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This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, with control effort detecting motor and encoder faults, while vibration signals identify bearing faults. This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). Results indicate that a WNN, using wavelet scattering features ranked by one-way anova, is optimal due to its consistency and reliability, while these features enhance computational efficiency by reducing classifier size. Sensitivity analysis demonstrates the classifier's capacity to detect untrained faults, highlighting the importance of effective feature extraction and classification methods for fault diagnosis in complex robotic systems. This research significantly contributes to fault diagnosis in delta robots and lays the groundwork for future studies on fault tolerance control and predictive maintenance planning. Future work will focus on the physical implementation of the delta robot in laboratory settings, aiming to improve operational efficiency and reliability in industrial applications. [ABSTRACT FROM AUTHOR]

[Objective] Reduce the running time and impact of Delta robot in food sorting trajectory, improve the efficiency and stability of Delta robot operation. [Methods] Based on the analysis of the food sorting system (using Delta robots), a Delta robot food sorting trajectory optimization method was proposed, which combined NURBS algorithm, sparrow search algorithm and butterfly optimization algorithm. Optimizing the running trajectory of Delta robot for NURBS curve planning with the goal of minimizing running time and impact, combining sparrow search algorithm and butterfly optimization algorithm to solve the model, which realized the optimization of Delta robot sorting trajectory. [Results] The proposed Delta robot sorting trajectory optimization method could balance running time and impact, reduce running time by more than 6.00% and running impact by more than 80.00%. The dynamic grasping success rate of the system was over 99.00%, which could meet the requirements of food sorting. [Conclusion] Optimizing NURBS curves through a combination of runtime and impact optimization can effectively improve the food sorting performance of Delta robots. [ABSTRACT FROM AUTHOR]

Robotic surgery has been steadily growing, with many new platforms entering the field. Research platforms, however, are limited in number, require a sizable capital expenditure or are difficult to access. This paper presents the analysis and development of a novel surgical manipulator based on parallel kinematics, utilizing the Delta robot as a foundational element. We investigate various aspects including kinematics, statics, workspace and constraints of the manipulator. Additionally, a physics-based model is constructed to validate the analysis and facilitate the creation of a control algorithm aimed at input tracking, particularly for teleoperation purposes. Two experiments are conducted to evaluate the manipulator's performance: one focusing on circle tracking and a second one employing real kinematic data from a suturing task. The results indicate a maximum tracking error under 1 mm and an RMS error below 0.6 mm for the first trial and 0.3 mm by 2 mm for the suturing tracking task, respectively. Furthermore, through non-linear Bode analysis we demonstrate that the closed-loop system effectively decouples input–output cross-gain terms while maintaining minimal amplification in the diagonal terms. This suggests that the system is well-suited for the intricate and precise motions required in surgical procedures. [ABSTRACT FROM AUTHOR]

Delta robots are the most common parallel robots for manipulation tasks. In many industrial applications, they must be operated at reduced speed, or dwell times have to be included in the motion planning, to prevent frame vibrations. As a result, their full potential cannot be realized. Against this background, this publication is concerned with the mechanical design of an active dynamic balancing unit for the reduction of frame vibrations. In the first part of this publication, the main design requirements for an active dynamic balancing mechanism are discussed, followed by a presentation of possible mechanism designs. Subsequently, one the most promising mechanisms is described in detail and its kinematics and dynamics equations are derived. Finally, the dimensions of a prototype mechanism designed to experimentally validate the concept of active dynamic balancing are defined using the example of Suisui Bot, a low-cost Delta robot. [ABSTRACT FROM AUTHOR]

This paper introduces a new concept of a zero-platform delta robot with three key parameters affecting the shape and size of the workspace. This concept is applied to directly bring the torus configuration into the links of the robot and shows its usefulness in configuring and generating the workspace conveniently. Analyzing the workspace of parallel robots, such as delta robots, requires extensive computation due to the constraints between the links, typically requiring complex equations or numerical methods. This paper proposes a new method for quickly estimating the shape and size of the workspace using a cross-section diagram based on a geometrical analysis of the zero-platform delta robot. The shape and size of the workspace can be rapidly estimated because the intersection of three cross-section diagrams needs only the torus's 2D operation. Comparing the workspace between the cross-section diagram and the 3D CAD software, this paper shows that the cross-section diagram can analyze the shape and size of the workspace quickly and give a more geometrical understanding of the workspace. [ABSTRACT FROM AUTHOR]

