm. After limiting the arm-angle, dual-arm manipulation is anthropomorphic the robot does not produce distorted arm configurations.

Applications of Light-Weight Wearable Devices to Online Programming of Industrial Dual-Arm Robots

Dexterous manipulation of dual-arm robots in unstructured environments is very useful. Programming a dual-arm industrial robot to efficiently complete dexterous tasks, however, is especially challenging due to the complexity of its inverse kinematics, motion planning, dual-arm coordination with self-collision avoidance, and so on. This paper presents a systematic solution to accurately manipulate a dual-arm industrial robot on-site via light-weight wearable devices. In the developed system, the human operator directly drives the robot through the human arms motions tracked by the combination of inertial measurement units and handheld joystick controllers. A proper motion retargeting method with self-collision avoidance is used to enable the user to manipulate the robot directly through intuitive arm motions within a comfortable range and ensure the task manipulation with safety in unstructured environments. The developed system has been tested with various tasks, such as the manipulation of objects of different shapes, dexterous turn-over, and dual-arm coordination. Compared with the existing telerobotic systems, the developed system with simultaneous 14 degree-of-freedom teleoperation directly driven by light-weight wearable devices is able to handle more dexterous and accurate manipulation tasks with the capability of fast deployment and self-collision awareness. Such a solution could pave the way for online dual-arm robot programming on efficient manipulation skills transfer in the future.

A Multi-Priority Control of Asymmetric Coordination for Redundant Dual-Arm Robot

The paper develops a multi-priority control method of asymmetric coordination for a redundant dual-arm robot. A novel dual-arm coordination impedance is introduced to the multi-priority control, and then the performance of the object tracking and the redundant joints is improved by the regulation of the relative Cartesian errors between two arms. The control of asymmetric coordination is divided into the main task and the secondary task. The control of the main task can regulate the two end-effectors’ errors and the relative errors by building the model of spatial parallel spring and damping (SPSDM), which establishes the dual-arm coordination impedance relation in Cartesian space. The control of the secondary task optimizes the performance of the redundant joint impedance and joint limit avoidance in null space. Finally, a typical asymmetric coordination experiment of peg-in-hole is carried out to verify the validity and feasibility of the proposed method. The results indicate that the proposed dual-arm coordination impedance can regulate the relative tracking errors between two objects directly, and in the context of the external impact force applied to the two end-effectors, the peg-in-hole dual-arm task can be achieved successfully.

Research on dual-arm coordination motion control strategy for power cable mobile robot

Regarding the typical operation tasks in high-voltage transmission line environment, in order to effectively eliminate or reduce the disadvantageous effects on the robot operation reliability caused by the close chain internal forces. According to the motion status of manipulator operation, the dual-arm coordination control of the mobile operation robot can be divided into three different modes, which are dual-arm independent operation mode, dual-arm fully constrained mode and dual-arm partially constrained mode. The dynamic model between dual-arm and operation objects for the three different models are established, respectively. The manipulator position and force are set as the control targets, a unified dual-arm coordination controller is designed for the three operation modes. Through the mutual compensation of force error and position error, the close chain internal force can be dynamically allocated in each joints, so that the robot joints force can be balanced during the operation, and the internal force in the close chain can be minimized during the operation process so as to ensure the operation reliability and safety. Finally, the validity and engineering practicability is verified by MATLAB simulation experiment and 220 kV living damper replacement and drainage plate bolt tightening operation experiment, respectively.

Human-like coordination motion learning for a redundant dual-arm robot

Abstract The motion of a redundant dual-arm robot is subject to dual-arm coordination constraints when performing a coordination task. However, these constraints are usually fixed. To improve the ability of dual arm robots to interact effectively with human beings, it is necessary to obtain the variable dual-arm coordination constraints from the observation of human arm motion. This paper developed a novel redundant dual-arm robot motion learning method based on human-arm coordination characteristics. It can realize the human-like coordination motion of a dual-arm robot in both Cartesian space and joint space. The proposed method was implemented on a real redundant dual-arm robot platform. Experiments involving two tasks, carrying and pouring, were carried out, and the results indicate that the robot can successfully reproduce the demonstrated human-arm motion tasks, and the dual-arm robot has the characteristics of coordinated human-like motion, making robotic dual-arm manipulations more smooth and natural.

