Selective Compliance Assembly Robot Arm Research Articles

Complete Kinematics/Dynamics Modeling and Performance Analysis of a Novel SCARA Parallel Manipulator Based on Screw Theory

Abstract In this paper, a novel Selective Compliance Assembly Robot Arm (SCARA) high-speed parallel manipulator that can realize three-translation and one-rotation motion is proposed, and an accurate dynamic modeling methodology is investigated. The mechanism is composed of four limbs with a double parallelogram structure and a single moving platform. The high bearing capacity and high dynamic response of the novel mechanism make it a viable alternative choice for this kind of automation equipment. The degree-of-freedom (DOF) of the mechanism is analyzed by the screw theory. At the same time, the velocity mapping model of the mechanism is established by the twist screw and the actuated Jacobian matrix. Then, the acceleration mapping model of the mechanism, including the generalized kinematic pairs, is established by reduced acceleration state, the modified Lie screw, and the acceleration Hessian matrix. On this basis, the complete dynamic model with a compact form of the mechanism is deduced by the combination of screw theory and virtual work principle, and the correctness of the developed model is verified by multibody simulation software. Finally, considering the inertial characteristics of the mechanism, the dynamic performance distribution in the reachable workspace of the mechanism is analyzed by the Joint-Reflected Inertia (JRI) index and Coefficient of Variation of joint-space Inertia (CVI) index, and some areas are selected as the task workspace using the above index to guarantee good dynamic performance.

Design and development of dual-extruder food 3D printer based on selective compliance assembly robot arm and printing of various inks

Recent research in food 3D printing mainly focuses on ink materials, with relatively limited research on printer equipment development. Furthermore, the development of traditional food three-axis 3D printer is high cost and low productivity. In this study, we developed a dual extruder for food 3D printing, and designed a dual-extruder system based on Selective Compliance Assembly Robot Arm (SCARA) equipment. The productivity was determined by measuring significance analysis on the physical dimensions and the texture characteristics of the printed products using single/dual-extruder. A variety of food inks were experimented and various self-developed print paths were experimented with. The results of the two sets of significance analyses on physical dimensions and texture characteristics lead to the conclusion that the development of the dual-extruder system based on the SCARA machine arm successfully improved productivity. This also verified the broad adaptability of the device's inks, which operate on the principle of air pressure extrusion. We believe that this finding provides a new research direction for 3D printing of food products.

Desain Sistem Mekanik Robot SCARA untuk Internet Nuclear Instrumentation Laboratory

Radiation protection training requires a robotic arm as a radiation shield regulator that can be controlled remotely to reduce radiation risk. The required robot is a Selective Compliance Assembly Robot Arm (SCARA) type robotic arm. This research aims to design a mechanical system for the SCARA robot for the Internet Nuclear Instrumentation Laboratory (INIL). The research method for making robots begins with a 3D CAD design followed by manufacturing and assembly. Robot control using the Arduino IDE application. Robotic arm testing using stress analysis from Autodesk Inventor software and movement and accuracy testing. The results of the research have successfully designed robotic arms with dimensions of 50.8 cm high, 14 cm wide, and 56 cm long. The robot's work area is 189 cm2, with the farthest range being 30 cm. Keywords: radiation protection, mechanical design, SCARA robot, robotic arm, NPP, INIL

SCARA TIPI 3D YAZICI TASARIMI VE İMALİ

This study designed and produced a Selective Compliance Assembly Robot Arm (SCARA)-type three-dimensional Fused Deposition Modelling (FDM) printer with three degrees of freedom based on kinematic and dynamic analyses. The dynamic capability of a SCARA robot and the FDM production method were combined, and a unique printer system was obtained by using open-source software. The Kinematic Calculations were achieved by analytical methods by using geometrical equations. An open-loop control system was created by inputting forward kinematic and inverse kinematic equations to the control software. The printing processes of the Cube and Prism samples were carried out with the SCARA-type three-dimensional printer. The data obtained from the analytical calculations and the results obtained from the experiments were compared, and the error rates in the desired and obtained prints and findings that were obtained based on print quality were shared. Academic studies on SCARA in the literature have usually focused on the dynamic calculations, design and control of SCARA robots, and especially in recent years, implementation of nature-inspired algorithms. A unique printer system was obtained by using open-source software. Prints were taken from cube and prism samples, and the samples were compared to the data obtained from the analytical results.This study implemented a form of a hybrid printer model where the movement capability of a SCARA robot was combined with FDM-type three-dimensional printer production technology. The SCARA-type three-dimensional printer was produced after making kinematic and kinetic calculations.

