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ObjectiveTo investigate the influence of acupuncture lifting-thrusting frequency and amplitude on the analgesic effects, and its correlation with mast cell degranulation.MethodsAcute adjuvant arthritis (AA) rat models were employed. Robot-arm aided lifting-thrusting acupuncture therapy was conducted with various frequencies (ranging from 0.5 to 4 Hz) and amplitudes (ranging from 0.5 to 2.0 mm). The rats’ pain thresholds were measured multiple times before and after the therapy, and the analgesic effects were evaluated using the pain threshold recovery rate (PTRR), a normalized index. The mast cell degranulation rate (MCdR) at the acupoint was calculated, and a correlation analysis between PTRR and MCdR was performed.ResultsAcupuncture therapy partially restored the pain threshold affected by arthritis. The analgesic effects were influenced by stimulus frequency and amplitude, with best outcomes occurring at an intermediate optimal frequency of 1.0 Hz and amplitude of 1.0–1.5 mm. Similarly, the MCdR peaked at the optimal frequency and amplitude.ConclusionOur animal experiment suggests that optimal analgesic effects can be achieved with stimulation at an optimal intensity. This intensity-effect correlation appears to originate from mast cell activation rates under different mechanical stimulus.

In this paper, we propose a solution of designing and building a three-order 4D magician robot arm to palletize goods. Inverse kinematic of system is calculated. Thence, we design a suitable robot arm which can utilizing camera to palletizing goods. PID controller is used to move operating point. Simulation and experimental results are shown to prove the well operation of this robot arm.

Abstract Within this interdisciplinary study, we demonstrate the applicability of a 6D printer for soft tissue engineering models. For this purpose, a special plant was constructed, combining the technical requirements for 6D printing with the biological necessities, especially for soft tissue. Therefore, a commercial 6D robot arm was combined with a sterilizable housing (including a high‐efficiency particulate air (HEPA) filter and ultraviolet radiation (UVC) lamps) and a custom‐made printhead and printbed. Both components allow cooling and heating, which is desirable for working with viable cells. In addition, a spraying unit was installed that allows the distribution of fine droplets of a liquid. Advanced geometries on uneven or angled surfaces can be created with the use of all six axes. Based on often used bioinks in the field of soft tissue engineering (gellan gum, collagen, and gelatin methacryloyl) with very different material properties, we could demonstrate the flexibility of the printing system. Furthermore, cell‐containing constructs using primary human adipose‐derived stem cells (ASCs) could be produced in an automated manner. In addition to cell survival, the ability to differentiate along the adipogenic lineage could also be demonstrated as a representative of soft tissue engineering.

The field of robotics and autonomous systems has witnessed significant advancements in recent years, with an increasing focus on enhancing the capabilities of robotic agents through RL. This project centres around applying Reinforcement Learning (RL) techniques to do object manipulation tasks with a specific focus on making a robot arm reach a target object. Using a combination of Gazebo and Robot Operating System (ROS) environments, the robot arm is trained using custom OpenAI Gym environments to simulate the task. The primary objective involves positioning the end-effector of the robot arm close to a designated object and overcoming challenges such as self-collisions during movements. Various iterations of RL training, including different reward logics, curriculum learning approaches, and fine-tuning parameters, are explored to refine the decision-making capabilities of the agent. The training process of Curriculum Learning involves a phased approach, starting with basic movements and progressing to more complex tasks, demonstrating improved performance. However, challenges such as prolonged training times and uncertainties in arm behaviour persist. The project highlights the complexities inherent in designing effective RL strategies for robotic systems and stresses the need for further research to enhance computational efficiency and reliability in real-world applications.

Aiming at the problem that human beings are prone to fatigue when they repeatedly perform a single action in the performance test of the driver attention monitoring system, a humanoid robot arm is designed to simulate the driver's behaviors such as answering and making handsets and smoking. Firstly, based on the exoskeleton design method, the structure of the robot arm and the bionic hand is designed. Secondly, the improved D-H method is used to establish the kinematics model of the manipulator; the forward and inverse kinematics are solved, and the workspace is analyzed; the dynamics simulation of the robot arm is carried out in Adams software to obtain its motion characteristics and load characteristics. The simulation results show that the structural design of the manipulator is reasonable, and the selection of joint flexible actuators meets the requirements.

The precision of robotic manipulators in the industrial or medical field is very important, especially when it comes to repetitive or exhaustive tasks. Geometric deformations are the most common in this field. For this reason, new robotic vision techniques have been proposed, including 3D methods that made it possible to determine the geometric distances between the parts of a robotic manipulator. The aim of this work is to measure the angular position of a robotic arm with six degrees of freedom. For this purpose, a stereo camera and a convolutional neural network algorithm are used to reduce the degradation of precision caused by geometric errors. This method is not intended to replace encoders, but to enhance accuracy by compensating for degradation through an intelligent visual measurement system. The camera is tested and the accuracy is about one millimeter. The implementation of this method leads to better results than traditional and simple neural network methods.

