Dual Robot Manipulator Research Articles

A Gesture-Based Natural Human–Robot Interaction Interface With Unrestricted Force Feedback

This article presents a novel gesture-based natural human–robot interaction interface, which integrates a markerless gesture tracking system and an unrestricted electromagnetic force feedback mechanism. In this proposed interface, a markerless gesture tracking system is developed to relate the motion of the operator’s hand to the robot manipulator so that the operator can naturally and friendly control the robot without any markers. More importantly, this interface uses a new unrestricted electromagnetic force feedback mechanism to avoid the friction, hysteresis, and other nonlinear influence in the conventional actuation dynamics. The interface makes the operator obtain the effective force immersion of the robot. Therefore, the proposed interface can provide the promising operation accuracy for the operator. To effectively regulate the electric currents of coils and provide accurate force feedback, the broad learning system (BLS) is introduced in this unrestricted electromagnetic force feedback mechanism. In addition, two interval Kalman filters (IKFs) are applied to estimate the position and orientation of the operator’s hand, respectively, improving the measurement accuracy of the proposed interface. Experimental results show that the proposed interface is suitable for high-precision human–robot interactive tasks and enables the operator to focus on the tasks and operate dual robot manipulator, which provides natural and efficient human–robot interaction.

Mutual-Collision-Avoidance Scheme Synthesized by Neural Networks for Dual Redundant Robot Manipulators Executing Cooperative Tasks.

Collision between dual robot manipulators during working process will lead to task failure and even robot damage. To avoid mutual collision of dual robot manipulators while doing collaboration tasks, a novel recurrent neural network (RNN)-based mutual-collision-avoidance (MCA) scheme for solving the motion planning problem of dual manipulators is proposed and exploited. Because of the high accuracy and low computation complexity, the linear variational inequality-based primal-dual neural network is used to solve the proposed scheme. The proposed scheme is applied to the collaboration trajectory tracking and cup-stacking tasks, and shows its effectiveness for avoiding collision between the dual robot manipulators. Through network iteration and online learning, the dual robot manipulators will learn the ability of MCA. Moreover, a line-segment-based distance measure algorithm is proposed to calculate the minimum distance between the dual manipulators. If the computed minimum distance is less than the first safe-related distance threshold, a speed brake operation is executed and guarantees that the robot cannot exceed the second safe-related distance threshold. Furthermore, the proposed MCA strategy is formulated as a standard quadratic programming problem, which is further solved by an RNN. Computer simulations and a real dual robot experiment further verify the effectiveness, accuracy, and physical realizability of the RNN-based MCA scheme when manipulators cooperatively execute the end-effector tasks.

Tricriteria Optimization-Coordination Motion of Dual-Redundant-Robot Manipulators for Complex Path Planning

To remedy the discontinuity phenomenon existing in the infinity-norm velocity minimization (INVM) scheme, prevent the occurrence of high joint velocity, and decrease the joint-angle drift of redundant robot manipulators, a new type of tricriteria optimization-coordination-motion (TCOCM) scheme is proposed and investigated for dual-redundant-robot manipulators to track complex end-effector paths. Besides, the proposed scheme considers joint physical constraints (i.e., joint-angle limits and joint-velocity limits) and guarantees the joint velocity to approach zero at the end of tasks. Such a new-type TCOCM scheme combines the minimum velocity norm (MVN), repetitive motion planning (RMP), and INVM solutions via two weighting factor, and thus is termed MVN-RMP-INVM-TCOCM scheme. The proposed scheme consists of two subschemes, i.e., subscheme for the left robot manipulator and subscheme for the right robot manipulator. To control the dual arms simultaneously and collaboratively, two subschemes are reformulated as two general quadratic programming (QP) problems and further unified into one QP formulation. The unified QP problem is then solved by a simplified linear-variational-inequalities-based primal–dual neural network solver. Computer simulation results based on dual PUMA560 robot manipulators are illustrated to substantiate the advantage, efficacy, and applicability of the proposed MVN-RMP-INVM-TCOCM scheme to resolve the redundancy of dual-robot manipulators.

A Velocity-Level Bi-Criteria Optimization Scheme for Coordinated Path Tracking of Dual Robot Manipulators Using Recurrent Neural Network.

