An Accurate On-Line Correction Strategy for Gravity Compensation Aiming at Teaching by Touch of Collaborative Robots

2018 ◽  
Author(s):  
Yunfei Dong ◽  
Tianyu Ren ◽  
Dan Wu ◽  
Ken Chen
Keyword(s):  
Variable Stiffness ◽  
Industrial Robots ◽  
Torque Control ◽  
Joint Torque ◽  
Robot Dynamics ◽  
Gravity Compensation ◽  
End Effector ◽  
Collaborative Robots ◽  
Working Space ◽  
On Line

Modern industrial robots are increasing rapidly towards collaborating and physically interacting with people on complex tasks, and away from working in isolated cages that are separated from people. Collaborative robots are usually developed to perform variable stiffness control, teaching by touch, collision detection, and so on. Torque control with accurate gravity compensation becomes necessary. When the method of torque-based impedance force-control is used to achieve teaching by touch, the gravity compensation is added to the joint torque loop directly. As a result, the gravity model should be very accurate, otherwise the robot will not stay still even if no any artificial external force is applied. Unfortunately, it is very difficult to model and identify the robot dynamics such accurately in the whole working space because of the presence of the robot cables, joint and link flexibility, cables or tube of end-effector, the end-effector itself and so on. There are always some regions where the robot will drift due to the error of gravity compensation. This paper is motivated by the gravity compensation problem of collaborative robots equipped with joint torque sensors, and attempts to propose an accurate on-line correction strategy for gravity compensation especially aiming at the application of teaching by touch. The developed method has been tested on a 7-DOF collaborative robot and shown good performance.

scholarly journals Unidirectional variable stiffness hydraulic actuator for load-carrying knee exoskeleton

2017 ◽  
Vol 14 (1) ◽  
pp. 172988141668695 ◽  
Author(s):  
Jun Zhu ◽  
Yu Wang ◽  
Jinlin Jiang ◽  
Bo Sun ◽  
Heng Cao
Keyword(s):  
High Efficiency ◽  
Experimental Testing ◽  
Variable Stiffness ◽  
Torque Control ◽  
Joint Torque ◽  
Hydraulic Actuator ◽  
Passive Behavior ◽  
Compliance Behavior ◽  
Load Carrying ◽  
Passive Compliance

This article presents the design and experimental testing of a unidirectional variable stiffness hydraulic actuator for load-carrying knee exoskeleton. The proposed actuator is designed for mimicking the high-efficiency passive behavior of biological knee and providing actively assistance in locomotion. The adjustable passive compliance of exoskeletal knee is achieved through a variable ratio lever mechanism with linear elastic element. A compact customized electrohydraulic system is also designed to accommodate application demands. Preliminary experimental results show the prototype has good performances in terms of stiffness regulation and joint torque control. The actuator is also implemented in an exoskeleton knee joint, resulting in anticipant human-like passive compliance behavior.

scholarly journals Proposal of a Monitoring System for Collaborative Robots to Predict Outages and to Assess Reliability Factors Exploiting Machine Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1621
Author(s):  
Khurshid Aliev ◽  
Dario Antonelli
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
High Speed ◽  
Collaborative Work ◽  
Separation Distance ◽  
Industrial Robots ◽  
Management Technique ◽  
Collaborative Robots ◽  
On Line

Industry standards pertaining to Human-Robot Collaboration (HRC) impose strict safety requirements to protect human operators from danger. When a robot is equipped with dangerous tools, moves at a high speed or carries heavy loads, the current safety legislation requires the continuous on-line monitoring of the robot’s speed and a suitable separation distance from human workers. The present paper proposes to make a virtue out of necessity by extending the scope of on-line monitoring to predicting failures and safe stops. This has been done by implementing a platform, based on open access tools and technologies, to monitor the parameters of a robot during the execution of collaborative tasks. An automatic machine learning (ML) tool on the edge of the network can help to perform the on-line predictions of possible outages of collaborative robots, especially as a consequence of human-robot interactions. By exploiting the on-line monitoring system, it is possible to increase the reliability of collaborative work, by eliminating any unplanned downtimes during execution of the tasks, by maximising trust in safe interactions and by increasing the robot’s lifetime. The proposed framework demonstrates a data management technique in industrial robots considered as a physical cyber-system. Using an assembly case study, the parameters of a robot have been collected and fed to an automatic ML model in order to identify the most significant reliability factors and to predict the necessity of safe stops of the robot. Moreover, the data acquired from the case study have been used to monitor the manipulator’ joints; to predict cobot autonomy and to provide predictive maintenance notifications and alerts to the end-users and vendors.

