6-DOF Industrial Robot Research Articles

Sensorless robot collision detection based on fuzzy momentum observer

During physical human–robot interaction, unwanted collisions occur, which also threaten human safety. That is why different collision detection algorithms are proposed for safe collaboration between human and robots. The current detection methods are model-based methods without using additional sensors. However, the estimation of the collision force using a model-based method still has the problem of an unavoidable trade-off between the collision sensitivity and the reduction of the peaking value, and, in addition, the immunity to the model uncertainties. In this paper, an improved momentum observer using fuzzy system is proposed for robot collision detection. First, to avoid using the robot’s inverse inertia matrix, the generalized moment state equations based on the robot’s dynamics are deduced. Second, the extended linear momentum observer is built based on robot generalized momentum equations. Third, a fuzzy system is designed to select an appropriate bandwidth for the observer. In the design of this fuzzy system, the estimation error is considered as a fuzzy input, and the observer’s bandwidth is considered as its output. This fuzzy generalized momentum observer (FGMO) is compared with the existing generalized moment observers (GMOs) and has been proven its ability to overcome the mentioned above issues due to its ability to intelligently adjust the online filter parameters during the observation process. Finally, simulations are conducted with a 6-degree-of-freedom (6-DOF) industrial robot manipulator, and the obtained results illustrate the effectiveness of proposed approach in term of sensitivity and peaking value.

Kinematic and Parametric Modeling of 6DOF(Degree-of-Freedom) Industrial Welding Robot Design and Implementation

Kinematic and Parametric Modeling of 6DOF(Degree-of-Freedom) Industrial Welding Robot Design and Implementation

Trajectory Analysis of 6-DOF Industrial Robot Manipulators by Using Artificial Neural Networks.

Robot manipulators are robotic systems that are frequently used in automation systems and able to provide increased speed, precision, and efficiency in the industrial applications. Due to their nonlinear and complex nature, it is crucial to optimize the robot manipulator systems in terms of trajectory control. In this study, positioning analyses based on artificial neural networks (ANNs) were performed for robot manipulator systems used in the textile industry, and the optimal ANN model for the high-accuracy positioning was improved. The inverse kinematic analyses of a 6-degree-of-freedom (DOF) industrial denim robot manipulator were carried out via four different learning algorithms, delta-bar-delta (DBD), online back propagation (OBP), quick back propagation (QBP), and random back propagation (RBP), for the proposed neural network predictor. From the results obtained, it was observed that the QBP-based 3-10-6 type ANN structure produced the optimal results in terms of estimation and modeling of trajectory control. In addition, the 3-5-6 type ANN structure was also improved, and its root mean square error (RMSE) and statistical R2 performances were compared with that of the 3-10-6 ANN structure. Consequently, it can be concluded that the proposed neural predictors can successfully be employed in real-time industrial applications for robot manipulator trajectory analysis.

Cyber-physical recreation of six DOF industrial robot arm

AbstractThe Cyber-Physical and Vehicle Manufacturing Laboratory, a model Industry 4.0 laboratory, is applying new innovative solutions to improve the quality of education. As part of this, a digital twin of the lab was designed and built, where users can practice. In the virtual space, it is possible to apply the known robot motion types, and the tool centre and wrist speed have been measured virtually. Robot control tasks can be performed “offline” using parameters. This information can then be transferred to the actual physical robot unit. The stable diffusion 1.5 deep learning model generates 2D geometric shapes for trajectory, allowing users to perform unique tasks during education. The Google Colab cloud-based service was used to teach our rendered-type dataset. For the 3D simulation frame, we used V-REP, which was developed on a desktop PC equipped with an Intel Core i5 7600K processor, Nvidia GTX1070 VGA with 8 GB of DDR5 VRAM, and 64 GB of DDR4 memory modules. The following material describes an existing industrial six-axis robot arm and its implementation, which can be controlled and programmed while performing virtual measurements after integrating into a Cyber-Physical system and using deep learning techniques.