Nowadays, various Delta-type robots are widely used in many technological fields. In this work, we propose a novel 5-DOF Delta-type parallel robot with four linear and one rotational actuators. The major part of the article is devoted to the kinematic analysis of the robot, including solving its inverse and forward kinematic problems. To demonstrate the developed techniques, we consider two numerical examples. In the first one, we solve the inverse kinematics and determine the actuator displacements required to realize a spatial trajectory of the output link. The forward kinematic analysis, presented in the second example, results in six different assembly modes of the robot for the given set of the actuator displacements. The proposed algorithms represent the basis for subsequent velocity, acceleration, and dynamic analysis of the robot, and they can be adapted to other Delta-type parallel robots. [ABSTRACT FROM AUTHOR]

For the application of Delta robots with a 3-R(RPaR) configuration in machining tasks, this paper constructed a 54-parameter kinematic error model and a simplified dynamic model incorporating an integrated joint's position error and friction, respectively. Utilizing Singular Value Decomposition (SVD) of the Linear Model Coefficient Matrix (LMCM) and the coefficient chart, a criterion for identifiability of error components is established. For good identification results, the optimal measurement surface with Fourier series form is obtained using a combination of the Hook–Jeeves Direct Search Algorithm (DSA) and Inner Point Method (IPM). The friction coefficients and other dynamic parameters are obtained through fitting the integrated joint torque-angle pairs measured along specific trajectories using nonlinear least squares regression. The validation of the calibration process is conducted through simulations and experiments. The calibration results provide a foundation for the precise control of integrated joints and the high-precision motion of robots. [ABSTRACT FROM AUTHOR]

This paper presents the subsystem descriptions and testing of a small robotic platform intended to harvest strawberries that are grown on elevated beds in open field conditions. Agricultural robots can assist in the optimization of farm resources and help solve issues related to increasing farm costs and labor shortages. Monolithic, large size harvesters are already in development; however, they are susceptible to single-point-of-failure and lack the flexibility to adapt to varying field conditions and farm sizes. The proposed robotic harvester covers one row at a time and features a Delta arm configuration manipulator with a five-finger structure as the end-effector to individually pick strawberries. A deep neural network-based vision subsystem using a YOLOv4 model was adopted, which was configured to detect small objects and to locate and classify strawberries into five stages of maturity. During experiments on a commercial farm, the proposed platform, including vision, manipulation, and overbed navigation and control subsystems, achieved an overall success rate of 71.7% for five environment scenarios, with a minimum of 37.5% (the most complicated scenario) and a maximum of 94.0% (the easiest scenario). The average harvesting speed of the system was 7.5 s per strawberry.

This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, with control effort detecting motor and encoder faults, while vibration signals identify bearing faults. This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). Results indicate that a WNN, using wavelet scattering features ranked by one-way anova, is optimal due to its consistency and reliability, while these features enhance computational efficiency by reducing classifier size. Sensitivity analysis demonstrates the classifier’s capacity to detect untrained faults, highlighting the importance of effective feature extraction and classification methods for fault diagnosis in complex robotic systems. This research significantly contributes to fault diagnosis in delta robots and lays the groundwork for future studies on fault tolerance control and predictive maintenance planning. Future work will focus on the physical implementation of the delta robot in laboratory settings, aiming to improve operational efficiency and reliability in industrial applications.