Collision Avoidance for a Selective Compliance Assembly Robot Arm Manipulator Using Topological Path Planning

Industrial applications with robotic manipulators have grown and made production systems increasingly efficient. However, there are still some limitations that can delay production, causing losses. Several factors, such as accidents and collisions of manipulator robots with operators and other machines, can cause unforeseen stops. Thus, this work aims to develop a trajectory planning method to avoid collisions applied to a selective compliance assembly robot arm (SCARA) robotic manipulator in the context of collaborative robotics. The main contribution of this paper is a path planning method based on mathematical morphology, named topological path planning (TPP). Through some evaluation metrics such as the number of path points, computing time, distance, standard deviation of the joint acceleration, and maximum acceleration rate along the path, we show that TPP is a collision-free, deterministic, and predictable route planning. In our experiments, our proposal presented better results for applications in industrial robotic manipulators when compared to the probabilistic roadmap method (PRM) and TPP*, a particular case of TPP that is similar to the generalized Voronoi diagram (GVD).

Model Predictive Trajectory Tracking Control of 2 DoFs SCARA Robot under External Force Acting to the Tip along the Trajectory

The robot arms often follow a certain trajectory depending on the type of end effector with functions of spray-painting, arc welding, bonding or machining etc. Therefore, trajectory-tracking control is a very important issue in robot arm applications. Also, the robot must be able to follow the determined trajectory stably under the influence of external forces or machining forces it encounters in its operations. In this study, a Model Predictive Control (MPC) for trajectory tracking control of a 2 Degrees of Freedom (DoFs) Selective Compliant Assembly Robot Arm (SCARA) under an external force acting to the tip of the robot along the trajectory was performed. The effectiveness of the MPC method used has been demonstrated by simulation applications. According to simulation studies, successful results were obtained.

A Direct-Drive Permanent-Magnet Motor Selective Compliance Assembly Robot Arm: Modeling, Motion Control, and Trajectory Optimization Based on Direct Collocation Method

A Direct-Drive Permanent-Magnet Motor Selective Compliance Assembly Robot Arm: Modeling, Motion Control, and Trajectory Optimization Based on Direct Collocation Method

Fault Diagnosis in Robot Manipulators Using SVM and KNN

In this paper, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) based methods are to be applied on fault diagnosis in a robot manipulator. A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work. For both classifiers, the torque, the position and the speed of the manipulator have been employed as the input vector. However, it is to mention that a large database is needed and used for the training and testing phases. The SVM method used in this paper is based on the Gaussian kernel with the parameters γ and the penalty margin parameter “C”, which were adjusted via the PSO algorithm to achieve a maximum accuracy diagnosis. Simulations were carried out on the model of a Selective Compliance Assembly Robot Arm (SCARA) robot manipulator, and the results showed that the Particle Swarm Optimization (PSO) increased the performance of the SVM algorithm with the 96.95% accuracy while the KNN algorithm achieved a correlation up to 94.62%. These results showed that the SVM algorithm with PSO was more precise than the KNN algorithm when was used in fault diagnosis on a robot manipulator.

Task level disentanglement learning in robotics using βVAE

Humans observe and infer things in a disentanglement way. Instead of remembering all pixel by pixel, learn things with factors like shape, scale, colour etc. Robot task learning is an open problem in the field of robotics. The task planning in the robot workspace with many constraints makes it even more challenging. In this work, a disentanglement learning of robot tasks with Convolutional Variational Autoencoder is learned, effectively capturing the underlying variations in the data. A robot dataset for disentanglement evaluation is generated with the Selective Compliance Assembly Robot Arm. The disentanglement score of the proposed model is increased to 0.206 with a robot path position accuracy of 0.055, while the state-of-the-art model (VAE) score was 0.015, and the corresponding path position accuracy is 0.053. The proposed algorithm is developed in Python and validated on the simulated robot model in Gazebo interfaced with Robot Operating System.