New Approach to Planning the Complex Movements of 6-DOF Industrial Robot Subjected to Acceleration Constraints

New Approach to Planning the Complex Movements of 6-DOF Industrial Robot Subjected to Acceleration Constraints

Two-step calibration of 6-DOF industrial robots by grouping kinematic parameters based on distance constraints

The positioning accuracy of industrial robots is critical for their applications, which can be improved through calibration. This paper proposes a two-step calibration method for six-degree-of-freedom (6-DOF) industrial robots to reduce couplings of their modified Denavit-Hartenberg (MD-H) model parameters during optimization processes of kinematic calibration. In this method, a distance error model is established based on the distance constraints, and the MD-H model parameters of the industrial robots are identified group by group using the Levenberg-Marquardt (LM) algorithm. To determine the optimal MD-H model parameter grouping, the different grouping strategies are simulated and analyzed. In the proposed method, the coordinate system transformation between the measurement frame and the robot base frame can be avoided based on the distance error model, thereby reducing the errors introduced by the coordinate system transformation during the calibration process. Finally, the proposed two-step calibration method is verified through an experiment. The experimental result shows that the distance/positioning accuracy on the robot end-effector is improved from 0.602 mm/2.7317 mm to 0.151 mm/0.717 mm after calibration. The presented study may be beneficial for improving performance of industrial robots.

Time-Optimized Robot Trajectory Based on Improved Whale Optimization Algorithm in Intelligent Manufacturing

Abstract To discuss the low convergence accuracy of whale optimization algorithm (WOA) and the problem of converging to local optima, we proposed using nonlinear convergence factors and introducing nonlinear inertia weights in the WOA. The modified WOA was used to optimize the trajectory of a six-degrees-of-freedom (6DOF) industrial robot. To improve the convergence accuracy and the local and global search ability of the WOA, we first replaced the convergence factor with a nonlinear convergence factor and added a nonlinear inertia weight. The algorithm was used along with a quintic polynomial equation to develop a time-optimal trajectory, for the robot, for use in practical application scenarios. Simulation experiment results showed that the duration of a complete loading–unloading process was reduced by 30% after robot motion trajectory optimization compared with that before optimization, indicating the effectiveness of the improved WOA and its suitability for robot trajectory optimization.

The design and implementation of trajectory planning based on 4 DOF industrial robot

With the rapid progress of science and technology, industrial manufacturing and processing technology have also developed rapidly. Industrial robots are widely used in many industrial fields. In this paper, the design and implementation of trajectory planning based on 4 DOF industrial robot is accomplished. Firstly, trajectory planning of the 4 DOF industrial robot is analyzed. Secondly, the operation path is planned, and detailed operation steps are designed. Finally, the simulation verification process of trajectory planning for the 4 DOF industrial robot is completed, and some research results are obtained.

Modeling, Simulation and Implementation of Visual Servoing for a 6-DOF Industrial Robot

Modeling, Simulation and Implementation of Visual Servoing for a 6-DOF Industrial Robot

An Accurate Dynamic Model Identification Method of an Industrial Robot Based on Double-Encoder Compensation

Aiming at the challenges to accurately simulate complex friction models, link dynamics, and part uncertainty for high-precision robot-based manufacturing considering mechanical deformation and resonance, this study proposes a high-precision dynamic identification method with a double encoder. Considering the influence of the dynamic model of the manipulator on its control accuracy, a three-iterative global parameter identification method based on the least square method and GMM (Gaussian Mixture Model) under the optimized excitation trajectory is proposed. Firstly, a bidirectional friction model is constructed to avoid using residual torque to reduce the identification accuracy. Secondly, the condition number of the block regression matrix is used as the optimization objective. Finally, the joint torque is theoretically identified with the weighted least squares method. A nonlinear model distinguishing between high and low speeds was established to fit the nonlinear friction of the robot. By converting the position and velocity of the motor-side encoder to the linkage side using the deceleration ratio, the deformation quantity could be calculated based on the discrepancy between theoretical and actual values. The GMM algorithm is used to compensate the uncertainty torque that was caused by model inaccuracy. The effectiveness of the proposed method is verified by a simulation and experiment on a 6-DoF industrial robot. Results prove that the proposed method can enhance the online torque estimation performance by up to 20%.