The Delta robot is an over-actuated parallel robot with highly nonlinear kinematics and dynamics. Designing the control for a Delta robot to carry out various operations is a challenging task. Various advanced control algorithms, such as adaptive control, sliding mode control, and model predictive control, have been investigated for trajectory tracking of the Delta robot. However, these control algorithms require a reliable input–output model of the Delta robot. To address this issue, we have created a control-affine neural network model of the Delta robot with stepper motors. This is a completely data-driven model intended for control design consideration and is not derivable from Newton's law or Lagrange's equation. The neural networks are trained with randomly sampled data in a sufficiently large workspace. The sliding mode control for trajectory tracking is then designed with the help of the neural network model. Extensive numerical results are obtained to show that the neural network model together with the sliding mode control exhibits outstanding performance, achieving a trajectory tracking error below 5 cm on average for the Delta robot. Future work will include experimental validation of the proposed neural network input–output model for control design for the Delta robot. Furthermore, transfer learnings can be conducted to further refine the neural network input–output model and the sliding mode control when new experimental data become available. [ABSTRACT FROM AUTHOR]

[Display omitted] • This study shows how the delta robot was designed to physically pick-and-place MSW. • Combined with the YOLO-based model, the robot forms the Intelligent MSW Sorter. • IMSWS could automatically sort the multiple MSWs on the conveyor belt. • The mechanical efficiency of the robot dominated IMSWS performance. • Compared with the MSW detection, IMSWS must improve the mechanical efficiency first. Sorting Municipal Solid Waste (MSW) has helped promote the awareness of sustainable development of environment. A robot equipped with an intelligent deep learning (DL) detection algorithm have been proposed to improve the sorting task. But most of the related studies aimed to better the DL algorithms on MSW detection, and few studies integrated the DL algorithms with a robot to identify the dominated factors to Intelligent MSW Sorter (IMSWS). Therefore, this study is to develop IMSWS prototype to better sort MSW, based on the pick-and-place process, and preliminarily evaluate the dominated factors. First, the delta robot prototype was manufactured, and IMSWS was performed with a camera to acquire the RGB image and the height of a MSW in the conveyor belt. The DL algorithm, YOLOv3 or YOLOv4, detected the type and plane location of the MSWs in the conveyor belt. Next, the sequence program transferred the valid MSW data to the delta robot. After the calculation of the absorbed location of the target MSW was made, the arm of this delta robot moved to absorb and then transfer the MSW to the bin. Results showed that the IMSWS prototype could sort the multi-object MSWs in the MSW stream. Both YOLOv3 and YOLOv4 reached high detection accuracy on the MSW image dataset. However, the improvement should be made in the actually moving MSW stream even though the YOLOv4 performed the acceptable detection accuracy. The gripping stability of the arm mainly dominated the performance of IMSWS, and this should be improved first. [ABSTRACT FROM AUTHOR]

The present study focuses on the implementation of Deep Reinforcement Learning (Deep-RL) techniques for a parallel manipulator robot, specifically the Delta Robot, within a simulated setting. We introduced a simulation framework designed to guide the Delta Robot's end-effector to a designated spatial point accurately. Within this environment, the robotic agent undergoes a learning process grounded in trial and error. It garners positive rewards for successful predictions regarding the next action and faces negative repercussions for inaccuracies. Through this iterative learning mechanism, the robot refines its strategies, thereby establishing improved decision-making rules based on the ever-evolving environment states. Our investigation delved into three distinct Deep-RL algorithms: the Deep Q-Network Algorithm (DQN), the Double Deep Q-Network (DDQN), and the Trust Region Policy Optimization Algorithm (TRPO). All three methodologies were adept at addressing the challenge presented, and a comprehensive discussion of the findings is encapsulated in the subsequent sections of the paper. [ABSTRACT FROM AUTHOR]

Robotic surgery has been steadily growing, with many new platforms entering the field. Research platforms, however, are limited in number, require a sizable capital expenditure or are difficult to access. This paper presents the analysis and development of a novel surgical manipulator based on parallel kinematics, utilizing the Delta robot as a foundational element. We investigate various aspects including kinematics, statics, workspace and constraints of the manipulator. Additionally, a physics-based model is constructed to validate the analysis and facilitate the creation of a control algorithm aimed at input tracking, particularly for teleoperation purposes. Two experiments are conducted to evaluate the manipulator’s performance: one focusing on circle tracking and a second one employing real kinematic data from a suturing task. The results indicate a maximum tracking error under 1 mm and an RMS error below 0.6 mm for the first trial and 0.3 mm by 2 mm for the suturing tracking task, respectively. Furthermore, through non-linear Bode analysis we demonstrate that the closed-loop system effectively decouples input–output cross-gain terms while maintaining minimal amplification in the diagonal terms. This suggests that the system is well-suited for the intricate and precise motions required in surgical procedures.

