Cooperative control of dual-arm robots in different human-robot collaborative tasks

2019 ◽  
Vol 40 (1) ◽  
pp. 95-104 ◽  
Author(s):  
Xinbo Yu ◽  
Shuang Zhang ◽  
Liang Sun ◽  
Yu Wang ◽  
Chengqian Xue ◽  
...  
Keyword(s):  
Cooperative Control ◽  
Force Feedback ◽  
Control Strategies ◽  
Experimental Results ◽  
Distant Object ◽  
Content Type ◽  
Human Arm ◽  
Dual Arm Robot ◽  
Dual Arm ◽  
Robot Platform

Purpose This paper aims to propose cooperative control strategies for dual-arm robots in different human–robot collaborative tasks in assembly processes. The authors set three different regions where robot performs different collaborative ways: “teleoperate” region, “co-carry” region and “assembly” region. Human holds the “master” arm of dual-arm robot to operate the other “follower” arm by our proposed controller in “teleoperation” region. Limited by the human arm length, “follower” arm is teleoperated by human to carry the distant object. In the “co-carry” region, “master” arm and “follower” arm cooperatively carry the object to the region close to the human. In “assembly” region, “follower” arm is used for fixing the object and “master” arm coupled with human is used for assembly. Design/methodology/approach A human moving target estimated method is proposed for decreasing efforts for human to move “master” arm, radial basis functions neural networks are used to compensate for uncertainties in dynamics of both arms. Force feedback is designed in “master” arm controller for human to perceive the movement of “follower” arm. Experimental results on Baxter robot platform show the effectiveness of this proposed method. Findings Experimental results on Baxter robot platform show the effectiveness of our proposed methods. Different human-robot collaborative tasks in assembly processes are performed successfully under our cooperative control strategies for dual-arm robots. Originality/value In this paper, cooperative control strategies for dual-arm robots have been proposed in different human–robot collaborative tasks in assembly processes. Three different regions where robot performs different collaborative ways are set: “teleoperation” region, “co-carry” region and “assembly” region.

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

2018 ◽  
Vol 38 (5) ◽  
pp. 678-688 ◽  
Author(s):  
Fuhai Zhang ◽  
Jiadi Qu ◽  
He Liu ◽  
Yili Fu
Keyword(s):  
Real Time ◽  
Internal Force ◽  
Content Type ◽  
Force Coordination ◽  
Relative Pose ◽  
Dual Arm Robot ◽  
End Effectors ◽  
Damping Model ◽  
Dual Arm ◽  
Robot Platform

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.

A gesture-based telemanipulation control for a robotic arm with biofeedback-based grasp

2017 ◽  
Vol 44 (5) ◽  
pp. 575-587 ◽  
Author(s):  
Yassine Bouteraa ◽  
Ismail Ben Abdallah
Keyword(s):  
Real Time ◽  
Force Feedback ◽  
Emg Biofeedback ◽  
Control Technique ◽  
Robotic Arm ◽  
Effective Control ◽  
Content Type ◽  
Human Arm ◽  
Gesture Control ◽  
Posture Recognition

Purpose The idea is to exploit the natural stability and performance of the human arm during movement, execution and manipulation. The purpose of this paper is to remotely control a handling robot with a low cost but effective solution. Design/methodology/approach The developed approach is based on three different techniques to be able to ensure movement and pattern recognition of the operator’s arm as well as an effective control of the object manipulation task. In the first, the methodology works on the kinect-based gesture recognition of the operator’s arm. However, using only the vision-based approach for hand posture recognition cannot be the suitable solution mainly when the hand is occluded in such situations. The proposed approach supports the vision-based system by an electromyography (EMG)-based biofeedback system for posture recognition. Moreover, the novel approach appends to the vision system-based gesture control and the EMG-based posture recognition a force feedback to inform operator of the real grasping state. Findings The main finding is to have a robust method able to gesture-based control a robot manipulator during movement, manipulation and grasp. The proposed approach uses a real-time gesture control technique based on a kinect camera that can provide the exact position of each joint of the operator’s arm. The developed solution integrates also an EMG biofeedback and a force feedback in its control loop. In addition, the authors propose a high-friendly human-machine-interface (HMI) which allows user to control in real time a robotic arm. Robust trajectory tracking challenge has been solved by the implementation of the sliding mode controller. A fuzzy logic controller has been implemented to manage the grasping task based on the EMG signal. Experimental results have shown a high efficiency of the proposed approach. Research limitations/implications There are some constraints when applying the proposed method, such as the sensibility of the desired trajectory generated by the human arm even in case of random and unwanted movements. This can damage the manipulated object during the teleoperation process. In this case, such operator skills are highly required. Practical implications The developed control approach can be used in all applications, which require real-time human robot cooperation. Originality/value The main advantage of the developed approach is that it benefits at the same time of three various techniques: EMG biofeedback, vision-based system and haptic feedback. In such situation, using only vision-based approaches mainly for the hand postures recognition is not effective. Therefore, the recognition should be based on the biofeedback naturally generated by the muscles responsible of each posture. Moreover, the use of force sensor in closed-loop control scheme without operator intervention is ineffective in the special cases in which the manipulated objects vary in a wide range with different metallic characteristics. Therefore, the use of human-in-the-loop technique can imitate the natural human postures in the grasping task.