scholarly journals End-Effector Force and Joint Torque Estimation of a 7-DoF Robotic Manipulator Using Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2963
Author(s):  
Stanko Kružić ◽  
Josip Musić ◽  
Roman Kamnik ◽  
Vladan Papić
Keyword(s):  
Mean Squared Error ◽  
Force Sensor ◽  
Robotic Manipulator ◽  
Joint Torque ◽  
Estimation Methods ◽  
Robot Dynamics ◽  
Force Estimation ◽  
End Effector ◽  
Squared Error ◽  
Torque Estimation

When a mobile robotic manipulator interacts with other robots, people, or the environment in general, the end-effector forces need to be measured to assess if a task has been completed successfully. Traditionally used force or torque estimation methods are usually based on observers, which require knowledge of the robot dynamics. Contrary to this, our approach involves two methods based on deep neural networks: robot end-effector force estimation and joint torque estimation. These methods require no knowledge of robot dynamics and are computationally effective but require a force sensor under the robot base. Several different architectures were considered for the tasks, and the best ones were identified among those tested. First, the data for training the networks were obtained in simulation. The trained networks showed reasonably good performance, especially using the LSTM architecture (with a root mean squared error (RMSE) of 0.1533 N for end-effector force estimation and 0.5115 Nm for joint torque estimation). Afterward, data were collected on a real Franka Emika Panda robot and then used to train the same networks for joint torque estimation. The obtained results are slightly worse than in simulation (0.5115 Nm vs. 0.6189 Nm, according to the RMSE metric) but still reasonably good, showing the validity of the proposed approach.

Joint Torque Control of a Collaborative Robot Based on Active Disturbance Rejection With the Consideration of Actuator Delay

2017 ◽  
Author(s):  
Tianyu Ren ◽  
Yunfei Dong ◽  
Dan Wu ◽  
Guolei Wang ◽  
Ken Chen
Keyword(s):  
Disturbance Rejection ◽  
Controller Design ◽  
Robot Manipulator ◽  
Torque Control ◽  
Joint Torque ◽  
System Stability ◽  
Current Loop ◽  
Robot Dynamics ◽  
Harmonic Drive ◽  
Active Disturbance Rejection

The application of a robot manipulator to the task of parts assembling or collaboration with human workers requires compliant control and intrinsic safety. As a result, it is necessary to exert accurate torque on each joint of the robot through torque sensing and implementing closed-loop joint torque control. This torque servo system is required to track reference torque signals while operating under the influence of motor friction, flexibility of the harmonic drive, noise from the sensor, robot dynamics modelling error and other unknown certainties, resulting in large control efforts. This paper focuses on providing better compliance control for collaborative robots and proposes a joint torque controller design under development with active disturbance rejection concept. The controller is designed through a novel extended state observer to estimate and compensate for the unmodelled dynamics of the system, nonlinearly variable motor friction, and other uncertainties. Then, a simple proportional differential controller is designed to produce control law. In spite of the remarkable performance in dealing with the mechanical dynamics of the joint actuator, the original controller does not work well with the electrical factor of the joint actuator due to the limited current loop bandwidth in the hardware of motor and driver. To eliminate the detrimental effect of the time delay in current servo, a predictive output method based on a nonlinear tracking differentiator (TD) is used to improve the controller within the framework of active disturbance rejection control. Both simulations and experiments are conducted on a prototype one degree of freedom manipulator with a joint torque sensor. The results demonstrate the enhancement of both the system stability and disturbance rejection performances. Based on the proper treatment of actuator delay, the dominant effect of the motor friction and the flexibility of the harmonic drive has been reduced to insignificance. Moreover, the proposed controller is easy to implement because the explicit dynamic model of the system is not required.