This paper introduces a new concept of a zero-platform delta robot with three key parameters affecting the shape and size of the workspace. This concept is applied to directly bring the torus configuration into the links of the robot and shows its usefulness in configuring and generating the workspace conveniently. Analyzing the workspace of parallel robots, such as delta robots, requires extensive computation due to the constraints between the links, typically requiring complex equations or numerical methods. This paper proposes a new method for quickly estimating the shape and size of the workspace using a cross-section diagram based on a geometrical analysis of the zero-platform delta robot. The shape and size of the workspace can be rapidly estimated because the intersection of three cross-section diagrams needs only the torus’s 2D operation. Comparing the workspace between the cross-section diagram and the 3D CAD software, this paper shows that the cross-section diagram can analyze the shape and size of the workspace quickly and give a more geometrical understanding of the workspace.

Delta robots are the most common parallel robots for manipulation tasks. In many industrial applications, they must be operated at reduced speed, or dwell times have to be included in the motion planning, to prevent frame vibrations. As a result, their full potential cannot be realized. Against this background, this publication is concerned with the mechanical design of an active dynamic balancing unit for the reduction of frame vibrations. In the first part of this publication, the main design requirements for an active dynamic balancing mechanism are discussed, followed by a presentation of possible mechanism designs. Subsequently, one the most promising mechanisms is described in detail and its kinematics and dynamics equations are derived. Finally, the dimensions of a prototype mechanism designed to experimentally validate the concept of active dynamic balancing are defined using the example of Suisui Bot, a low-cost Delta robot.

In this paper, the design and experimental evaluation of a parallel robotic system based on a linear delta geometry is presented. The design considers the requirements for high-precision applications including workspace, motion resolution, and payload. The entire design process includes robot kinematics, control, and optimization, resulting in the demonstration of a working device. The robot structure offers a versatile and simplified design when compared with state-of-the-art devices being able to be adapted to perform different tasks while keeping the advantages of high precision with reduced complexity. The presented robot prototype was constructed and evaluated experimentally through three proof-of-concept experiments mimicking tasks requiring high motion precision such as microsurgery, semiconductor testing, and optical device alignment. The obtained results in the three experimental scenarios validate that the here-proposed design can achieve an average motion precision of ~3.3 ± 0.3 μm with varying load conditions, thus confirming its potential to be used for high-precision tasks in industrial and medical settings. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Industrial delta robots with motion-and visionbase controllers, providing high-precision and fast/flexible motion, normally come with high cost and complex software. Low-cost delta robots with vision solution could be beneficial to vast industries to increase productivity and to reduce labor cost. A delta robot using a low-cost motion controller and an opensource vision system is developed to accomplish real-time visual servoing with high motion accuracy. In the low-cost motion controller, three parallel links' upper-arm angles, computed from inverse kinematics for a given desired target position by a high-level computer, are used as reference position commands for three AC-motor drives. A low-level Arduino microcontroller is employed to convert these links' angles to high-frequency pulses and on-off signals for synchronously controlling three motor angles and direction. Experimental results of a point-topoint motion tracking exhibit high-precision repeatability. Synchronous pulse generation from Arduino microcontroller and structural misalignments of parallel links are major challenges for achieving high motion accuracy. For the visionbased system, the YOLOv5 algorithm is implemented along with a Python GUI Application. Then, the visual-servo performance is evaluated on localization accuracy and recognition rate of 3-color objects. However, a partial object occlusion can reduce the visual classification rate. A sorting task of 4-category medicine boxes demonstrate a high-speed pick-and-place operation using the low-cost visual-servo system of this delta robot. Therefore, integration of low-cost visual servoing with this delta robot can revolutionize various industries, like automobile, pharmaceutical, and food sectors, in separating, sorting and packing applications. [ABSTRACT FROM AUTHOR]