Kinematics Analysis and Modelling Based on 4 DOF Industrial Robot

With the rapid progress of science and technology, industrial manufacturing and processing technology have also developed rapidly. Industrial robots are widely used in many industrial fields. In this paper, based on the familiarity with the structure and principle of the 4 DOF industrial robot, combined with the virtual simulation platform, the kinematics analysis, modelling, and simulation of the 4 DOF industrial robot are studied. Firstly, the structure of the 4 DOF industrial robot is analyzed, the coordinate transformation is studied, and the coordinate transformation simulation is designed. Secondly, D-H parameter modeling and modelling simulation are achievements. Finally, the kinematic analysis and modeling are completed, and some research results are obtained.

AK-HRn: An efficient adaptive Kriging-based n-hypersphere rings method for structural reliability analysis

With increasing complexity of engineering problems, various traditional reliability analysis methods are facing rising challenges in terms of computational efficiency and accuracy. Surrogate models, especially Kriging model, have received growing attention and been widely used in reliability analyses by the virtue of their advantages for achieving high computational efficiency and ensuring high numerical accuracy. Nevertheless, there have been still two significant problems in the Kriging model-assisted reliability analyses due to the absence of prior knowledge: i.e. (1) the size of candidate sample pool tends to be quite large in order to ensure prediction of a convergent failure probability; and (2) local prediction accuracy of limiting state surface by Kriging model is generally excessive. These above two issues can often result in high computational cost for Kriging-based reliability analyses. To enhance computational efficiency, a new method that combines adaptive Kriging and n-hypersphere rings, named an AK-HRn method, is proposed in this study. First, the n-hypersphere rings, which can update its position and radius adaptively, is adopted to divide the design space into potential safety domains and potential failure domains. Second, these potential failure domains are used as the sampling domains for implementing importance sampling method to generate a suitably-sized candidate sample pool. Third, a novel learning function is presented to enrich the design of experiment (DoE), which avoids excessive local prediction accuracy of Kriging models by establishing the rejection domains. Finally, the efficiency and robustness of AK-HRn is compared with other Kriging-based reliability analysis methods through four illustrative numerical examples and one 6-DOF industrial robot case study. Comparison shows that the proposed AK-HRn method has high efficiency and robustness to solve complex reliability analysis problems.

A nonlinear robot joint friction compensation method including stick and sliding characteristics

PurposeDue to dynamic model is the basis of realizing various robot control functions, and it determines the robot control performance to a large extent, this paper aims to improve the accuracy of dynamic model for n-Degree of Freedom (DOF) serial robot.Design/methodology/approachThis paper exploits a combination of the link dynamical system and the friction model to create robot dynamic behaviors. A practical approach to identify the nonlinear joint friction parameters including the slip properties in sliding phase and the stick characteristics in presliding phase is presented. Afterward, an adaptive variable-step moving average method is proposed to effectively reduce the noise impact on the collected data. Furthermore, a radial basis function neural network-based friction estimator for varying loads is trained to compensate the nonlinear effects of load on friction during robot joint moving.FindingsExperiment validations are carried out on all the joints of a 6-DOF industrial robot. The experimental results of joint torque estimation demonstrate that the proposed strategy significantly improves the accuracy of the robot dynamic model, and the prediction effect of the proposed method is better than that of existing methods.Originality/valueThe proposed method extends the robot dynamic model with friction compensation, which includes the nonlinear effects of joint stick motion, joint sliding motion and load attached to the end-effector.