The paper presents the development of a new platform that represents a robotic arm system, useful and appropriate for the Additive Manufacturing applications. The main objective of this work was to explore the feasibility of integrating the off-the-shelf (COTS) Additive Manufacturing technologies and the six-degree-of-freedom industrial robotic arm, achieving a 3D Additive Manufacturing system which is able to perform six-degree fused deposition printing. The authors investigated the materials suitable to be used and performed more experiments with the aim to find the right configuration of the printing system. Finally, the technical issues reported during the experimental programme have been solved, the system being prepared to carry out workpieces with more complex shapes and more types of polymers.

As the intelligent and information technology advancing rapidly, enterprises selling refined oil product become active in exploring the intelligent refueling robot technology, in attempt to solve the problems of recruitment difficulty, high labor costs, high working intensity and inadequate customer experience. The research systematically reviewed the development of refueling robots at home and abroad,discussed in detail the key technologies such as the refueling robot arm technology and its control system, end actuator, visual identification system, explosion-proof and intelligent interaction. The research made a statistical analysis by observing 2 000 times of refueling actions conducted by 40 refueling staff of varying proficiency and from different types of petrol stations. Then the research compared systematically the factors of efficiency and economy between the two service modes of manual filling and robot filling by taking into consideration the difference of petrol stations in different regions. The analysis results provide references for determining the research and development objectives and measuring commercialization conditions of the refueling robot. On this basis, the research proposed the objective of the application of refueling robots and development goals and the directions for improvements.

In order to enhance the synchronization of welding robot arms and improve welding quality, this study proposes a fuzzy PID-based improved deviation coupling multi-axis synchronous control method. Firstly, in response to the intricacies inherent in the compensation mechanism of the deviation coupling control structure and the substantial volume of system computation, the integration of average speed and sub-average speed is proposed to optimize the speed compensator. This integration aims to mitigate speed synchronization errors, minimize synchronization adjustment time, and elevate overall system synchronization performance. Moreover, the fuzzy PID algorithm is employed to design the controller to realize the single-motor adaptive control, leading to improvement in both system stability and dynamic response performance. Finally, a simulation model for six-axis synchronization control and an experimental platform were developed. Both simulation and experimental results demonstrate that the improved deviation coupling control method exhibits superior synchronization performance. The proposed multi-axis synchronous control method effectively heightens the synchronous performance of the six-degrees-of-freedom robotic arm.

ABSTRACTA gecko foot-inspired, mushroom-shaped artificial dry adhesive exploiting intermolecular forces between microstructure and surface has drawn research attention for its strong adhesive force. However, the high pull-off strength corresponding to the adhesive force matters when detaching fragile substrates. In this study, we report a vacuum-powered soft actuator having oblique air chambers and a dry adhesive. The soft actuator performs coupled contraction and twisting by applying negative pneumatic pressure inward and exhibits not only high pull-off strength but also easy detachment. This effective detachment can be achieved thanks to the twisting motion of the soft actuator. The detachment performances of the actuator models are assessed using a 6-degrees-of-freedom robot arm. Results show that the soft actuators exhibit remarkable pull-off strength decrement from ~20 N cm−2 to ~2 N cm−2 due to the twisting. Finally, to verify a feasible application of this study, we utilize the inherent compliance of the actuators and introduce a glass transfer system for which a glass substrate on a slope is gripped by the flexibility of the soft actuators and delivered to the destination without any fracture.

Robot arm monitoring is often required in intelligent industrial scenarios. A two-stage method for robot arm attitude estimation based on multi-view images is proposed. In the first stage, a super-resolution keypoint detection network (SRKDNet) is proposed. The SRKDNet incorporates a subpixel convolution module in the backbone neural network, which can output high-resolution heatmaps for keypoint detection without significantly increasing the computational resource consumption. Efficient virtual and real sampling and SRKDNet training methods are put forward. The SRKDNet is trained with generated virtual data and fine-tuned with real sample data. This method decreases the time and manpower consumed in collecting data in real scenarios and achieves a better generalization effect on real data. A coarse-to-fine dual-SRKDNet detection mechanism is proposed and verified. Full-view and close-up dual SRKDNets are executed to first detect the keypoints and then refine the results. The keypoint detection accuracy, PCK@0.15, for the real robot arm reaches up to 96.07%. In the second stage, an equation system, involving the camera imaging model, the robot arm kinematic model and keypoints with different confidence values, is established to solve the unknown rotation angles of the joints. The proposed confidence-based keypoint screening scheme makes full use of the information redundancy of multi-view images to ensure attitude estimation accuracy. Experiments on a real UR10 robot arm under three views demonstrate that the average estimation error of the joint angles is 0.53 degrees, which is superior to that achieved with the comparison methods.