For the application of Delta robots with a 3-R(RPaR) configuration in machining tasks, this paper constructed a 54-parameter kinematic error model and a simplified dynamic model incorporating an integrated joint’s position error and friction, respectively. Utilizing Singular Value Decomposition (SVD) of the Linear Model Coefficient Matrix (LMCM) and the coefficient chart, a criterion for identifiability of error components is established. For good identification results, the optimal measurement surface with Fourier series form is obtained using a combination of the Hook–Jeeves Direct Search Algorithm (DSA) and Inner Point Method (IPM). The friction coefficients and other dynamic parameters are obtained through fitting the integrated joint torque-angle pairs measured along specific trajectories using nonlinear least squares regression. The validation of the calibration process is conducted through simulations and experiments. The calibration results provide a foundation for the precise control of integrated joints and the high-precision motion of robots.

The Delta robot is a parallel robot that is over-actuated and has a highly nonlinear dynamic model, which poses a significant challenge to its control design. The inverse kinematics that maps the motor angles to the position of the end effector is highly nonlinear and extremely important for the control design of the Delta robot. It has been experimentally shown that geometry-based inverse kinematics is not accurate enough to capture the dynamics of the Delta robot due to manufacturing component errors, measurement errors, joint flexibility, backlash, friction, etc. To address this issue, we propose a neural network model to approximate the inverse kinematics of the Delta robot with stepper motors. The neural network model is trained with randomly sampled experimental data and implemented on the hardware in an open-loop control for trajectory tracking. Extensive experimental results show that the neural network model achieves excellent performance in terms of the trajectory tracking of the Delta robot under different operation conditions, and outperforms the geometry-based inverse kinematics model. A critical numerical observation indicates that neural networks trained with the specific trajectory data fall short of anticipated performance due to a lack of data. Conversely, neural networks trained on random experimental data capture the rich dynamics of the Delta robot and are quite robust to model uncertainties compared to geometry-based inverse kinematics. [ABSTRACT FROM AUTHOR]

This paper presents a new technique to design an inverse dynamic model for a delta robot experimental setup to obtain an accurate trajectory. The input/output data were collected using an NI DAQ card where the input is the random angles profile for the three-axis and the output is the corresponding measured torques. The inverse dynamic model was developed based on the deep neural network (NN) and the new COVID-19 optimization to find the optimal initial weights and bias values of the NN model. Due to the system uncertainty and nonlinearity, the inverse dynamic model is not enough to track accurately the preselected profile. So, the PD compensator is used to absorb the error deviation of the end effector. The experimental results show that the proposed inverse dynamic deep NN with PD compensator achieves good performance and high tracking accuracy. The suggested control was examined using two different methods. The spiral path is the first, with a root mean square error of 0.00258 m, while the parabola path is the second, with a root mean square error of 0.00152 m. [ABSTRACT FROM AUTHOR]

Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we develop a low-cost delta robot arm for grasping objects of unspecified size thanks to a vision system. Stepper motors are used instead of ac servo motors to build a low-cost delta robot arm. Furthermore, we use available materials and machining methods such as laser cutting and 3d printing instead of CNC milling and turning to reduce fabrication costs. The controller is based on a low-cost embedded controller - Arduino Uno for controlling the robot's motion. The vision system is constructed to determine the 3D coordinate of objects in the workspace as well as the sizes of objects. The gripper is opened with a distance of two fingers equal to the size of the objects, and the robot is controlled to the objects' coordinates to grasp them. An application to pick up objects on a conveyor belt is developed to validate the design. The experimental results show that the robot system works correctly, the robot arm moves smoothly, and the information determined by the vision system has a small error, ensuring that the robot can accurately pick up products.