Kinematics analysis and self - collision detection of Truss type multi-robot cooperative welding platform

A collaborative welding platform consists of a XYZ-mobile truss, a 3R (revolute joint) clamping robot and a dual-arm welding robot. Based on Denavit-Hartenberg (D-H) method, the analytical inverse kinematics solution of the platform is obtained. Dual-arm coordination is a technical difficulty in robot control field. Focusing on self-collision problem during dual-arm robot motion planning, this paper proposes a new obstacle avoidance algorithm based on Rapidly Exploring Random Tree (RRT) algorithm. Setting searching area founded on artificial potential field (APF) algorithm and adopting note-pruning strategy achieves high searching efficiency. Simulation results verify the correctness of kinematics analysis and self-collision detection algorithm.

Symmetrical adaptive variable admittance control for position/force tracking of dual-arm cooperative manipulators with unknown trajectory deviations

Abstract In this paper, a new symmetrical adaptive variable admittance control is proposed for position/force tracking of dual-arm cooperative manipulators. Thanks to this control approach, the dual-arm cooperative manipulator is able to track a dynamic desired force and compensate for the unknown trajectory deviations, resulted from in terms of external disturbances and calibration errors. An object-oriented motion planning approach for dual-arm cooperative manipulators is adopted firstly, the motion of the single arm in the system is computed via closed-chain constrains. The trajectory deviations from external disturbance and calibration errors are considered, which will lead existing unknown external forces and internal forces to the closed-chain system. In particular, the modeling of closed-chain which including dual-arm and object is analyzed. According to the principle of ”nonsqueezing” pseudoinverse, the forces acting on the center of the object are decomposed into external and internal forces. Furthermore, the external and internal forces are transformed to the tip of the object by load distribution strategy. To track the desired position and forces, a symmetrical adaptive variable admittance control for dual-arm coordination is achieved for the first time by adjusting the admittance parameters on-line based on the tracking error to compensate the unknown trajectory deviations. In the end, the developed control scheme is experimentally tested on a dual-arm setup composed of two ESTUN industrial manipulators carrying a common object. The simulations and experimental results strongly prove that the proposed approach can achieve good position/force tracking performance.

A pose/force symmetric coordination method for a redundant dual-arm robot

PurposeThis paper aims to develop a pose/force coordination method for a redundant dual-arm robot to achieve a symmetric coordination task.Design/methodology/approachA novel control strategy of dual-arm coordination is proposed that associates pose coordination with force coordination. The spatial in-parallel spring and damping model is built to regulate the relative pose error of two end-effectors in real time, and force coordination factor is introduced to realize the dynamic distribution of loadings to limit the object’s internal force in real time.FindingsThe proposed method was verified on a real dual-arm robot platform. The symmetric coordination task is performed and the experiment results show that a good behavior on the regulation of the relative pose errors between two arms to achieve the object’s trajectory tracking, and the distribution of the two end-effectors’ loadings to limit the object’s internal force.Originality/valueThe benefits of the proposed method are to improve the object’s tracking performance and avoid the object damage during the symmetric coordination task.

Asymmetric Bimanual Control of Dual-Arm Exoskeletons for Human-Cooperative Manipulations

In this paper, two upper limbs of an exoskeleton robot are operated within a constrained region of the operational space with unidentified intention of the human operator's motion as well as uncertain dynamics including physical limits. The new human-cooperative strategies are developed to detect the human subject's movement efforts in order to make the robot behavior flexible and adaptive. The motion intention extracted from the measurement of the subject's muscular effort in terms of the applied forces/torques can be represented to derive the reference trajectory of his/her limb using a viable impedance model. Then, adaptive online estimation for impedance parameters is employed to deal with the nonlinear and variable stiffness property of the limb model. In order for the robot to follow a specific impedance target, we integrate the motion intention estimation into a barrier Lyapunov function based adaptive impedance control. Experiments have been carried out to verify the effectiveness of the proposed dual-arm coordination control scheme, in terms of desired motion and force tracking.

