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To improve the efficiency of robots, a novel sustainable design of a robotic arm is proposed that can address the thermal, vibration, energy usage and dynamic stiffness issues. The proposed design utilises easy-to-produce carbon fibre tubes including some that are commercially available, for the robot link instead of traditional materials, which can be heavy and carbon intensive. The design involves arranging several carbon fibre reinforced polymer (CFRP) tubes in a specific array inside an epoxy resin substrate. Based on this design, a Finite Element Analysis (FEA) was implemented demonstrating the relatively low mass and high stiffness characteristics. Design optimisation was then performed using custom generated MATLAB® code to reduce the computation time. The proposed design for a one-meter-long robot link with a diameter of 108 mm offers a stiffness of 0.75 N/μm. A structural steel link, with the same stiffness, is 33 % heavier than the concept design. The design maintains thermal stability with about a third of the thermal deformation seen in structural steel. [ABSTRACT FROM AUTHOR]

Small–size (desktop) robot arms are being increasingly used in research and education, where low–cost implementation prevails over the need for high dynamic performance. This paper presents a design of a control unit for a 6DoF desktop robot arm with cylindrical joints actuated with stepper motors. Feedforward control, often chosen as a control strategy for stepper motors due to the simplicity of control algorithms, as well as the cost–effectiveness of the solution, is selected as the control method. The applicative software with GUI developed in Matlab is presented. A trajectory planner based on the solution of the inverse kinematics problem and the user–selected joint velocity profile is implemented. In order to avoid step skipping occurrence, the torque in joints necessary to produce the programmed joints motion have to be achievable by the installed stepper motors. As a part of applicative software, the check on the feasibility of the programmed robot trajectories by numerical simulation of solution of inverse dynamics problem in Simscape Multibody is performed. The microcontroller is used as an interface device that controls the stepper motor drivers. The applicative software communicates with the microcontroller using the TCP protocol. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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, a support vector machine (SVM) model is trained to classify objects in the automatic sorting system using a robot arm. The robot arm is used to grab objects and move them to the right position according to their shape predicted by the SVM model. The position of objects in the image is identified by using the contouring technique. The centroid of objects is calculated from the image moment of the object's contour. The calibration is conducted to get the parameters of the camera and combine with the pinhole camera model to compute the 3D position of the objects. The feature vector for SVM training is the zone feature and the SVM kernel is the Gaussian kernel. In the experiment, the SVM model is used to classify four objects with different shapes. The results show that the accuracy of the SVM classifier is 99.72%, 99.4%, 99.4% and 99.88% for four objects, respectively. [ABSTRACT FROM AUTHOR]

This manuscript presents an efficient algorithm for solving the inverse kinematics problem of a 6R robot manipulator to be deployed on embedded control hardware. The proposed method utilizes the geometric relationship between the end-effector and the base of the manipulator, resulting in a computationally efficient solution. The approach aims to minimize computational complexity and memory consumption while maintaining the accuracy and real-time performance demonstrated by simulations and verified by experimental results on an embedded system. Furthermore, the manipulator is analyzed in terms of singularities, limits, the workspace, and general solvability. Due to the simplicity of the algorithm, a platform-independent implementation is possible. As a result, the average calculation time is reduced by a factor of five to eight and the average error is decreased by a factor of fifty compared to a powerful analytical solver. [ABSTRACT FROM AUTHOR]