Objective: To solve the problems of low efficiency and poor motion stability of DELTA robots in the tomato sorting process of existing tomato processing production lines. Methods: Based on the analysis of the DELTA robot sorting system, the optimized gate shaped trajectory of the cyclotron was used for DELTA robot trajectory planning. By replacing the right angle part of the gate shaped trajectory with a convolutional line, an optimization model with the goal of time optimization was established. The optimized parameters of the gate shaped trajectory were solved using an improved ant colony optimization algorithm. Analyzed its performance through experiments. Results: The proposed method had better average sorting time and sorting success rate compared to the gate shaped trajectory and other methods, with an average sorting time reduction of 11.61% and 1.63%, respectively, and a sorting success rate increase of 2.05% and 0.56%. Conclusion: The proposed planning method effectively improves the sorting accuracy and efficiency of DELTA robots.

Asparagus harvesting presents unique challenges, due to the variability in spear growth, which makes large-scale automated harvesting difficult. This paper describes the development of an asparagus harvesting robot system. The system consists of a delta robot mounted on a mobile track-based platform. It employs a real-time asparagus detection algorithm and a sensory system to determine optimal harvesting points. Low-level control and high-level control are separated in the robot control. The performance of the system was evaluated in a laboratory field mock-up and in the open field, using asparagus spears of various shapes. The results demonstrate that the system detected and harvested 88% of the ready-to-harvest spears, with an average harvesting cycle cost of 3.44 s ± 0.14 s . In addition, outdoor testing in an open field demonstrated a 77% success rate in identifying and harvesting asparagus spears. [ABSTRACT FROM AUTHOR]

In this paper, a delta robot is designed to grasp objects in an automatic sorting system. The system consists of a delta robot arm for grasping objects, a belt conveyor for transmitting objects, a camera mounted above the conveyor to capture images of objects, and a computer for processing images to classify objects. The delta robot is driven by three direct current (DC) servo motors. The controller is implemented by an Arduino board and Raspberry Pi 4 computer. The Arduino is programmed to provide rotation to each corresponding motor. The Raspberry Pi 4 computer is used to process images of objects to classify objects according to their color. An image processing algorithm is developed to classify objects by color. The blue, green, red (BGR) image of objects is converted to HSV color space and then different thresholds are applied to recognize the object's color. The robot grasps objects and put them in the correct position according to information received from Raspberry. Experimental results show that the accuracy when classifying red and yellow objects is 100%, and for green objects is 97.5%. The system takes an average of 1.8 s to sort an object. [ABSTRACT FROM AUTHOR]

The delta robot is considered one of the most well-known representatives of manipulation devices with a parallel structure. It includes three kinematic chains, each containing a rotary drive and a hinged parallelogram. We present solutions to the kinematics problems, including the position problems and the precision analysis problems. When solving them, we applied the screw calculus and showed that contiguity to singular positions causes a repeatability deterioration. [ABSTRACT FROM AUTHOR]

In this study, Delta robot that one of the most studied and used robot and has distinctive advantages and parallel structure, has been optimized in point of dimensions. 3D solid model of the optimized robot model transferred to MATLAB/Simulink environment. Dimensions of the Delta robot have been obtained to ensure desired workspace with minimum dimensions with moth swarm algorithm (MSA) optimization and particle swarm optimization (PSO). Widely known PSO algorithm has been used for the measure to the performance of the MSA algorithm that is new to the literature. Joints of the robot actuated with the brushless DC (BLDC) motors. System has been controlled fractional-order PID (FOPID) controller. Also integer-order PID controller (IOPID) has been used to clearly see the performance of the FOPID with the aim of comparison. Parameters of both of these controllers are optimized with MSA optimization in equal conditions. When the optimization cost results and the performance test results are examined, it is seen that the FOPID controller performed better than IOPID controller. Designed Delta robot system has been tested with different scenarios, and robotic system has been achieve that this scenarios with high performance. [ABSTRACT FROM AUTHOR]