The use of robot-assisted frameless stereotactic electroencephalography (SEEG) is becoming more common. Among available robotic arms, Stealth Autoguide (SA) (Medtronic, Minneapolis, MN, USA) functions as an optional instrument of the neuronavigation system. The aims of this study were to present our primary experiences with SEEG using SA and to compare the accuracy of implantation between SA and navigation-guided manual adjustment (MA). Seventeen electrodes from two patients who underwent SEEG with SA and 18 electrodes from four patients with MA were retrospectively reviewed. We measured the distance between the planned location and the actual location at entry (De) and the target (Dt) in each electrode. The length of the trajectory did not show a strong correlation with Dt in SA (Pearson's correlation coefficient [r] = 0.099, p = 0.706) or MA (r = 0.233, p = 0.351). De and Dt in SA were shorter than those in MA (1.99 ± 0.90 vs 4.29 ± 1.92 mm, p = 0.0002; 3.59 ± 2.22 vs 5.12 ± 1.40 mm, p = 0.0065, respectively). SA offered higher accuracy than MA both at entry and target. Surgical times per electrode were 38.9 and 32 min in the two patients with SA and ranged from 51.6 to 88.5 min in the four patients with MA. During the implantation period of 10.3 ± 3.6 days, no patients experienced any complications.

The study presents a design alternative, simulates the operation, and analyzes the new structure of the 6 DOF robot arm. The proposed robot model is based on reference shapes from AR3 robot arm, with modifications to details and structures to accommodate the actual and economic circumstances in Vietnam. Solidwork software was first utilized for the arm structure's design, strength testing, and optimization. The material is chosen with the goal of reducing production cost and ensuring the robot's operation properly. The study concludes by applying the topology optimization algorithm to a machine component to determine the optimal structure of the robot arm. To validating the design results, the prototype is manufacturers using CNC machine. The experimental results show the robot arm basic functions effectively. This research could be used as references for designing and manufacturing a 6 DOF robot arm with relative robot's structure.

This document presents the development of a prototype of a Cartesian robot with a 5 degrees-of-freedom articulated robot arm for possible applications in a wet spinning process at the laboratory-level. The mechatronic system was developed using various modular electronics elements compatible with an ArduinoTM Mega 2560 microcontroller. The control algorithm recognizes the position of the servomotors and the speed of the motors so that the prototype performs a complete cycle of displacement in 67 s, which includes the transport of a polymeric filament immersed in a coagulation bath for 10 s. A structural analysis indicates that there will be no tension failures because the maximum and axial stress of the Cartesian robot was 2.89 MPa while the von Mises tension of the robotic arm was 468 MPa, both tensions below their upper limits. The pulse signals, in the order of 4000 +25 ms, of the servomotors were consistent in 96-98% repeatability and 3-19% reproducibility. Forty percent of the extrusion tests performed were satisfactory, since the transport of a polymeric filament within a coagulant solution was achieved.

To achieve accurate position tracking, there is a need to develop high-fidelity robot arm models that are compliant and affordable. However, physics-based models are constrained by their stiffness and complexity. Therefore, reduced-order modelling developed from data through sub-space system identification is proposed as a solution to this problem. A high-fidelity simulation model of a two-link robot arm developed in MATLAB and Simulink was used to generate synthetic data, and the data acquired was used for the estimation and validation of first- and second-order linear state-space models. Due to its effective tracking characteristics, the model predictive control technique was used for trajectory tracking. The results of the simulations demonstrate that the first-order and second-order models can track the intended set points accurately but at the cost of larger joint torques required to counteract gravity. The results show that low-order and data-compliant models can follow trajectories with high precision. MATLAB 2020a was used for all simulations.

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.

Dördüncü sanayi devrimiyle birlikte makineleşme giderek artmakta ve sistemlerde insan etkileşimi olabildiğince azaltılmaktadır. Gelişen endüstriyel teknolojiler ile minimum zamanda maksimum verimli ürünler üretilmektedir. Günümüzde robot kolların ve gömülü sistemlerin kullanılması teknolojinin bu seviyelere gelmesinde büyük rol oynamıştır. Bu çalışmada konveyör bant üzerinden gelen nesneler bir kamera tarafından algılanmaktadır. Algılanan nesneler, geliştirilen gömülü sistem yazılımı, robot kol ve görüntü işleyen bilgisayar yazılımı yardımıyla renklerine göre belirli koordinatlarda bulunan kutulara bırakılarak sınıflandırılmıştır. Geliştirilen sistem farklı renklerdeki nesnelerin renk kodlarına göre ilgili kutulara yerleştirilmesi görevini %100 başarı ile tamamlamıştır.

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.

Industrial robots have a key role in the concept of Industry 4.0. On the one hand, these systems improve quality and productivity, but on the other hand, they require a huge amount of energy. Energy saving solutions have to be developed and applied to provide sustainable production. The purpose of this research is to develop the optimal control strategy for industrial robots in order to minimize energy consumption. Therefore, a case study was conducted for the development of two control strategies to be applied to the RV-2AJ Mitsubishi robot arm with 5 DOF, where the system is a nonlinear one. The first examined controller is the classical linear proportional integral derivative (PID) controller, while the second one is the linear model predictive control (MPC) controller. In our study, the performances of both the classical PID model and the linear MPC controller were compared. As a result, it was found that the MPC controller in the execution of the three defined reference trajectories [(1) curve motion, (2) N-shaped motion, and (3) circle motion] was always faster and required less energy consumption, whereas in terms of precision the PID succeeded in executing the trajectory more precisely than the MPC but with higher energy consumption. The main contribution of the research is that the performances of the two control strategies with regard to a complex dynamic system were compared in the case of the execution of three different trajectories. The evaluations show that the MPC controller is, on the one hand, more energy efficient; on the other hand, it provides a shorter cycle time compared to the PID controller.