In this paper, the Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm is employed to enable a double-jointed robot arm to reach continuously changing target locations. The experimentation of the algorithm is carried out by training an agent to control the movement of this double-jointed robot arm. The architectures of the actor and cretic networks are meticulously designed and the DDPG hyperparameters are carefully tuned. An enhanced version of the DDPG is also presented to handle multiple robot arms simultaneously. The trained agents are successfully tested in the Unity Machine Learning Agents environment for controlling both a single robot arm as well as multiple simultaneous robot arms. The testing shows the robust performance of the DDPG algorithm for empowering robot arm manoeuvring in complex environments. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to quickly and stably achieve grasping objects with a 3D robot arm controlled by electrooculography (EOG) signals. A EOG signal is a biological signal generated when the eyeballs move, leading to gaze estimation. In conventional research, gaze estimation has been used to control a 3D robot arm for welfare purposes. However, it is known that the EOG signal loses some of the eye movement information when it travels through the skin, resulting in errors in EOG gaze estimation. Thus, EOG gaze estimation is difficult to point out the object accurately, and the object may not be appropriately grasped. Therefore, developing a methodology to compensate, for the lost information and increase spatial accuracy is important. This paper aims to realize highly accurate object grasping with a robot arm by combining EMG gaze estimation and the object recognition of camera image processing. The system consists of a robot arm, top and side cameras, a display showing the camera images, and an EOG measurement analyzer. The user manipulates the robot arm through the camera images, which can be switched, and the EOG gaze estimation can specify the object. In the beginning, the user gazes at the screen's center position and then moves their eyes to gaze at the object to be grasped. After that, the proposed system recognizes the object in the camera image via image processing and grasps it using the object centroid. The object selection is based on the object centroid closest to the estimated gaze position within a certain distance (threshold), thus enabling highly accurate object grasping. The observed size of the object on the screen can differ depending on the camera installation and the screen display state. Therefore, it is crucial to set the distance threshold from the object centroid for object selection. The first experiment is conducted to clarify the distance error of the EOG gaze estimation in the proposed system configuration. As a result, it is confirmed that the range of the distance error is 1.8–3.0 cm. The second experiment is conducted to evaluate the performance of the object grasping by setting two thresholds from the first experimental results: the medium distance error value of 2 cm and the maximum distance error value of 3 cm. As a result, it is found that the grasping speed of the 3 cm threshold is 27% faster than that of the 2 cm threshold due to more stable object selection. [ABSTRACT FROM AUTHOR]

When industrial robots transfer objects at high speeds, residual vibrations occur at the target positions. Recently, autonomous mobile robots such as AGV and AMR have become popular. However, the vibrations of the objects to be transported occur when the robot stops moving. Waiting until the residual vibration converges is necessary during the installation and assembly of transported objects. Thus, the overall work time increases even though the robots operate and move at a high speed. If the trajectory, which is the change in the position and attitude of the output point of a robot with time, can be adjusted such that the robot can move and operate within the same operation time by suppressing the residual vibration caused in transported objects, the work time can be significantly reduced. However, suppressing vibration is not easy because it usually requires solving complex equations of motion to consider the dynamics of robots. Therefore, in this study, based on the behavior of an object to be transported, which is measured by actually operating a robot, we propose a method for determining a trajectory that can suppress residual vibration using a heuristic algorithm without performing a kinetic analysis. The trajectories determined by this method reduced the settling time by approximately 90% with only a few dozen robot operations. The proposed method can be widely applied to commercial robots because appropriate trajectories are generated using a simple program that determines the variables used in the robot language without kinetic analysis. • We proposed the method for generating the trajectory of the robots using results measured in actual operation. • The trajectories suppress the residual vibration of the carried object by the heuristic algorithm. • The proposed method is widely applied to the commercial robot because the variables in the robot language are used. [ABSTRACT FROM AUTHOR]

This paper proposes a simple mathematical model based on the variable fractional-order difference equation of a robot arm. The model of the described arm does not consider the impact of the movement of the mobile platform, it was assumed that all degrees of freedom would be taken away from it. The implementation of the task was divided into two stages. First, a mechanical model was developed. In order to estimate the torques of nodal propulsion motors, a description of the components of the Lagrange equation for the considered system, i.e., energy, power, and external interactions, and derivation of the equations of motion of the tested manipulator based on the Lagrange equation was made. An additional criterion was also considered in the selection of drives in the kinematic nodes of the links, which was to set the manipulator in a vertical position at a specific time. Processing the measured data of a robot arm, model parameters were selected, and the order function was chosen. The second stage was a simulation, whose results were compared with the collected data. [ABSTRACT FROM AUTHOR]