Objective: In order to solve the problems of poor accuracy and low efficiency of delta robot in dynamic target grasping in current food production. Methods: A new dynamic target grasping method was proposed for the grasping control of delta robot in food production line. The dynamic position of the target was calculated through the offset of the conveyor belt and the feedback value of the encoder, and the grasping order of the target was optimized. The dynamic target was grasped by combining the door trajectory and the improved trapezoidal acceleration and deceleration algorithm. The control methods before and after optimization were analyzed through experiments. Results: At the conveying speed of 20 mm/s, the missed grasping rate of the robot before and after optimization decreased from 3.33% to 0, and the grasping speed increased from 0.756 s-1 to 0.860 s-1. At the conveying speed of 30 mm/s, the missed grasping rate of the robot before and after optimization was reduced from 8.83% to 0, and the grasping speed was increased from 0.885 pieces/s to 1.130 pieces/s. Conclusion: The dynamic target grasping method can effectively improve the grasping efficiency of delta robot.

The delta robot can reach higher speeds than traditional serial-axis machines used in fused filament fabrication 3D printing. However, like serial machines, delta printers suffer from undesirable vibration at high speeds which degrades the quality of fabricated parts. This undesirable vibration has been suppressed in serial printers using linear model-inversion feedforward control methods like the filtered B-splines (FBS) approach. However, techniques like the FBS approach are computationally challenging to implement on delta 3D printers because of their coupled, position-dependent dynamics. In this paper, we propose a methodology to address the computational bottlenecks by (1) parameterizing the position-dependent portions of the dynamics offline to enable efficient computation of the model online, (2) computing models at sampled points (instead of every point) along the given trajectory, and (3) employing QR factorization to reduce the number of floating-point arithmetic operations associated with matrix inversion. In simulations, we report a computation time reduction of up to 23× using the proposed method when compared to using the computationally expensive exact LPV model—all while maintaining high tracking accuracy. Accordingly, we demonstrate significant quality improvements on parts printed at various positions on a commercial delta 3D printer using our proposed controller compared to a baseline alternative, which uses an LTI model from one position. Acceleration measurements during printing show that the improvement in print quality of the proposed controller is due to vibration reductions of up to 39% when compared to the baseline controller. [ABSTRACT FROM AUTHOR]

This paper presents a novel compliant Delta micromanipulator with low parasitic motions. Flexure joints are used to construct the compliant counterpart of the Delta robot. The rigid-body modelling technique is employed for forward and inverse kinematic modelling. The compliance analysis of the design is formulated by resorting to the matrix method. Then, finite-element-analysis (FEA) simulations are done to verify the analytical models. The results confirm with acceptable deviations. Compensation factor based correction in the kinematic model is then proposed, which reduces the errors even further. A comparison with the other reported designs reveals that the proposed design displays an overall superior performance in terms of errors and parasitic motions. For an expected 50 μ m motion, the maximum error is observed to be 0.422%, and the maximum parasitic motions in the x, y, and z directions are 0.044%, 0.095%, and 0.018%, respectively. [ABSTRACT FROM AUTHOR]

The article presents the concept and first results of tests of a Virtual Reality system dedicated for interactive education of manual assembly procedure. The presented solution is based on the use of a Delta robot. The main task of the robot is to simulate the shape of virtual objects as an alternative to haptic devices. The manipulator with parallel kinematics is integrated with low-cost VR equipment intended for visualization (HMD) and tracking the position of real objects (optical tracking system), and also provides manipulation of physical models (representing digital data). Experimental tests were conducted with special attention to touch interaction between the user and the end effector of the robot. An interactive VR application has been prepared for the simulation of the developed test scenarios, during which the participant of the virtual scene interacted with the simulated object. Manipulator task was to move to a location enabling such interaction. The scenario of virtual training of procedural task was also performed. The purpose of the test was to demonstrate whether the solution could be applied to work-oriented training at the production stand. [ABSTRACT FROM AUTHOR]