The problems of the traditional gangue sorting robot arm control algorithms such as the grasping function method and the dynamic target grasping algorithm based on Ferrary method are relying on an accurate environment model and lacking adaptivity in the control process. At the same time, the problems of the traditional intelligent control algorithms such as deep deterministic policy gradient (DDPG) are excessive output actions and sparse rewards that are easily covered. In order to solve these problems, this study improves the neural network structure and reward function in the traditional DDPG algorithm, and proposes an improved DDPG algorithm based on reinforcement learning, which is suitable for handling six-degree-of-freedom gangue sorting robot arms. After the gangue enters the working space of the robot arm, the improved DDPG algorithm can make decisions according to the gangue position and robot arm state returned by the corresponding sensor, and can output a set of joint angle state control quantity to the corresponding motion controller. The algorithm can control the movement of the robot arm according to the gangue position and joint angle state control quantity, so that the robot arm moves to the nearby gangue to conduct gangue sorting. The simulation results show that compared with the traditional DDPG algorithm, the improved DDPG algorithm has the advantages of model-free versatility and adaptive learning of grasping pose in interaction with the environment. Moreover, the improved algorithm can be the first to converge to the maximum reward value encountered during exploration. The robot arm controlled by the improved DDPG algorithm has better policy generalization, smaller joint angle state control output and higher gangue sorting efficiency.

Spatial cues play an important role in navigating people in both physical and virtual spaces. In spatial navigation, visual information with additional cues, such as haptic cues, enables effective guidance. Most haptic devices are applied to various body parts to make mechanical stimuli, while few devices stimulate a head despite the excellent sensitivity. This article presents Virtual Whiskers, a spatial directional guidance technique by cheek haptic stimulation using tiny robot arms attached to a Head-Mounted Display (HMD). The tip of the robotic arm has photo reflective sensors to detect the distance between the tip and the cheek surface. Using the robot arms, we stimulate a point on the cheek obtained by calculating an intersection between the cheek surface and the target direction. In the directional guidance experiment, we investigated how accurately participants identify the target direction provided by our guidance method. We evaluated an error between the actual target direction and the participant's pointed direction. The experimental result shows that our method achieves the average absolute directional error of 2.54° in the azimuthal plane and 6.54° in the elevation plane. We also conducted a spatial guidance experiment to evaluate task performance in a target search task. We compared the condition of visual information, visual and audio information, and visual information and cheek haptics for task completion time, System Usability Scale (SUS) score, NASA-TLX score. The averages of task completion time were M = 6.39 s, SD = 3.34 s, and M = 5.62 s, SD = 3.12 s, and M = 4.35 s, SD = 2.26 s, in visual-only condition, visual+audio condition, and visual+haptic condition, respectively. In terms of the SUS score, visual condition, visual+audio condition, and visual+haptic condition achieved M = 55.83, SD = 20.40, and M = 47.78, SD = 20.09, and M = 80.42, SD = 10.99, respectively. As for NASA-TLX score, visual condition, visual+audio condition, and visual+haptic condition resulted in M = 75.81, SD = 16.89, and M = 67.57, SD = 14.96, and M = 38.83, SD = 18.52, respectively. Statistical tests revealed significant differences in task completion time, SUS score, and NASA-TLX score between the visual and the visual+haptic condition and the visual+audio and the visual+haptic condition.

In order to control joints of manipulators with high precision, a position tracking control strategy combining fractional calculus with iterative learning control and sliding mode control is proposed for the control of a single joint of manipulators. Considering the coupling between joints of manipulators, a fractional-order iterative sliding mode cross-coupling control strategy is proposed and the theoretical proof of its progressive stability is given. The paper takes a two-joint manipulator as the research object to verify the control strategy of a single-joint manipulator. The results show that the control strategy proposed in this paper makes the two-joint mechanical arm chatter less and the tracking more accurate. The synchronous control of the manipulator is verified by a three-joint manipulator. The results show that the angular displacement adjustment times of the three-joint manipulator are 0.11 s, 0.31 s and 0.24 s, respectively. 3.25 s > 5 s, 3.15 s of a PD cross-coupling control strategy; 2.85 s, 2.32 s, 4.22 s of a PD iterative cross-coupling control strategy; 0.14 s, 0.33 s, 0.28 s of a fractional-order sliding mode cross-coupling control strategy. The root mean square error of the position error of the designed control strategy is 6.47 × 10-6 rad, 3.69 × 10-4 rad, 6.91 × 10-3 rad, respectively. The root mean square error of the synchronization error is 3.96 × 10-4 rad, 1.36 × 10-3 rad, 7.81 × 10-3 rad, superior to the other three control strategies. The results illustrate the effectiveness of the proposed control method.