Models trained with Deep Reinforcement learning (DRL) have been deployed in various areas of robotics with varying degree of success. To overcome the limitations of data gathering in the real world, DRL training utilizes simulated environments and transfers the learned policy to real-world scenarios, i.e., sim–real transfer. Simulators fail to accurately capture the entire dynamics of the real world, so simulation-trained policies often fail when applied to reality, termed a reality gap (RG). In this paper, we propose a search (mapping) algorithm that takes in real-world observation (images) and maps them to the policy-equivalent images in the simulated environment using a convolution neural network (CNN) model. The two-step training process, DRL policy and a mapping model, overcomes the RG problem with simulated data only. We evaluated the proposed system with a gripping task of a custom-made robot arm in the real world and compared the performance against a conventional DRL sim–real transfer system. The conventional system achieved a 15–57% success rate in gripping operation depending on the position of the target object while the mapping-based sim–real system achieved 100%. The experimental results demonstrated that the proposed DRL with mapping method appropriately corresponded the real world to the simulated environment, confirming that the scheme can achieve high sim–real generalization at significantly low training costs. [ABSTRACT FROM AUTHOR]

Copyright of Journal Of Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban is the property of Editorial Department of Journal of Sichuan University Natural Science Edition 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.)

To tackle the jittering of a robot arm-tip, a novel method of the jittering mitigation is proposed. The key idea is to transmit the jittering to vibration of a mass block nested inside the jittering-mitigator. This method only requires the frequency of the jittering at the arm-tip. To verify the practicability of the method, a practical robot was applied through simulations and physical experiments. The jittering mitigator device can be directly attached to or detached from the arm-tip. The jittering at the arm-tip was first measured through experiments using accelerometers connected to the vibration and noise testing system. Then, the dominant frequency was identified through fast Fourier transform analysis. The theoretical parameters of the jittering mitigator were calculated accordingly. A model was established to simulate the jitter reduction effect of the mitigator. The results revealed a 68% reduction in the average amplitude of the jittering vibration at the arm-tip, which is corroborated by the experiment results. The proposed method could be applicable to all types of robots theoretically because it only requires computing the frequency of jitter. [ABSTRACT FROM AUTHOR]

This article presents an exploratory study aimed at: (i) understanding participants' first impression of the Cyton Gamma 1500, a small machine-like robot arm, and (ii) designing robot body language to convey certain intentions with regard to a personal assistant robotic application. The between-group study comprised of three distinct groups of participants. Each group had a different first encounter with the robot arm: the first group saw an idly sitting robot arm; the second group watched another person working with the robot; lastly, the third group worked with the robot themselves. They rated the robot arm on a bipolar adjective questionnaire after the first encounter which gives us insights into participant's "first impression". Then they performed a categorization task where they chose the most suitable posture for conveying specific messages. The objective here was to select postures that can easily convey the robot assistant's intentions to users in future. The two main findings of the study are: (1) participants' impression of the robot arm does not differ significantly across the three groups despite having different "first encounter" with the robot, and (2) certain postures are easily relatable to the message they convey (like, Robot is saying "Hi!"), while certain others are not (like, Robot has surrendered). In light of phenomenological theories of communication, it was concluded that tempered forms of anthropomorphic or zoomorphic postures are more easily identified than completely abstract postures. The findings of this study can be used to design robot-specific body language for robot arms. [ABSTRACT FROM AUTHOR]

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.)

Due to structural complexity of robot arms, constraints of experiments, especially uncertainty of design parameters, numerical models for dynamics analysis of robot arms can produce erroneous results, which can seriously affect the performance of the designed robot arms. Reliable parameter uncertainty identification for robot arms becomes important. The current methods for uncertainty analysis have double-layered processes, in which the inner layer is for uncertainty propagation and the outer layer is an iterative optimization process. Such a nested double-layered approach limits computational efficiency. This work proposes a novel inverse method for parameter uncertainty identification using a two-way neural network. First, an element (FE) model of a robot arm is established and validated experimentally. Sensitivity analysis is then conducted using the FE model to determine a set of major parameters to be identified. A two-way neural network is next established, and the explicit formulae of direct weight inversion (DWI) use to inverse these parameters of the robot arm. Finally, the inverse result is validated by experiments. Our study show that the present inverse method can greatly improve the computational efficiency. It provides a new avenue to tackle complex inverse problems in engineering and sciences. [ABSTRACT FROM AUTHOR]