Skillful Operating of Working Plate to Control Ball Rolling Motion With a Dual Arm Robot

2013 ◽  
Author(s):  
Shun Kinoshita ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
Wei Wu
Keyword(s):  
Cooperative Control ◽  
Present Report ◽  
Past Research ◽  
Circular Path ◽  
Rotary Axes ◽  
Ball Rolling ◽  
Novel Method ◽  
Rotational Motions ◽  
Dual Arm Robot ◽  
Dual Arm

Industrial dual-arm robots have been gaining attention as novel tools in the field of new automation. Our past research has focused on using them flexibly to control both the linear and rotational motions of a working plate. However, it has been difficult to measure the synchronous accuracy of two rotary axes without a high-accuracy gyro sensor. We therefore developed a novel method to measure the synchronous accuracy of the two rotary axes of a working plate with a ball, in which the ball is kept rolling around a circular path by dual-arm cooperative control. In the present report, in order to widen the range of application, we tried to keep the ball rolling around a rhomboid path, which is one of the polygonal paths used on a working plate by dual-arm cooperative control. It could be seen that there is some possibility of generating an equal speed diamond motion by inputting wave as the odd power of a trigonometric function and considered a deceleration angle with the robot that we handled.

Adaptive neural network visual servoing of dual-arm robot for cyclic motion

2017 ◽  
Vol 44 (2) ◽  
pp. 210-221 ◽  
Author(s):  
Jiadi Qu ◽  
Fuhai Zhang ◽  
Yili Fu ◽  
Guozhi Li ◽  
Shuxiang Guo
Keyword(s):  
Neural Network ◽  
Visual Servoing ◽  
Joint Angle ◽  
Pi Controller ◽  
Steering Wheel ◽  
Adaptive Neural Network ◽  
Content Type ◽  
Cyclic Motion ◽  
Dual Arm Robot ◽  
Dual Arm

Purpose The purpose of this paper is to develop a vision-based dual-arm cyclic motion method, focusing on solving the problems of an uncertain grasp position of the object and the dual-arm joint-angle-drift phenomenon. Design/methodology/approach A novel cascade control structure is proposed which associates an adaptive neural network with kinematics redundancy optimization. A radial basis function (RBF) neural network in conjunction with a conventional proportional–integral (PI) controller is applied to compensate for the uncertainty of the image Jacobian matrix which includes the estimated grasp position. To avoid the joint-angle-drift phenomenon, a dual neural network (DNN) solver in conjunction with a PI controller and dual-arm-coordinated constraints is applied to optimize the closed-chain kinematics redundancy. Findings The proposed method was implemented on an industrial robotic MOTOMAN with two 7-degrees of freedom robotic arms. Two experiments of carrying a tray repeatedly and turning a steering wheel were carried out, and the results indicate that the closed-trajectories tracking is achieved successfully both in the image plane and the joint spaces with the uncertain grasp position, which validates the accuracy and realizability of the proposed PI-RBF-DNN control strategy. Originality/value The adaptive neural network visual servoing method is applied to the dual-arm cyclic motion with the uncertain grasp position of the object. The proposed method enhances the environmental adaptability of a dual-arm robot in a practical manipulation task.

Peg-in-hole assembly based on master-slave coordination for a compliant dual-arm robot

2020 ◽  
Vol 40 (2) ◽  
pp. 189-198
Author(s):  
Yanjiang Huang ◽  
Yanglong Zheng ◽  
Nianfeng Wang ◽  
Jun Ota ◽  
Xianmin Zhang
Keyword(s):  
Design Methodology ◽  
Position Control ◽  
Control Model ◽  
Torque Control ◽  
Human Beings ◽  
Content Type ◽  
Different Shapes ◽  
Dual Arm Robot ◽  
Assembly Tasks ◽  
Dual Arm

Purpose The paper aims to propose an assembly scheme based on master–slave coordination for a compliant dual-arm robot to complete a peg-in-hole assembly task. Design/methodology/approach The proposed assembly scheme is inspired by the coordinated behaviors of human beings in the assembly process. The left arm and right arm of the robot are controlled to move alternately. The fixed arm and the moving arm are distinguished as the slave arm and the master arm, respectively. The position control model is used at the uncontacted stage, and the torque control model is used at the contacted stage. Findings The proposed assembly scheme is evaluated through peg-in-hole assembly experiments with different shapes of assembly piece. The round, triangle and square assembly piece with 0.5 mm maximum clearance between the peg and the hole can be assembled successfully based on the proposed method. Furthermore, three assembly strategies are investigated and compared in the peg-in-hole assembly experiments with different shapes of assembly piece. Originality/value The contribution of this study is that the authors propose an assembly scheme for a compliant dual-arm robot to overcome the low positioning accuracy and complete the peg-in-hole assembly tasks with different shapes parts.

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