A Redundant Manipulator Joint Torque Estimation Method Based on Disturbance Observer

2020 ◽  
Vol 29 (07n08) ◽  
pp. 2040015
Author(s):  
Xun Liu ◽  
Yaqiu Liu ◽  
Hanchen Zhao
Keyword(s):  
Error Compensation ◽  
Disturbance Observer ◽  
Control Method ◽  
Estimation Method ◽  
Industrial Robots ◽  
Torque Control ◽  
Joint Torque ◽  
Torque Sensor ◽  
Torque Estimation ◽  
Joint Torque Sensor

With the continuous development of the robot industry, both industrial robots and collaborative robots are developing towards light type and intelligence. The core issue is that how to improve the dynamic control performance of robots and reduce costs. The accurate torque feedback control can be achieved by introducing a joint torque sensor. The disadvantages brought by it are higher cost and the limited performance of the torque sensor. Therefore, on the basis of the traditional current estimated torque, combined with the accurate joint torque data fed back by the torque sensor, a method to estimate the harmonic transmission torque in the joint based on the disturbance observer is proposed, and a joint torque model is constructed. At the same time, the compensation factor is introduced to improve the accuracy of torque estimation. In the method proposed in this paper, the theoretical position and actual position, speed difference and motor current of the dual encoder on the motor side and the link side are used to estimate the harmonic transmission torque through the disturbance observer, and the corresponding coefficient is identified. By calibrating the transmission error compensation term and friction force with the torque sensor, the joint torque estimation model is obtained, and the sensorless joint torque estimation can be realized. This method does not require additional torque error compensation caused by harmonic drive deformation in the controller. Therefore, the torque control method without torque sensor is adopted in batch, which is not affected by the configuration and dynamic parameters of the manipulator. In the experiment, the output data of the joint torque sensor is used for testing and comparison. Through the single joint and redundant robot manipulator integration testing, the effectiveness of the proposed joint torque estimation method is verified.

scholarly journals Behavioural Study of the Force Control Loop Used in a Collaborative Robot for Sanding Materials

Materials ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 67
Author(s):  
Rodrigo Pérez Ubeda ◽  
Santiago C. Gutiérrez Rubert ◽  
Ranko Zotovic Stanisic ◽  
Ángel Perles Ivars
Keyword(s):  
Force Control ◽  
Feed Rate ◽  
Significant Variation ◽  
Industrial Robots ◽  
Control Loop ◽  
Saturation State ◽  
Collaborative Robots ◽  
Increase Productivity ◽  
Different Characteristics ◽  
Collaborative Robot

The rise of collaborative robots urges the consideration of them for different industrial tasks such as sanding. In this context, the purpose of this article is to demonstrate the feasibility of using collaborative robots in processing operations, such as orbital sanding. For the demonstration, the tools and working conditions have been adjusted to the capacity of the robot. Materials with different characteristics have been selected, such as aluminium, steel, brass, wood, and plastic. An inner/outer control loop strategy has been used, complementing the robot’s motion control with an outer force control loop. After carrying out an explanatory design of experiments, it was observed that it is possible to perform the operation in all materials, without destabilising the control, with a mean force error of 0.32%. Compared with industrial robots, collaborative ones can perform the same sanding task with similar results. An important outcome is that unlike what might be thought, an increase in the applied force does not guarantee a better finish. In fact, an increase in the feed rate does not produce significant variation in the finish—less than 0.02 µm; therefore, the process is in a “saturation state” and it is possible to increase the feed rate to increase productivity.

Finding Features of Positioning Error for Large Industrial Robots Based on Convolutional Neural Network

2021 ◽  
Author(s):  
Daiki Kato ◽  
Kenya Yoshitugu ◽  
Naoki Maeda ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Production Systems ◽  
Industrial Robot ◽  
Three Dimensional ◽  
Industrial Robots ◽  
Numerical Control ◽  
Robot Motion ◽  
Positioning Error ◽  
End Effector

Abstract Most industrial robots are taught using the teaching playback method; therefore, they are unsuitable for use in variable production systems. Although offline teaching methods have been developed, they have not been practiced because of the low accuracy of the position and posture of the end-effector. Therefore, many studies have attempted to calibrate the position and posture but have not reached a practical level, as such methods consider the joint angle when the robot is stationary rather than the features during robot motion. Currently, it is easy to obtain servo information under numerical control operations owing to the Internet of Things technologies. In this study, we propose a method for obtaining servo information during robot motion and converting it into images to find features using a convolutional neural network (CNN). Herein, a large industrial robot was used. The three-dimensional coordinates of the end-effector were obtained using a laser tracker. The positioning error of the robot was accurately learned by the CNN. We extracted the features of the points where the positioning error was extremely large. By extracting the features of the X-axis positioning error using the CNN, the joint 1 current is a feature. This indicates that the vibration current in joint 1 is a factor in the X-axis positioning error.

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