Path planning for robot arms to reach a target and avoid obstacles has had a crucial role in manufacturing automation. Although many path planning algorithms, including RRT, APF, PRM, and RL-based, have been presented, they have many problems: a time-consuming process, high computational costs, slowness, non-optimal paths, irregular paths, failure to find a path, and complexity. Scholars have tried to address some of these issues. However, those methods still suffer from slowness and complexity. In order to address these two limitations, this paper presents a new hybrid path planning method that contains two separate parts: action-finding (active approach) and angle-finding (passive approach). In the active phase, the Q-learning algorithm is used to find a sequence of simple actions, including up, down, left, and right, to reach the target cell in a gridded workspace. In the passive phase, the joints angles of the robot arm, with respect to the found actions, are obtained by the trained neural network. The simulation and test results show that this hybrid approach significantly improves the slowness and complexity due to using the simplified agent-environment interaction in the active phase and simple computing the joints angles in the passive phase. [ABSTRACT FROM AUTHOR]

Time-dependent nonlinear equation (TDNE) arises in numerous engineering applications. Recently, zeroing neural network (ZNN) has been proven to be an effective alternative for solving the TDNE. In this paper, we present a new solution to the TDNE by using the discrete-time ZNN (DTZNN). Specifically, a special difference formula is first constructed via Taylor series expansion. Then, by utilizing such a formula to discretize the existing continuous-time ZNN model, the new DTZNN model is proposed to determine the TDNE solution. Theoretical analysis and numerical results further indicate the validity and superiority of the proposed DTZNN model in comparison with the previous models. Finally, the DTZNN practicality is presented by the simulation and experiment on the DOBOT arm using the proposed model. • A special difference formula is constructed via Taylor series expansion. • A novel DTZNN model is proposed to solve TDNE, unreported previously. • Results indicate the quintic error mode of the proposed DTZNN model in solving TDNE. • The DTZNN's error mode is O (δ 5) , i.e., SSCE decrease by 100000x with δ decreasing by 10x. • The proposed DTZNN model's practicality is shown by the experiment on the DOBOT arm. [ABSTRACT FROM AUTHOR]

• The manuscript investigates a digital twin of a robot arm typical of manufacturing processes. • This explores the virtual construction of the training and evaluation environments as well as the physical equivalency of this digital twin. • We train, using reinforcement learning, a robot arm to perform a task in virtual space and then map this learnt behaviour to a physical counterpart whilst illustrating linkages between the halves of the digital twin. [Display omitted] For many industry contexts, the implementation of Artificial Intelligence (AI) has contributed to what has become known as the fourth industrial revolution or "Industry 4.0" and creates an opportunity to deliver significant benefit to both businesses and their stakeholders. Robot arms are one of the most common devices utilised in manufacturing and industrial processes, used for a wide variety of automation tasks on, for example, a factory floor but the effective use of these devices requires AI to be appropriately trained. One approach to support AI training of these devices is the use of a "Digital Twin". There are, however, a number of challenges that exist within this domain, in particular, success depends upon the ability to collect data of what are considered as observations within the environment and the application of one or many trained AI policies to the task that is to be completed. This project presents a case-study of creating and training a Robot Arm Digital Twin as an approach for AI training in a virtual space and applying this simulation learning within physical space. A virtual space, created using Unity (a contemporary Game Engine), incorporating a virtual robot arm was linked to a physical space, being a 3D printed replica of the virtual space and robot arm. These linked environments were applied to solve a task and provide training for an AI model. The contribution of this work is to provide guidance on training protocols for a digital twin together with details of the necessary architecture to support effective simulation in a virtual space through the use of Tensorflow and hyperparameter tuning. It provides an approach to addressing the mapping of learning in the virtual domain to the physical robot twin. [ABSTRACT FROM AUTHOR]

As systems become increasingly complex, their simulation techniques have to be more accurate and enhanced. Despite the wide use of robotic arms in industries, they still encompass a wide number of complexities. The control of a flexible robot arm driven by a Brushless DC Motor (BDCM) for tracking problems is a great challenge, not only for its complex algorithms but also for its verification process. Robotic systems are designed heterogeneously by combining continuous and event discrete models. Therefore, computer-aided engineering tools have to be enhanced in order to support the verification of the control algorithms. Ensuring definite and rapid simulations is a challenging task for robotic application systems. Although control strategies can be tested using the Matlab/Simulink environment to assess their performance, their verification at a low-level still remains a very difficult task. This paper studies different simulation techniques based on Model In the Loop (MIL), Hardware In the Loop (HIL), and Hardware Software In the Loop (HSIL) on a flexible robot arm driven by BDCM in order to overcome each method's limits, focusing on performance and cost and simulation time reduction. The HSIL achieves the highest accuracy and speed than MIL and HIL and provides reusability and portability of the control unit compared to the other techniques. [ABSTRACT FROM AUTHOR]