İşitme kaybı olan insanlar, nasıl iletişim kuracaklarını öğrenme konusunda güçlük çekerler çünkü çevrelerindeki tüm sesleri ve hatta kendi seslerini duyamazlar. Çalışmada 25 Türkçe örneklem hareket, Google ses kütüphanesi kullanılarak ses yoluyla veya klavyeden bilgi girişi yoluyla işitme engellilere işaret dili hareketleri ile bir insansı robot kolları tarafından gerçekleştirip işaret dilinin algılaması ve hareketin insansı robot kol tarafından gerçekleştirilmesi sağlanmıştır. Klavyeden bilgi girişi ile robot kolun hareketi gerçekleştirme süresinin ses ile hareketi gerçekleştirme süresinden %40 daha hızlı olduğu görülmüştür.

This paper presents a run-time solver for the inverse kinematics of a robotic arm implemented on a heterogeneous Multi-Processor System-on-Chip (MPSoC). The solver has been formulated as an optimization problem, in which two levels of algorithmic parallelism are proposed: i) the Nelder-Mead derivative-free method used as the optimization engine is modified to allow the evaluation of the cost function in multiple vertices simultaneously, ii) the trajectory is divided into non-overlapping segments, in which all the points are solved concurrently. Algorithmic parallelism is supported by a variable number of parallel instances of a custom hardware accelerator, which speeds up the computation of the forward kinematics equations of the robot required during the resolution of the inverse kinematics. This adaptable scheme provides run-time scalability in terms of trajectory accuracy, logic resources, dependability, and execution time. New design methodologies are used to unify the modeling of the software and hardware partitions of the controller while transparently providing adaptability. They are based on the dataflow Model of Computation (MoC), supported by the PREESM prototyping tool. This tool has been extended to support the use of dynamically reconfigurable hardware accelerators implemented using the ARTICo3 framework. The proposal has been validated with a python-based robotic arm simulator. Experimental results show how the proposed parallelism, combined with hardware acceleration, enables the run-time resolution of the trajectory with adaptable performance using a Xilinx Zynq UltraScale+ MPSoC device.

This paper proposes a novel remote monitoring system using a head mounted display (HMD). By using an HMD, a high degree of immersion and the sense of reality is provided to the user. Further, it becomes more convenient to move the camera position while controlling the robot arm on which the camera is mounted through the coordinates obtained from the HMD sensors. The proposed system performs two tasks. Initially, in order to render images in the HMD, the camera captures input images, and the PC connected to the robot arm sends the image to the PC connected to the HMD in real-time after which the image is rendered in the HMD. For the real-time monitoring, we increased the frame per second by reducing the data size. In order to reduce the data size, we define region of interest which is the region a user can see. Then, region of interest of the image is cropped and the resolution of entire image is degraded. Therefore, two images composed of cropped image and degraded image are transmitted and merged into an image. In this way, we can reduce the data size and provide the user with monitoring the image of the original quality. Next, using the inertial measurement unit sensor and base station sensors built into the HMD, the user controls the displacement of the camera during rotation and translation by controlling the robot arm by the user's own motion. The PC connected to the HMD transmits the motion coordinates of the HMD acquired from the sensor into the PC connected to the robot arm. To control the translation of the camera, we define a coordinate system that represents the y-axis as running from top to bottom, and the x-axis as running from the front to the back in three-dimensional space. Thus, each step motor along each axis, which controls the robot arm, has its movement controlled by supplying appropriate values of angular degrees. Through the above process, the proposed method implements a remote monitoring system with five degrees of freedom. In the experiment, we measured the data transmission delay time and the displacement error due to the robot arm's motion control to determine whether the system is suitable for a real-time remote monitoring system. Experimental results show that the proposed system can be optimally operated with a frame rate of 15 frames per second and a group of pixel resolutions of $640\times 640$ of the cropped image and $240\times 120$ of the degraded image. In addition, the average error rate of the robot arm's displacement was 6.45% when the camera position was controlled through the robot arm.

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.

Over the past two decades, the field of civil engineering has witnessed a remarkable transformation with the integration of robotic technology in civil and infrastructures inspection. Robotic drone arms have emerged as a pivotal tool, offering unparalleled access to hard-to-reach areas and enabling comprehensive assessments of civil structures. This article presents a comprehensive review of the advancements, challenges, and applications of robotic drone arms for civil structure inspection over the last two decades. This article discusses the key developments in types of robotic drone arms, real-world applications, success stories, current challenges and future directions for civil and infrastructure inspection.

The control architecture guiding simple movements such as reaching toward a visual target remains an open problem. The nervous system needs to integrate different sensory modalities and coordinate multiple degrees of freedom in the human arm to achieve that goal. The challenge increases due to noise and transport delays in neural signals, non-linear and fatigable muscles as actuators, and unpredictable environmental disturbances. Here we examined the capabilities of hierarchical feedback control models proposed by W. T. Powers, so far only tested in silico. We built a robot arm system with four degrees of freedom, including a visual system for locating the planar position of the hand, joint angle proprioception, and pressure sensing in one point of contact. We subjected the robot to various human-inspired reaching and tracking tasks and found features of biological movement, such as isochrony and bell-shaped velocity profiles in straight-line movements, and the speed-curvature power law in curved movements. These behavioral properties emerge without trajectory planning or explicit optimization algorithms. We then applied static structural perturbations to the robot: we blocked the wrist joint, tilted the writing surface, extended the hand with a tool, and rotated the visual system. For all of them, we found that the arm in machina adapts its behavior without being reprogrammed. In sum, while limited in speed and precision (by the nature of the do-it-yourself inexpensive components we used to build the robot from scratch), when faced with the noise, delays, non-linearities, and unpredictable disturbances of the real world, the embodied control architecture shown here balances biological realism with design simplicity.