Optimization on flow field in an operational cleanroom subject to kinematic component and structural parameters

Due to the high uniformity and stability of streamline, unidirectional cleanrooms are widely used in semiconductor clean workshops. However, the moving parts and complex process of production in the cleanrooms may cause disturbance of the streamline, leading to increased contamination. In this study, a small wafer transport cleanroom with a SCARA (selective compliance assembly robot arm) as a moving part is considered, and a numerical method of overset mesh together with the momentum source method is employed to simulate the flow field, which is compensated with experimental measurements. Hence, taking the nonuniformity and air age as the indicators to evaluate the airflow and particulate emission efficiency of the cleanroom, nine influencing factors are investigated by making orthogonal simulations. This includes the velocity, the height, the cross-section area, and the moving range of the SCARA, the velocity of supplied air, the blowing ratio, and the distribution of the Fan Filter Units, the height and the grid porosity of the cleanroom. The optimal results alleviate the average and standard deviation of nonuniformity by 6.22% and 33.39%, respectively, compared with the original design of a factory. The particulate emission efficiency of the flow field is also significantly improved. Furthermore, the results inspire a potential optimal design for cleanrooms with dynamic loads in typical semiconductor processes.

Evaluation of Parameter Identification of a Real Manipulator Robot

Given the widespread use of the Kalman filter in robotics, an increasing number of researchers devote themselves to its study and application. This work underscores the importance of this filter while analyzing the modifications made to the same to improve its performance and reduce its deficiencies in some fields and presenting some of its applications in robotics. The following methods are presented in this study: least squares (LS), Hopfield Neural Networks (HNN), Extended Kalman filter (EKF), and Unscented Kalman filter (UKF). These methods are used in the parameter identification of a Selective Compliant Assembly Robot Arm (SCARA) robot with 3-Degrees of Freedom (3-DoF) and a clamp at its end. The dynamic model of this robot is obtained and employed to identify its parameters; then, the identification results are compared considering the difference between the obtained parameters and the real values of the robot parameters; in this comparison, the good results yielded by the LS and UKF method stand out. Subsequently, the obtained parameters through each method are validated by measuring different performance indexes—during trajectory tracking—such as: Residual Mean Square Error (RMSE), Integral of the Absolute Error (IAE), and the Integral of the Square Error (ISE). In this way, a comparison of the robot’s performance is possible.

SCARA mechanical fault identification based on WPM-SE+BPNN method

The Selective Compliance Assembly Robot Arm (SCARA) is becoming increasingly important in industrial manufacturing and its operating condition directly determines the safe operation of production lines. However, the complex structure, the variety of mechanical faults and unstable movements of SCARA make fault identification extremely difficult. Therefore, this study proposed identification of SCARA mechanical faults based on wavelet packet multi-segment entropy (WPM-SE) + back propagation neural network (BPNN). First, the original vibration signal was decomposed into several sub-node signals by wavelet packet transform and its envelope spectrum was obtained by Hilbert transform. Then, the envelope spectrum was divided equally into multiple intervals along the time axis, and the energy of each interval was calculated. Afterwards, the feature information of the envelope spectrum was obtained from the energy of each interval, which is defined as multi-segment entropy (M-SE). Where an envelope spectrum obtains a M-SE, and the number of segmentation intervals determines the value of the M-SE. Finally, a feature vector composed of the values of the M-SE was used as the feature input data of the BPNN for mechanical fault identification in SCARA. The BPNN has been tested to achieve an average recognition accuracy of 99.67% for both single mechanical faults and multiple mechanical faults. The results show that the WPM-SE method can effectively extract the feature information of the vibration signal and achieve fast and accurate identification of mechanical faults in SCARA.