Electro-hydrostatic actuator (EHA) has backdrivability and is suitable for a robot system that cooperates with human. However, its design methodology to miniaturize and to generate high pressure has not been established. We develop a robotic arm, all joints of which are actuated by EHAs. This paper reports the development of the vane motor type EHA and a wrist mechanism using the EHA. We employ a single vane motor for a wide motion range. In order to suppress internal leakage, we carefully set the clearance of the EHA and made the rigid casing by using ceramics disks. It is confirmed that the internal leakage of the new EHA is significantly reduced compared to that of the previous model. The wrist mechanism has three degrees of freedom, consisting of three vane motors in series. The flow path between a hydraulic pump and a vane motor is designed using metal pipings, swivel joints and manifolds are presented for a compact wrist mechanism. We validated the fundamental performance of the developed wrist mechanism through experiments. [ABSTRACT FROM AUTHOR]

In the design optimization of robot arms, the use of simulation technologies for modeling and optimizing the objective functions is still challenging. The difficulty is not only associated with the large computational cost of high-fidelity structural simulations but also linked to the reasonable compromise between the multiple conflicting objectives of robot arms. In this paper we propose a surrogate-based evolutionary optimization (SBEO) method via a global optimization approach, which incorporates the response surface method (RSM) and multi-objective evolutionary algorithm by decomposition (the differential evolution (DE) variant) (MOEA/D-DE) to tackle the shape design optimization problem of robot arms for achieving high speed performance. The computer-aided engineering (CAE) tools such as CAE solvers, computer-aided design (CAD) Inventor, and finite element method (FEM) ANSYS are first used to produce the design and assess the performance of the robot arm. The surrogate model constructed on the basis of Box–Behnken design is then used in the MOEA/D-DE, which includes the process of selection, recombination, and mutation, to optimize the robot arm. The performance of the optimized robot arm is compared with the baseline one to validate the correctness and effectiveness of the proposed method. The results obtained for the adopted example show that the proposed method can not only significantly improve the robot arm performance and save computational cost but may also be deployed to solve other complex design optimization problems. [ABSTRACT FROM AUTHOR]

Robots have increasingly replaced humans for many jobs, including 24 h work, routine tasks, and dangerous jobs. However, the robot operating system has high power consumption in many processes. This has led to energy efficiency being the main focus. We have opted to build a robot with high strength, light weight, and low power consumption by reducing the weight of its components. Presently, we know that the structure of most robots in the world is made of metals, plastics, and composite materials. In this research, we designed the mechanical structure of robot arms with three different materials (cast iron, polyamide, and aluminum) using the finite element method to analyze and evaluate the possibilities of these materials. The dynamic load, power consumption, and mechanical characteristics were compared. It was found that polyamide could help lighten the weight by 40% and increase energy efficiency along with cost effectiveness by 41%. Although polyamide is particularly easy to find, cast iron is stronger than polyamide. [ABSTRACT FROM AUTHOR]

In this research, the author presents the model of the two-link robot arm and its dynamic equations. Based on these dynamic equations, the author builds the sliding mode controller for each joint of the robot. The tasks of the controllers are controlling the Torque in each Joint of the robot in order that the angle coordinates of each link coincide with the desired values. The proposed algorithm and robot model are built on Matlab-Simulink to investigate the system quality. The results show that the quality of the control system is very high: the response angles of each link quickly reach the desired values, and the static error equal to zero. [ABSTRACT FROM AUTHOR]

The development of an algorithm to describe a dynamic system and to predict its future behaviour in further consequence is the aim of the present study. Non parametric models provide a general description of object dynamics and artificial neural networks (ANN), which are a very flexible and universal learning method, belong to it. However, the standard estimation procedures for ANN like Levenberg-Marquardt (LM) do not consider that data is observed and consequently is uncertain. The combination with the extended Kalman filter (EKF) enables the consideration of the uncertainty in the estimation process. The analogies between EKF and LM are discussed and thereon the advantages of the EKF are outlined. The integration of ANN into EKF will be evaluated on an industrial robot arm. At first, a simplified model is determined; the ANN describes the robot position deviations as a function of the joint encoder values. The robot reference positions are measured by a laser tracker. In order to compare it with the robot outputs, the observations need to be transformed to the robot frame and the offset between the end-effector and the robot flange has to be determined. A method to estimate both parameters simultaneously is developed and the results are verified on basis of simulated data. This paper comprises two novel approaches. First, uncertainty is considered in the ANN estimation on basis of the combination with the EKF. Considering the full covariance matrix of the robot deviations leads to a better prediction of the robot's behaviour. Second, an integrated transformation and lever arm determination is introduced and the robot's repeatability presents the limiting factor of the achievable parameter uncertainty. [ABSTRACT FROM AUTHOR]