Tensegrity is a structure consisting of rigid bodies and internal tensile members, with no contact between the rigid bodies. The model of an arm with a tensegrity structure is not movable as it is, but we believe that it can be made movable and flexible by incorporating springs. We developed an arm that incorporates springs in the arm’s tensile members by extending the model of an arm with a tensegrity structure. Then, as an evaluation of the developed arm, we measured the ranges of motions and the forces required for that motion. We also developed a mechanism that allows the arm to bend and extend. We believe that this method of making the tensegrity arm controllable by incorporating springs will be useful in the development of flexible robotic arms for caregiving using robots and other applications.

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.

Many processes involve the manipulation of easily deformable objects such as food and cloth products, and the automation of such processes is required. However, there are still many challenges in pick-and-place, assembly and handling those objects using industrial robots. The insertion task involved in the formation of rubber hoses is an example of such a task. In this study, to automate the rubber hose insertion task, a preliminary investigation of friction force that affects the insertion task was examined and it was found that the air injection into the hose was effective to reduce the friction force. Based on the result, an air injection robotic hand (AIR-Hand) and a hose buckling preventing hand (HELP-Hand) were developed for a dual-arm robot. Furthermore, we integrated those two end effectors into one for a single-arm robot. The hose insertion task was successfully performed by both the dual-arm and single-arm robots, along with their corresponding manipulation plans.

Collaborative robots (or cobots) are robots that can safely work together or interact with humans in a common space. They gradually become noticeable nowadays. Compliant actuators are very relevant for the design of cobots. This type of actuation scheme mitigates the damage caused by unexpected collision. Therefore, elastic joints are considered to outperform rigid joints when operating in a dynamic environment. However, most of the available elastic robots are relatively costly or difficult to construct. To give researchers a solution that is inexpensive, easily customisable, and fast to fabricate, a newly-designed low-cost, and open-source design of an elastic joint is presented in this work. Based on the newly design elastic joint, a highly-compliant multi-purpose 2-DOF robot arm for safe human-robot interaction is also introduced. The mechanical design of the robot and a position control algorithm are presented. The mechanical prototype is 3D-printed. The control algorithm is a two loops control scheme. In particular, the inner control loop is designed as a model reference adaptive controller (MRAC) to deal with uncertainties in the system parameters, while the outer control loop utilises a fuzzy proportional-integral controller to reduce the effect of external disturbances on the load. The control algorithm is first validated in simulation. Then the effectiveness of the controller is also proven by experiments on the mechanical prototype.

Malfunctions on industrial robots can cost factories 22000 dollars per minute. Although the benefits of a fault-tolerant robot arm are clear, redundant sensors would steeply add to the costs of such robots while machine learning-based methods would spend too much time learning the robot's model. We propose a simple but highly effective method to infer which joint underwent failure and at which angle this joint is constrained and to, then, modify the inverse kinematics (IK) algorithm to adaptively achieve the goal. Our method involves combining the robot arm with a QR code and an inexpensive camera, building a virtual link between these three to give the relative position of the end-effector. Once one joint/encoder/motor suffers damage, we use this virtual link to calibrate this joint by coordinate transformation, to calculate the constrained angle, and to recalculate the trajectory through IK iterations with the Newton-Raphson method. We prove the efficacy of our method with pick-and-place experiments, commonly seen in industrial settings, emulating malfunctions on different joints and at different angles, and our method can successfully finish the task in most cases. We further demonstrate that our method is capable, for almost all of the six-degree-of-freedom manipulators, to adapt to joint failures after suffering an actuator failure. With the steep increase of robots within factories, this paper presents an elegant approach to keep robots functional until maintenance is scheduled, reducing downtime.

Artificial intelligence techniques for pneumatic robot manipulators have become of deep interest in industrial applications, such as non-high voltage environments, clean operations, and high power-to-weight ratio tasks. The principal advantages of this type of actuator are the implementation of clean energies, low cost, and easy maintenance. The disadvantages of working with pneumatic actuators are that they have non-linear characteristics. This paper proposes an intelligent controller embedded in a programmable logic device to minimize the non-linearities of the air behavior into a 3-degrees-of-freedom robot with pneumatic actuators. In this case, the device is suitable due to several electric valves, direct current motors signals, automatic controllers, and several neural networks. For every degree of freedom, three neurons adjust the gains for each controller. The learning process is constantly tuning the gain value to reach the minimum of the mean square error. Results plot a more appropriate behavior for a transitive time when the neurons work with the automatic controllers with a minimum mean error of ±1.2 mm.