The Delta robot is a high-speed and high-precision parallel robot. When it is in function, the end effector generates residual vibration, which reduces the repeat positioning accuracy and positioning efficiency. The input shaping method has previously been shown to suppress the residual vibration of the robot, but the vibration suppression effect of the single-modal input shaper is not good for the delta robot, which has multiple dominant modes for the residual vibration. To solve this problem, this paper proposes an effective method for residual vibration suppression of Delta robots based on dual-modal input shaping technology. Firstly, the modal analysis of the Delta robot is performed using finite element software, and the dominant modal of its residual vibration is determined. Secondly, six dual-modal input shapers are designed according to the obtained modal parameters. Finally, Simulink is used for simulation analysis to verify the robustness and vibration suppression performance of the designed six dual-modal input shapers and traditional single-modal input shapers. The simulation results show that the designed ZVD-EI dual-modal input shaper has good robustness, can effectively suppress the residual vibration of the Delta robot, and can effectively improve the repetitive positioning accuracy and work efficiency of the Delta robot when it is running at high speed. [ABSTRACT FROM AUTHOR]

In this paper, the design and experimental evaluation of a parallel robotic system based on a linear delta geometry is presented. The design considers the requirements for high-precision applications including workspace, motion resolution, and payload. The entire design process includes robot kinematics, control, and optimization, resulting in the demonstration of a working device. The robot structure offers a versatile and simplified design when compared with state-of-the-art devices being able to be adapted to perform different tasks while keeping the advantages of high precision with reduced complexity. The presented robot prototype was constructed and evaluated experimentally through three proof-of-concept experiments mimicking tasks requiring high motion precision such as microsurgery, semiconductor testing, and optical device alignment. The obtained results in the three experimental scenarios validate that the here-proposed design can achieve an average motion precision of ~3.3 ± 0.3 μm with varying load conditions, thus confirming its potential to be used for high-precision tasks in industrial and medical settings.

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An approach for calculating the effective workspaces of parallel mechanisms has been considered in this paper. The effective workspace is a set of reachable by the end-effector points, where an important parameter (or parameters) from the point of view of operation is not higher than critical. In this article, the speed and torque in the drive and the stiffness of the mechanism are considered as such parameters. An approach to determining the appropriate effective workspace is presented for each parameter. The optimization process, which is based on presented methods, allows one to choose basic technical solutions of the designed robot: the links (lengths and cross-sections), the drives, and types of hinges, which guarantee its operability at the specified operational characteristics. Case studies of a delta robot with four degrees of freedom and a planar five-bar mechanism are presented for testing these approaches. The presented methods are of great practical importance because it uses movement parameters or operational characteristics that are decisive for this mechanism and have clear physical meaning. [ABSTRACT FROM AUTHOR]

Lightweight Delta robot is typical high-speed and high-precision industrial parallel robot. However, under high-speed condition, because of the lightweight components, it will inevitably lead to the vibration of Delta robot, which reduces the position accuracy and positioning efficiency. To solve this problem comprehensively, this article considers the process vibration and residual vibration of Delta robot, and the intelligent shaping vibration suppression control system is designed by using trajectory planning method of improved trapezoidal mode, shaping control method, and fast terminal sliding mode controller, and the detailed experimental analysis is carried out. The experimental results show that the proposed intelligent shaping vibration suppression control method can well suppress the process vibration and residual vibration of Delta robot, which can effectively improve the operation stability, work efficiency, and position accuracy of Delta robot. [ABSTRACT FROM AUTHOR]

To accurately, stably, and efficiently control the complex rigid‐flexible coupled robot, this paper takes Delta robot as the study object and decomposes it into slow subsystem and fast subsystem in different timescales by using singular perturbation principle, which the slow subsystem representing the rigid motion and the fast subsystem representing flexible vibration. In addition, the backstepping control system is designed for the slow subsystem, and the dynamic surface control system based on K‐observer is designed for the fast subsystem, and the overall control scheme is proposed by combining the backstepping control system and the dynamic surface control system, and the stability of the proposed overall control scheme is proven. Finally, the proposed control scheme is compared with proportional derivative control and trajectory control with workspace lattices, and the related experimental results are analyzed in details. [ABSTRACT FROM AUTHOR]