In this paper, a robust nonlinear tracking control design for a bio-inspired robot arm with human-like motion mechanism is investigated, and the bio-inspired operator controller based on human multi-joint viscoelastic properties is designed by using operatorbased robust right coprime factorization approach. The motion mechanism of human multi-joint arm is used, and the measurement uncertainties of human multi-joint arm viscoelasticity are considered in designing bio-inspired operator controller. Based on the proposed design scheme, the sufficient conditions for the robust stability are derived in considering the coupling effects and measurement uncertainties, and the output tracking performance is realized. The effectiveness of the proposed design scheme was confirmed by the simulation results based on experimental data, and the time-varying estimated experimental data of human multi-joint arm viscoelasticity is used in simulation. [ABSTRACT FROM AUTHOR]

Abstract Collaborative robots that share work spaces with human workers should be equipped with joint brakes to safely stop the movement in an emergency situation. Currently, the joint brakes that are used in the field have disadvantages of high cost, heavy weight, or inability to handle stop category 0, which is necessary in extreme situations such as power outage. In order to overcome these problems, we developed a joint brake constructed with a solenoid and an elastic latch module using coil springs. The brake is lightweight and low-cost, but it exhibits good braking performance and durability in the emergency involving stop category 0. In this study, we explain the principle and structure of the developed joint brake, select components based on dynamics simulation, and verify braking performance and durability by repetitive emergency stop experiments on applying stop category 0. [ABSTRACT FROM AUTHOR]

The paper presents some building schemas of manipulator - robots used in auxiliary operations of electric arc furnaces that are found in steel works. The technology of making steel in electric arc furnaces requires that the liquid steel outlet is filled following the primary elaboration. An "EBT SAND" type manipulator is used to fill the steel outlet, the kinematic scheme of which corresponds to SCARA robots. To unlock the steel outlet, an "EBT STAP" type manipulator is used, which is equipped with an oxygen lance to open the tap-hole. To clean the furnace outlet following the discharge, an "EBT STROP" type manipulator is used to allow the correct reconditioning of the tap-hole. It consists of an electrically driven swing arm and a hydraulic lift that cleans the EBT tap-hole. [ABSTRACT FROM AUTHOR]

The entire semiconductor manufacturing process can be largely divided into two sequential sub-processes that are commonly referred to as front-end and back-end production. Both production processes involve many complicated and elaborate steps for wafer fabrication, probe testing and sorting, assembly, final test, etc. The front-end process refers to wafer fabrication process, whereas the back-end process refers to the assembly, packaging, and testing of individual semiconductor devices. Once the front-end process is complete, the wafers are transferred to the back-end process in order to facilitate its integration into electronic devices and to take final performance test. In back-end production process, based on quality and location information of individual chip (die) derived from the EDS process, only good semiconductor chips (dies) are individually picked up by an automatic robot arm and then attached to the support structure (e.g., the lead frame) on a strip. This process is called die attach process. In order to improve the production efficiency of die attach process, it is necessary to evaluate various robot arm operation methods (rules) and then to provide the most efficient operation method since the total transfer distance (or total transfer time) considerably depends on the robot arm operation methods as well as the distribution of good chips on wafers. Thus, in this research, a pick-and-place problem of the die attach process is mathematically formulated and several transfer methods are compared. [ABSTRACT FROM AUTHOR]