Dynamic grasping of manipulator based on realtime smooth trajectory generation

For safe and effective grasping in a dynamic environment, planning algorithms need real-time to deal with changing the target’s movement and obstacles. This paper proposes a new sequential Sense-Plan-Act (SeqSPA) dynamic grasping framework to generate a robot’s real-time and smooth grasping trajectory. Specifically, we cluster all stable grasps of the target, transform the clustering centers into pregrasps, and predict the future motion of the moving objects by using the observed values. The trajectory optimization algorithm constructing the approximative joint space gradient field can generate a smooth trajectory for a 6-DOF industrial robot arm within 2 ms. Our method generates trajectories for multiple pregrasps and selects the time-optimal trajectory for execution. Simulation comparison and actual experiments verify that our framework can immediately respond to environmental changes and efficiently find a grasping trajectory of the near-optimal time. The trajectory optimization algorithm in the framework can also be used alone to generate a real-time grasping trajectory when the prediction module cannot accurately predict the target motion.

Error Analysis of a Coordinate Measuring Machine with a 6-DOF Industrial Robot Holding the Probe

A complex surface measurement is important for quality control and manufacturing processes. Articulated arm coordinate measuring machines (AACMMs) are widely used in measuring the complex surface. However, the AACMMs that are currently used always require manual operation, which reduces efficiency and introduces operator errors. This study presents a measuring device with a 6−DOF industrial robot holding a contact probe, which realizes the automation measurement of a complex surface and eliminates operator errors compared with the traditional measurement process of an AACMM. In order to explore the source of the measuring errors of the device, the influence of three measurement parameters (approaching velocity, contact angle, and measurement position) on the measurement error of the device is analyzed in this paper. A calibration ball measurement experiment is conducted for each parameter. The results show that the optimal approaching velocity of the measuring device is around 2 mm/s, the probe should be as perpendicular as possible to the surface being measured during the measurement, and the maximum measurement error at different positions is 0.1979 mm, with a maximum repeatability error of 0.0219 mm. This study will help improve the automation measuring errors of the AACMM by utilizing an industrial robot to hold the probe, pushing for a wider application of the AACMM.

External force estimation for industrial robots using configuration optimization

External force estimation for industrial robots can be applied to the scenes such as human–robot interaction and robot machining. The model-based methods have gained the attention of many researchers because they only need motor signals. However, the performance of the above-mentioned methods depends on the accuracy of robot inverse dynamic model (IDM). Traditional methods focus on improving the accuracy of IDM to obtain a better estimation performance. However, suppressing the negative impact of modelling error can be achieved without reducing the modelling error. Thus, this article proposes an external force estimation method that uses the semi-parametric friction model and applies configuration optimization based on Jacobian condition number (JCN) to reduce the modelling error and its negative impact. First, the IDM is identified. Second, the semi-parametric friction model refines the traditional model in the torque observer to improve the estimation accuracy. Third, the JCN is used as an evaluating indicator to optimize the robot configurations. Finally, several simulations and experiments on a 6-DoF industrial robot demonstrate the validity of the proposed method. This method can enhance the performance of online force estimation by up to 48.28%. In addition, the application of the proposed method is verified on the laboratory-developed machining platform.

Time-optimal path following for non-redundant serial manipulators using an adaptive path-discretization

AbstractThe time-optimal path following (OPF) problem is to find a time evolution along a prescribed path in task space with shortest time duration. Numerical solution algorithms rely on an algorithm-specific (usually equidistant) sampling of the path parameter. This does not account for the dynamics in joint space, that is, the actual motion of the robot, however. Moreover, a well-known problem is that large joint velocities are obtained when approaching singularities, even for slow task space motions. This can be avoided by a sampling in joint space, where the path parameter is replaced by the arc length. Such discretization in task space leads to an adaptive refinement according to the nonlinear forward kinematics and guarantees bounded joint velocities. The adaptive refinement is also beneficial for the numerical solution of the problem. It is shown that this yields trajectories with improved continuity compared to an equidistant sampling. The OPF is reformulated as a second-order cone programming and solved numerically. The approach is demonstrated for a 6-DOF industrial robot following various paths in task space.