Design and implementation of fault-tolerant control strategies for a real underactuated manipulator robot

This paper presents the design and implementation of four control strategies applied to a real underactuated manipulator robot with 3-DOF (Degrees of Freedom). Additionally, an original methodology for controlled oscillatory compensations is designed and implemented to mitigate the effect of a passive joint on the overall performance of this manipulator robot. The objective of this methodology is to create controlled oscillations that allow the faulty link and its (passive) joint to physically align with their adjacent previous link. The implemented control techniques are sinh–cosh, neural compensation, gain scheduling PID, and gain scheduling sinh–cosh. The real robot in which these four control strategies and oscillatory compensation methodology are implemented is a SCARA (Selective Compliant Assembly Robot Arm) robot. To assess controller performance—once the 3-DOF underactuated manipulator robot starts its trajectory—after t = 4.5 s, a fault is activated in its joint No. 2, converting it into a passive joint. The performance indicators IA (index of agreement), RMS (Root Mean Square), and RSD (Residual Standard Deviation) are used to analyze, compare, and evaluate the behavior of the four control strategies and the compensation methodology.

Design and analysis of a novel 3T1R four-degree-of-freedom parallel mechanism with infinite rotation capability

A 4-degree-of-freedom (DOF) parallel mechanism with 3 translations and 1 rotation (3T1R) has wide application potential in the industry. However, most of the existing 3T1R parallel mechanisms have limited rotation capacity of the moving platform, which is usually less than ±90°. In this paper, a new type of 3T1R parallel mechanism is designed by configuration deduction method based on the 3T1R parallel mechanism studied at present. The structure of the parallel mechanism is simple and symmetrical, and the motion pairs are all rotating pairs, which have Selective Compliance Assembly Robot Arm motion and complete rotation capability. Firstly, the branch motion spiral system is established by using the screw theory, and then the constraint spiral of each branch to the motion platform is solved, and the constraint spiral system is analyzed, so as to determine the number of DOFs and properties of the mechanism. Secondly, the position, velocity, and singularity of the mechanism are analyzed, and the workspace of the mechanism is analyzed by the positive position solution. Then, the trajectory planning of operation space is completed, and a mathematical model of portal grasping trajectory is established. Thirdly, the kinematic simulation analysis of the mechanism is carried out. Finally, an equal scale physical model was made by 3D printing technology to verify the rationality of the design and the correctness of the theoretical analysis.

Structural Design and Position Tracking of the Reconfigurable SCARA Robot by the Pre-Filter AFE PID Controller

The selective compliance assembly robot arm (SCARA) has been developed for decades and has been widely used in industry due to its light structure, quick response and high stiffness in a specific axis. In this work, we designed a reconfigurable SCARA robot with extensible physical space by adjusting the number of modular arms. Considering potential structural failure due to vibration, we employed a multi-objective optimization method based on the finite element method to optimize the bolt connectors between the modules. Compared with the classic finite element optimization method, this method focuses on the key factors affecting the precision of the reconfigurable robot; we used the fitting method of the multi-objective function to obtain the influential factors, which make the optimization results more consistent with a real situation. Then, exploiting the acceleration feedback enhanced (AFE) control strategy, a pre-filter AFE PID controller is proposed by introducing a pre-filter to effectively suppress the system disturbance, which is caused by gaps between the joints when the robot operates at high speed. Experiments show that the reconfigurable SCARA robot still has strong stability under the continuous disturbance of a 2 kg hanging plate weight.

A Fully Automatic Calibration for Vision-Based Selective Compliance Assembly Robot Arm and Its Application to Intelligent Wafer Inspection Scheduling

A Fully Automatic Calibration for Vision-Based Selective Compliance Assembly Robot Arm and Its Application to Intelligent Wafer Inspection Scheduling

Robust Passivity-based Sliding Mode Control of a Large Class of Nonlinear Systems Subject to Unmatched Uncertainties: A Robot Manipulator Case Study

The existence of unmatched uncertainties is known as a serious challenge for designing a robust sliding mode control (SMC) law for nonlinear systems. To pass over this obstacle, a novel robust passivity-based sliding mode approach is proposed here for a large class of MIMO nonlinear systems. The design procedure is accomplished in two steps in presence of both matched and unmatched uncertainties. First, an efficient adaptive sliding surface is designed based on the passivity concept. The passivity provides a flexible robust structure for the sliding surface and guarantees the global asymptotic stability of the reduced-order equations of the system as well. Furthermore, simple adaptation laws are obtained to eliminate the effects of uncertainties. Then, the robust controller is formulated using the SMC method. As another important advantage, the proposed approach is relaxed from the upper bound’s constraints of the uncertainties. It is also applied to n-link rigid electrically driven robot manipulator as a widely-used practical nonlinear system. The simulation results show the appropriate tracking performance for the industrial selective compliance assembly robot arm (SCARA) in the presence of noisy external disturbances. Also, an extending case is studied with related research to emphasize the potential of the proposed approach.