This paper presents an application of optical word recognition and fuzzy control to a smartphone automatic test system. The system consists of a robot arm and two webcams. After the words from the control panel that represent commands are recognized by the robot system, the robot arm performs the corresponding actions to test the smartphone. One of the webcams is utilized to capture commands on the screen of the control panel, the other to recognize the words on the screen of the tested smartphone. The method of image processing is based on the Red-Green-Blue (RGB) and Hue-Saturation-Luminance (HSL) color spaces to reduce the influence of light. Fuzzy theory is used in the robot arm's position control. The Optical Character Recognition (OCR) technique is applied to the word recognition, and the recognition results are then checked by a dictionary process to increase the recognition accuracy. The camera which is used to recognize the tested smartphone also provides object coordinates to the fuzzy controller, then the robot arm moves to the desired positions and presses the desired buttons. The proposed control scheme allows the robot arm to perform different assigned test functions successfully. [ABSTRACT FROM AUTHOR]

In this study computer vision and robot arm are used together to design a smart robot arm system which can identify objects from images automatically and perform given tasks. A serving robot application, in which specific tableware can be identified and lifted from a table, is presented in this work. A new database was created by using images of objects used in serving a meal. This study consists of two phases: First phase includes recognition of the objects through computer vision algorithms and determining the specified objects' coordinates. Second phase is the realization of the robot arm's movement to the given coordinates. Artificial neural network is used for object recognition in this system. 98.30% overall accuracy of recognition is achieved. Robot arm's joint angles were calculated by using coordinate dictionary for moving the arm to desired coordinates and the robot arm's movement was performed. [ABSTRACT FROM AUTHOR]

A novel dual recurrent neural network is presented and is used to identify the dynamics for a robot arm with three- Degrees of freedom (DoF) and trained with a filtered error algorithm. The dual neural network has a structure of two recurrent neural networks working simultaneously, fighting each other to obtain the best identification values, being the criteria for the selection of the vest values: the standard deviation for the identification error. The neural identifier provides important information to a nonlinear block control transformation form acting as a control law to solve the trajectory tracking problem for the robotic plant's behavior. [ABSTRACT FROM AUTHOR]

Copyright of Revista Tecno Lógicas is the property of Instituto Tecnologico Metropolitano 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.)

Abstract: This paper describes an assistive robot application that combines a portable wireless interface based on electrooculography (EOG) and Radiofrequency Identification (RFID) technology. This assistive application is aimed at handicapped users who suffer from a severe motor disability. To that end, a realistic application has been designed. It consists of an environment in which users can bring a glass and a water bottle closer with only the help of their eye movement using a real robot arm. RFID will be used as a support to the EOG interface in a shared control architecture by storing information of the objects in tags placed on the scene. Five volunteers tested the assistive robot application. The results obtained show that all of them were able to finish the tests in a suitable time and the results improved with practice and training. This proves that the assistive robot application can be a feasible way to help handicapped users. [Copyright &y& Elsevier]

This paper presents an analysis and performance evaluation of the proportional-derivative (PD) fuzzy logic controller design by using Matlab and field programmable gate array (FPGA). The fuzzy logic controller consists of a Fuzzifier, inference engine and Defuzzifier; the Fuzzifier block accepts two PD inputs. Two types of controller are designed; the first one is using fuzzy logic toolbox in Matlab. The second type is designed using VHDL language for implementation on FPGA. Mathematical models of robot arm and bench-top helicopter are used for the purpose of simulation with the first type. This controller is used with a unity feedback control system in Matlab Simulink, in order to control these systems and to generate the simulation results. The best response with the robot arm has 0.02 errors and zero overshot, and the best response with the bench-top helicopter has 0.01 error with 0.001 overshot. Altera Quartus II and ModelSim simulation program are used to generate the simulation results of the second type. A mathematical model that represents industrial processes, such as temperature, pressure, pH, and fluid-level controls with unity feedback control systems and subjected to 0.2 step input is used to generate these results. This FPGA-based controller is able to produce a fast response ranging from 0.3 μs, even with time delay added with the plant model. [ABSTRACT FROM AUTHOR]

The paper focuses on design and control of a new anthropomorphic robot arm enabling the torque measurement in each joint to ensure safety while performing tasks of physical interaction with human and environment. When the contact of the robot arm with an object occurs, local admittance algorithm provides active compliance of corresponding robot arm joint. Thus, the whole structure of the manipulator can safely interact with an unstructured environment. The detailed design procedure of the 4-DOF robot arm and optical torque sensors is described in the paper. The experimental results of joint admittance control revealed the feasibility of the proposed approach to provide safe interaction of entire structure of robot arm with a person. The control system with load angle position feedback and lead compensator is proposed to improve dynamic behavior of flexible joint arm. The experimental results show high performance of the developed controller in terms of successful damping of vibrations. [ABSTRACT FROM AUTHOR]