Real-time path correction of industrial robots in machining of large-scale components based on model and data hybrid drive

Industrial robots are increasingly used in machining of large-scale components due to advantages of high repeatability, large workspace and low cost. Nevertheless, applying industrial robots to high-accuracy machining of large-scale components remains a challenge, where the major hurdle is the insufficient manipulator stiffness due to joint flexibility. When the robot is performing a machining task, joint flexibility-induced position errors between motor and link called joint position errors (JPEs), as the main source of robot deformations, make the robot deviate from the desired path. For most industrial robots, due to the lack of link-side encoders, it is difficult to obtain the JPEs by direct measurement and compensate them in the controller, which deteriorates the path accuracy of the robot during machining greatly. To address this problem, this paper presents a real-time path correction approach of industrial robots based on JPE estimation and compensation with requiring only motor-side measurements and external wrenches. The proposed approach is divided into three steps. First, to estimate the actual link position of the robot in real-time, the dynamics of a manipulator with joint flexibility called flexible dynamics (FD) is introduced. Second, by taking both FD and disturbance dynamics into account, a novel link state estimator called flexible-dynamics based disturbance Kalman filter (FDBDKF) is developed, and thus JPEs can be estimated in real-time. Third, a data-driven locally weighted projection regression (LWPR)-based JPE prediction and compensation method is developed to further improve the compensation accuracy of the JPEs. Simulation and experimental results, obtained on a 6-DOF industrial robot, demonstrate the feasibility and effectiveness of the proposed approach. Experimental results show significant improvement (>80%) in the path accuracy of a simple material removal process corrected using the proposed approach.

Trajectory planning method with grinding compensation strategy for robotic propeller blade sharpening application

A propeller is the main part of a water-borne carrier's drive system. Geometrical shape accuracy and sharpness on the cutting side of the propeller's blade play vital roles to minimize fluid resistance and improve transmission efficiency. For Jet Ski and small boats, the significant thickness variation along the blade edge is embedded in the lost-wax casting process. The automatic trajectory planning for the robotic propeller blade's sharpening operation is challenging due to its complicated curved shape and the tiny gaps between two connected blades. The trajectory planning for robotic grinding based on the edge thickness distribution of the standard model may result in over- or under-cutting due to the highly inconsistent shape deformation caused by the casting process. To address the aforementioned challenges, this study proposes a fully automated robot grinding system. The proposed method combines a novel trajectory planning approach based on a parametric and point cloud model with a 3D scanning on edge-based trajectory adjustment approach. Moreover, to improve the grinding accuracy, a laser-vision sensor-based detection algorithm is also developed for grinding compensation to counteract the geometrical variation caused by the casting process. The effectiveness of the proposed system is validated through numerous experimental trials using a 6-DOF industrial robot. The research findings showcase that the proposed technique is feasible, stable, and effective for robotic propeller blade edge sharpening operations.

High-precision magnetorheological finishing based on robot by measuring and compensating trajectory error.

The 6-DOF industrial robot has wide application prospects in the field of optical manufacturing because of its high degrees of freedom, low cost, and high space utilisation. However, the low trajectory accuracy of robots will affect the manufacturing accuracy of optical components when the robots and magnetorheological finishing (MRF) are combined. In this study, aiming at the problem of the diversity of trajectory error sources of robot-MRF, a continuous high-precision spatial dynamic trajectory error measurement system was established to measure the trajectory error accurately, and a step-by-step and multistage iterations trajectory error compensation method based on spatial similarity was established to obtain a high-precision trajectory. The experimental results show that compared with the common model calibration method and general non-model calibration method, this trajectory error compensation method can achieve accurate compensation of the trajectory error of the robot-MRF, and the trajectory accuracy of the Z-axis is improved from PV > 0.2 mm to PV < 0.1 mm. Furthermore, the finishing accuracy of the plane mirror from 0.066λ to 0.016λ RMS and the finishing accuracy of the spherical mirror from 0.184λ RMS to 0.013λ RMS using the compensated robot-MRF prove that the robot-MRF has the ability of high-precision polishing. This promotes the application of industrial robots in the field of optical manufacturing and lays the foundation for intelligent optical manufacturing.