Achieving the smart motion of any autonomous or semi-autonomous robot requires an efficient algorithm to determine a feasible collision-free path. In this paper, a novel collision-free path homotopy-based path-planning algorithm applied to planar robotic arms is presented. The algorithm utilizes homotopy continuation methods (HCMs) to solve the non-linear algebraic equations system (NAES) that models the robot’s workspace. The method was validated with three case studies with robotic arms in different configurations. For the first case, a robot arm with three links must enter a narrow corridor with two obstacles. For the second case, a six-link robot arm with a gripper is required to take an object inside a narrow corridor with two obstacles. For the third case, a twenty-link arm must take an object inside a maze-like environment. These case studies validated, by simulation, the versatility and capacity of the proposed path-planning algorithm. The results show that the CPU time is dozens of milliseconds with a memory consumption less than 4.5 kB for the first two cases. For the third case, the CPU time is around 2.7 s and the memory consumption around 18 kB. Finally, the method’s performance was further validated using the industrial robot arm CRS CataLyst-5 by Thermo Electron.

Computer vision-based path planning can play a crucial role in numerous technologically driven smart applications. Although various path planning methods have been proposed, limitations, such as unreliable three-dimensional (3D) localization of objects in a workspace, time-consuming computational processes, and limited two-dimensional workspaces, remain. Studies to address these problems have achieved some success, but many of these problems persist. Therefore, in this study, which is an extension of our previous paper, a novel path planning approach that combined computer vision, Q-learning, and neural networks was developed to overcome these limitations. The proposed computer vision-neural network algorithm was fed by two images from two views to obtain accurate spatial coordinates of objects in real time. Next, Q-learning was used to determine a sequence of simple actions: up, down, left, right, backward, and forward, from the start point to the target point in a 3D workspace. Finally, a trained neural network was used to determine a sequence of joint angles according to the identified actions. Simulation and experimental test results revealed that the proposed combination of 3D object detection, an agent-environment interaction in the Q-learning phase, and simple joint angle computation by trained neural networks considerably alleviated the limitations of previous studies.

The main focus of this paper is to design and develop a system of two robot arms for classifying and sorting objects based on shape and size using machine vision. The system uses a low-cost and high-performance hierarchical control system including one master and two slaves. Each slave is a robot controller based on a microcontroller that receives commands from the master to control the robot arm independently. The master is an embedded computer used for image processing, kinematic calculations, and communication. A simple and efficient image processing algorithm is proposed that can be implemented in real-time, helping to shorten the time of the sorting process. The proposed method uses a series of algorithms including contour finding, border extraction, centroid algorithm, and shape threshold to recognize objects and eliminate noise. The 3D coordinates of objects are estimated just by solving a linear equation system. Movements of the robot's joints are planned to follow a trapezoidal profile with the acceleration/deceleration phase, thus helping the robots move smoothly and reduce vibration. Experimental evaluation reveals the effectiveness and accuracy of the robotic vision system in the sorting process. The system can be used in the industrial process to reduce the required time to achieve the task of the production line, leading to improve the performance of the production line.

The paper presents some building schemas of manipulator-robots used in auxiliary operations of electric arc furnaces that are found insteel works. The technology of making steel in electric arc furnaces requires thatthe liquid steel outlet is filledfollowing theprimary elaboration. An "EBT SAND" type manipulator is used to fill the steel outlet, the kinematic scheme of which corresponds toSCARA 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 ofan electrically driven swing arm and a hydraulic lift that cleansthe EBT tap-hole.

Background and Objectives: This paper presents a new optimization problem in the field of linkage reconfiguration. This is the problem of minimizing moving parts of a given robot arm for positioning the end effector of the given robot arm at the given target point as well as minimizing the movement of the movable parts. Methods: Initially, formal modeling is accomplished by minimizing the movement problem. At this time, a criterion called AM (Arithmetic Measure) is introduced, and this criterion is used to quantify the motion of the linkage. Afterward, it is indicated that the presented problem is an NP-Hard problem. Consequently, a greedy heuristic algorithm is presented to minimize the movement of the robot's moving components. After identifying the moving components and the movement of these parts, an algorithm is provided to determine the final configuration of the robot arm. Results: The results indicate that the discussed model successfully reduced the moving parts of the robot arm. Moreover, the results show that the proposed approach fulfills the goal of minimization of the linkage components. Furthermore, this method leads to erosion of arm, reduces energy consumption and the required parameters and variables for calculating the final configuration of the linkages. Conclusion: The mentioned algorithm solves the problem by mapping the robot arm with an arbitrary number of links to a robot with a single link or two links. The proposed heuristic approach requires O(n2) time using O(n) space.======================================================================================================Copyrights©2018 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================

In this study, a real time monitoring and control of the working parameters of distributed robotic systems used in manufacturing processes is presented. Additionally, a fault diagnosis and protection system is developed in the control and monitoring system to prevent possible errors during the working process. Unlike the conventional monitoring and control systems, Android/IOS based smart phones and tablets are used besides SCADA and the process is supported by cameras. 3 different robotic systems are used in the study and electrical, electronic and mechanical prototypes are designed for each of them. Thus, a complete robotic system being able to perform real time monitoring and control for industrial manufacturing processes that is supported by smart phone applications and fault diagnosis and protection system is developed successfully.