Comparison of design approaches for low-cost sampling mechanisms in open-source chemical instrumentation

Robotic positioning systems are used in a variety of chemical instruments, primarily for liquid handling purposes, such as autosamplers from vials or well plates. Here, two approaches to the design of open-source autosampler positioning systems for use with 96-well plates are described and compared. The first system, a 3-axis design similar to many low-cost 3D printers that are available on the market, is constructed using an aluminum frame and stepper motors. The other system relies upon a series of 3D printed parts to achieve movement with a series of linker arms based on Selective Compliance Assembly Robot Arm (SCARA) design principles. Full printer design files, assembly instructions, software, and user directions are included for both samplers. The positioning precision of the 3-axis system is better than the SCARA mechanism due to finer motor control, albeit with a slightly higher cost of materials. Based on the improved precision of this approach, the 3-axis autosampler system was used to demonstrate the generation of a segmented flow droplet stream from adjacent wells within a 96-well plate.

Optimization of Multi-Intelligent Robot Control System Based on Wireless Communication Network

The robot is a very complex multi-input multioutput nonlinear system. It has time-varying, strong coupling, and nonlinear dynamic characteristics, and its control is very complicated. Due to the inaccuracy of measurement and modeling, coupled with changes in the load and the influence of external disturbances, it is actually impossible to obtain an accurate and complete dynamic model of the robot. We must face the existence of various uncertain factors of the robot. This paper analyzes the real-time communication protocol in the wireless network control system and confirms that the main way to improve the real-time performance of the wireless network control system is to implement the real-time media access control (MAC) protocol. This paper studies robots from the perspective of control and mainly discusses how to use artificial immune algorithms to design robust nonlinear proportion integral derivative (PID) controllers. A nonlinear PID controller is used to replace the classic PID controller. The nonlinear link can be adjusted with the change of the error, so as to achieve the purpose of improving the adaptability and robustness to obtain satisfactory tracking performance. We carried out selective compliance assembly robot arm (SCARA) robot remote control experiment, dual robot following experiment, SCARA and ABB robot collisionless motion planning experiment, and multirobot intelligent collaborative assembly experiment. The experimental results show that the C/S mode remote control system has good practicability and can complete remote tasks; the P2P communication system has good information transmission effects and can realize real-time information sharing between robots; the collision-free motion planning algorithm enables the dual robots to complete obstacle avoidance tasks well in complex operating environments; the functional modules of the system can closely cooperate to complete the tasks in coordination, and the multirobot system has a certain degree of intelligence.

Combining convolutional neural networks with unsupervised learning for acoustic monitoring of robotic manufacturing facilities

For automated robotic manufacturing, a key aspect of monitoring is the identification and segmentation of core actuation processes captured in sensor logs. Once segmented, the behavior of an industrial system during a particular actuation can be tracked to detect signs of degradation. This study presents a technique for performing such an analysis through a combination of machine learning techniques designed to work with an acoustic monitoring system. A spectrogram-based convolutional neural network (CNN) is first trained to identify and segment primary motion classes from acoustic data. Unsupervised clustering and feature-space analysis are then employed to further separate the data into motion sub-classes beyond the capabilities of the CNN. This approach was evaluated on acoustic recordings of a Selective Compliance Assembly Robot Arm (SCARA) system. The developed CNN performed primary robotic motion segmentation with a maximum actuation identification accuracy of 87% when compared to validation data. The unsupervised clustering process had mixed success in distinguishing more fine-grained motion sub-classes due to strong variances in signal energy for some sub-classes. Further refinement is required for improved segmentation accuracy as well as automatic feature generation. The application of this process for life-cycle system monitoring is discussed as well.