Coordinated Resolved Motion Control of Dual-Arm Manipulators with Closed Chain

When applied to some tasks, such as payload handling, assembling, repairing and so on, the two arms of a humanoid robot will form a closed kinematic chain. It makes the motion planning and control for dual-arm coordination very complex and difficult. In this paper, we present three types of resolved motion control methods for a humanoid robot during coordinated manipulation. They are, respectively, position-level, velocity-level and acceleration-level resolved motion control methods. The desired pose, velocity and acceleration of each end-effector are then resolved according to the desired motion of the payload and the constraints on the closed-chain system without consideration of the internal force. Corresponding to the three cases above, the joint variables of each arm are then calculated using the inverse kinematic equations, at position-level, velocity-level or acceleration-level. Finally, a dynamic modelling and simulation platform is established based on ADAMS and Matlab software. The proposed methods are verified by typical cases. The simulation results show that the proposed control strategy can realize the dual-arm coordinated operation and the internal force of the closed chain during the operation is controlled in a reasonable range at the same time.

Adaptive neural control for dual-arm coordination of humanoid robot with unknown nonlinearities in output mechanism.

To achieve an excellent dual-arm coordination of the humanoid robot, it is essential to deal with the nonlinearities existing in the system dynamics. The literatures so far on the humanoid robot control have a common assumption that the problem of output hysteresis could be ignored. However, in the practical applications, the output hysteresis is widely spread; and its existing limits the motion/force performances of the robotic system. In this paper, an adaptive neural control scheme, which takes the unknown output hysteresis and computational efficiency into account, is presented and investigated. In the controller design, the prior knowledge of system dynamics is assumed to be unknown. The motion error is guaranteed to converge to a small neighborhood of the origin by Lyapunov's stability theory. Simultaneously, the internal force is kept bounded and its error can be made arbitrarily small.

Study on Dynamic Hybrid Position/Force Control of Two Coordinated Manipulators

A dynamic hybrid position/force control method is developed for the coordination of two manipulators of a dual-arm robot to cope with the case of dual-arm tightly cooperate a common rigid object in the presence of environmental constraint. Begin with the definition of a group of generalized motion and force vectors used for task description, and by synthesizing the object dynamics and manipulator dynamics, an object-oriented dynamic equation of the dual-arm rigid coordination system is first derived, where relationships between object motion, internal stress force, and environmental contact force are explicitly presented. Furthermore, this equation and that of single arm dynamics in Cartesian still remain the same form. Based on this definition and description, the dynamic hybrid position/force control scheme for dual-arm symmetric coordination is then designed, and the decomposition and parallel realization of the control algorithm is also discussed. Several experiments have been done on two coordinated PUMA562 robot manipulators, which show that the proposed method works effectively, where the object motion and internal/external force can be simultaneously controlled during cooperation.

Two-arm manipulation tasks with friction-assisted grasping

—The main objective of this paper is to study human dual-arm manipulation tasks and to develop a computational model that predicts the trajectories and force distribution for the coordination of two arms moving an object between two given positions and orientations in a horizontal plane. Our ultimate goal is to understand the dynamics of human dual-arm coordination in order to develop better robot control algorithms. We propose a computational model based on the hypothesis proposed by Uno et al. that suggests that human movements minimize the integral of the norm of the rate of change of actuator torques. We compare the experimental trajectories and force distributions with those obtained from the computational model. The observed trajectories show a significant degree of repeatability across trials and across subjects. We show that the computational model predicts the trajectories and the distribution of forces (torques) for a certain class of trajectories. However, the trajectories in the sagittal and frontal plane are characterized by asymmetric features that are hard to model using any integral cost function. Finally, we show that the computational model can be used to generate smooth trajectories and actuator forces for cooperating robots and discuss the advantages of such an approach to motion planning.

Dual arm coordination and control

A generalized master/slave technique and experimental results for coordinated control of two arms rigidly grasping an object is described in this paper. Usually master/slave refers to the case when one arm is controlled in position mode while the second arm is controlled in force mode. In generalized master/slave control, each arm at its tool frame has six degrees of freedom; each degree of freedom can be in either position or force control mode. An interactive program has been developed to allow a user the flexibility to select appropriate control modes for a given experiment. This interface also allows for control gain adjustments. This paper presents the results of several experiments performed on this system to demonstrate its capabilities such as transporting an object with or without induced internal forces and movement of a constrained object. The system is further developed to achieve a so-called shared control mode in which an operator species the free motion trajectory for a point on the object of manipulation via a joystick while the autonomous control system is used for coordination and control of the arms.