A dual-robot system is a robotic device composed of two robot arms. To eliminate the joint-angle drift and prevent the occurrence of high joint velocity, a velocity-level bi-criteria optimization scheme, which includes two criteria (i.e., the minimum velocity norm and the repetitive motion), is proposed and investigated for coordinated path tracking of dual robot manipulators. Specifically, to realize the coordinated path tracking of dual robot manipulators, two subschemes are first presented for the left and right robot manipulators. After that, such two subschemes are reformulated as two general quadratic programs (QPs), which can be formulated as one unified QP. A recurrent neural network (RNN) is thus presented to solve effectively the unified QP problem. At last, computer simulation results based on a dual three-link planar manipulator further validate the feasibility and the efficacy of the velocity-level optimization scheme for coordinated path tracking using the recurrent neural network.

A Markerless Human–Robot Interface Using Particle Filter and Kalman Filter for Dual Robots

A more natural means of communicating complex movements to a robot manipulator is where the manipulator copies the movements of human hands. This paper presents a markerless human-robot interface that incorporates Kalman filters (KFs) and particle filters (PFs) to track the posture of human hands. This method allows one operator to control dual robot manipulators by using his/her double hands without any contact devices or markers. The algorithm employs Leap Motion to determine the orientation and the position of the human hands. Although the position and the orientation of the hands can be obtained from the sensor, the measurement errors increase over time due to the noise of the devices and the tracking error. The PFs and KFs are used to estimate the position and the orientation of the human hand. Due to the limitations of the perception and the motor, a human operator cannot accomplish high-precision manipulation without any assistance. An adaptive multispace transformation is employed to assist the operator to improve the accuracy and reliability in determining the posture of the manipulator. The greatest advantage of this method is that the posture of the human hands can be estimated accurately and steadily without any assistant markers. The human-manipulator interface system was experimentally verified in a laboratory, and the results indicate that such an interface can successfully control dual robot manipulators even if the operator is not an expert.

Human-Manipulator Interface Using Hybrid Sensors via CMAC for Dual Robots

This paper presents a novel method that allows a human operator to communicate his motion to the dual robot manipulators by performing his two double hand-arms movements, which would naturally carry out an object manipulation task. The proposed method uses hybrid sensors to obtain the position and orientation of the human hands. Although the position and the orientation of the human can be obtained from the sensors, the measurement errors increase over time due to the noise of the devices and the tracking error. A cerebellar model articulation controller (CMAC) is used to estimate the position and orientation of the human hand. Due to the limitations of the perceptive and the motor, human operator can not accomplish the high precision manipulation without any assistant. An adaptive multi-space transformation (AMT) is employed to assist the operator to improve the accuracy and reliability in determining the pose of the manipulator. With making full use of the human hand-arms motion, the operator would feel kind of immersive. Using this human-robot interface, the object manipulation task done in collaboration by dual robots could be carried out flexibly through preferring the double hand-arms motion by one operator.

Optimization of the Time of Task Scheduling for Dual Manipulators using a Modified Electromagnetism-Like Algorithm and Genetic Algorithm

A method based on a modified electromagnetism-like with two-direction local search algorithm (MEMTDLS) and genetic algorithm (GA) is proposed to determine the optimal time of task scheduling for dual-robot manipulators. A GA is utilized to calculate the near-optimal task scheduling for both robots, and the MEMTDLS is recommended as a suitable alternative in obtaining multiple solutions at each task point for both manipulators with minimal error. During the course of the tour, the robots move from point to point with a short cycle time, while ensuring that no collision occurs between the two manipulators themselves or between the dual manipulators and the static obstacles in the workspace. The movement and the configurations of the manipulators at the task points were illustrated using a simulator that was developed via Visual Basic .Net. The method is verified using two simulators that are used as examples for two identical four-link planar robots that work in the environment, with square-shaped obstacles cluttered at different locations.

Markerless human–robot interface for dual robot manipulators using Kinect sensor

Remote teleoperation of robot manipulators is often necessary in unstructured, dynamic, and dangerous environments. However, the existing mechanical and other contacting interfaces require unnatural, or hinder natural, human motions. At present, the contacting interfaces used in teleoperation for multiple robot manipulators often require multiple operators. Previous vision-based approaches have only been used in the remote teleoperation for one robot manipulator as well as require the special quantity of illumination and visual angle that limit the field of application. This paper presents a noncontacting Kinect-based method that allows a human operator to communicate his motions to the dual robot manipulators by performing double hand–arm movements that would naturally carry out an object manipulation task. This paper also proposes an innovative algorithm of over damping to solve the problem of error extracting and dithering due to the noncontact measure. By making full use of the human hand–arm motion, the operator would feel immersive. This human–robot interface allows the flexible implementation of the object manipulation task done in collaboration by dual robots through the double hand–arm motion by one operator.