This paper describes the development of a desktop robotic system that enables the plug-and-play function through the USB (universal serial bus) port of a personal computer (PC). Thus a new kind of desktop PC peripheral is invented that has programmable manipulability. The robotic system is realized on an internally distributed control structure that facilitates higher system reliability. A PID control algorithm is implemented on a prototype of the proposed system, to demonstrate the system's ability to implement feedback control. Experimental results show the performance and properties of the proposed system. [ABSTRACT FROM AUTHOR]

Abstract: Electro-rheological (ER) actuators are potential devices in modern industrial applications. This is due to the excellent performances of the ER devices such as large output dynamics and fast speeds of responses. By using its fast dynamic responses, the ER device or ER brake is studied as a possible actuator to halt the robot arm rapidly. Although many research studies of the ER brake were conducted in the past, but the details of the ER braking dynamics is not clearly understood. Therefore in this paper, a mathematical model is developed and the modelled results are compared with the experimental output velocity responses of the ER brake. After the model validation is completed, the ER brake is subjected to various loads, electric fields, fluid temperatures and volume fractions. By observing the effect of each input variable, the trend behaviour of the ER braking velocity response is investigated in a bid to identify suitable parameters that can halt the robot arm at the fastest time durations. In addition, the braking displacement responses of the attached loads are examined in order to understand the variations of the load position errors at different input conditions. This study is aimed to establish the fundamental knowledge of using the ER brake in future ER-robotic applications. [Copyright &y& Elsevier]

Abstract: This paper presents the design of a robot-based detector manipulator for microdiffraction applications with a hard X-ray nanoprobe instrument system being constructed at the Advanced Photon Source (APS) for the Center for Nanoscale Materials (CNM) being constructed at Argonne National Laboratory (ANL). Applications for detectors weighing from 1.5 to 100kg were discussed in three configurations. [Copyright &y& Elsevier]

• Control of redundantly actuated robot arms driven by muscle-like actuators is addressed. • New method to find the actuator forces producing the desired behavior is provided. • The method enables us to control the stiffness independently from the joint toque. • The method is based on a direct sum decomposition of the actuator force space. • Conditions for the actuator force space to be split into the direct sum are provided. The aim of this study is to solve the force distribution problem for a special class of musculoskeletal robot arms. The proposed method expresses a vector of actuator forces as the direct sum of three components: one produces control torque, another joint stiffness, and the other neither of them. The decomposition allows us to find the actuator force vector producing desired joint stiffness independently from the actuator force vector generating desired control torque. The ultimate goal of this research is to develop a robot arm that possesses a human-like stiffness property. To facilitate the controller design, we make the following assumptions: the robot arm consists of two links and six muscle-like actuators; the moment arms of the actuators are fixed; the force generation of the actuators is described by a special Hill-type model; the actuators can pull and push. This paper formulates the conditions for the actuator force vector space to be decomposed into the direct sum and provides a particular solution to the distribution problem. The validity of the proposed method is demonstrated by using numerical simulations. [ABSTRACT FROM AUTHOR]

If a collaborative robot can measure contact force when in contact with the environment or humans, it can achieve better performance on collision detection, hand guiding, and force control. In order to achieve this, joint torque sensors are often mounted on each joint of a robot arm. However, a torque sensor mounted between the harmonic drive and the robot link suffers from crosstalk problems due to loads other than the torque of interest as well as the deformation of the harmonic drive during its power transmission. In addition, the torsional stiffness of a torque sensor itself tends to lower the overall stiffness of the arm, thereby affecting the accuracy of the robot. In this study, we propose a novel design of the strain gauge-based joint torque sensor that overcomes the shortcomings of conventional torque sensors. It is experimentally shown in various ways that the proposed joint torque sensor can properly deal with the crosstalk and thus accurately measure the torque of interest. Furthermore, it is found that the torsional stiffness of the sensor is large enough to minimize a reduction in overall arm stiffness, and its ring-type structure allows the sensor to be used without increasing the volume of the joint module. [ABSTRACT FROM AUTHOR]