Robotics is an interdisciplinary field and there exist several well-known approaches to represent the dynamics model of a robot arm. The robot arm is an open kinematic chain of links connected through rotational and translational joints. In the general case, it is very difficult to obtain explicit expressions for the forces and the torques in the equations where the driving torques of the actuators produce desired motion of the gripper. The robot arm control depends significantly on the accuracy of the dynamic model. In the existing literature, the complexity of the dynamic model is reduced by linearization techniques or techniques like machine learning for the identification of unmodelled dynamics. This paper proposes a novel approach for deriving the equations of motion and the actuator torques of a robot arm with an arbitrary number of joints. The proposed approach for obtaining the dynamic model in closed form employs graph theory and the orthogonality principle, a powerful concept that serves as a generalization for the law of conservation of energy. The application of this approach is demonstrated using a 3D-printed planar robot arm with three degrees of freedom. Computer experiments for this robot are executed to validate the dynamic characteristics of the mathematical model of motion obtained by the application of the proposed approach. The results from the experiments are visualized and discussed in detail.

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.

The robot arm DH parameter calibration method based on calibration board is proposed,the 7points of calibration board can be sampled by using the end effector in sequence,among which the first 3 points are used to construct the relationship between world and robot coordination system while the other 4 points are applied to establish objective function and error equation. The DH parameter after calibration can be obtained by optimization solution of error equation. At last,the effectiveness of this method is verified through repeated positioning accuracy,the repeated positioning accuracy can be improved from 4. 68 mm to 0. 87 mm. This method has the advantages of simple equipment and easy operation.

By adopting a genetic algorithm for optimization of neural network,the gravity compensation for robot arm is researched.Firstly,through the basic knowledge of mechanics and D-H parameter to set up robot kinematics model,the theoretical computation formula of the gravity item of each joint torque in robot arm is got.Secondly,in the Solid Works simulation software,the simulation value of the gravity item in some certain pose got,thus the correctness of the theoretical computation formula is verified.Finally,the predicted value of the gravity item by genetic algorithm for optimization of neural network is obtained.Experimental results show that the predicted value of gravity items of robot arm learnt with this method is basically conforming with the theoretical value.Consequently,the work load of calculation for the gravity items of robot arm is effectively reduced.Furthermore,this method provide a possible way for the real-time control.

This paper shows a singular mechanism for a gravity compensated and reactionless robot arm. Firstly, the difference between singular point and singular mechanism is explained. A Simple example shows that singular point is local but singular mechanism is global. In other words the latter singularity is globally maintained. Secondly, four characteristics are introduced. The first one is gravity equilibrium, the second is centroid immobility, the third is conservation of angular momentum and the last is constant posture. Gravity compensation is made possible by gravity equilibrium, and no reaction is made possible by centroid immobility and conservation of angular momentum. Thirdly, a robot arm using a singular mechanism is introduced, and characteristics of no reaction are shown by a computer simulation. Finally, it shows the motion range of the arm as an issue.

In the field of regenerative medicine, cell processing is currently done manually. The process is labor intensive and expensive, and its efficiency must be improved. Automatic cell culture apparatuses equipped with a vertical articulated robot have been proposed recently. However, automating all tasks of cell processing complicates the system constitution. The present study aims to develop a simple and rational cell processing system by combining the tasks performed by a robot with those performed by a human. Herein, we first analyze each task in the cell processing operation, verify whether a task can be efficiently performed by a robot and automated equipment, and decide the combination of the tasks. In a previous paper, we improved the efficiency of the task of discarding spent culture media in a flask by using a robot arm in the media change process. In the present paper, by focusing on the media change process, we examine the efficiency improvement of the task of injecting culture media by using a robot. We propose the configuration of an inspiration/injection port and an algorithm to estimate the start time of injection end motion from the flow rate and conduct a verification experiment. Our results show that a robot can perform the injecting task more efficiently than a human. Moreover, the risk of dripping can be reduced by using a robot.

This research is concerned with the study of Fractional order PID controller (FOPID). The fractional order controller (FOPID) is the generalization of the integer order PID controller. Which applied on robotic arm have 5Joints, practically and by simulation utilizing Matlab/Simulink. The Simulink by using tools fomcon version (1.0) has been added to the Matlab program. As well as the building and convert the robot arm of the type robotic arm edge. Practically the realization has been done using microcontroller Arduino which connection Matlab program by using tools box has been added to the Matlab program too. To control the arm joints of the positions that is the human arm simulator. Since the FOPID and PID controller has many parameters, which must be tuned to get the best performance for the robotic arm system so that the robotic arm system simulated in Matlab and interconnected with intelligent optimization method which called Bacterial Foraging Optimization to find the optimal parameter values that minimizing an objective function given as overshoot and Integral of time weighted absolute error (ITAE). The time domain and frequency domain performance for the closed loop the arm joints system compared for both FOPID with conventional PID controller. The obtained results show that the performance of the system with FOPID controller was better than the conventional PID controller.

Symmetries play very important in the dynamics of robot systems. The relevant control of robot arm motion with fault diagnosis including the optimized fuzzy algorithm based on the error rate adjustment P, I, D value (Fuzzy PID algorithm) model relies on symmetry principles. A robot is a kind of mechanical device that can program and perform certain operations and mobile tasks under automatic control. The manipulator is a very complex multi-input multi-output non-linear system and the main actuator of the robot. This paper focuses on the design of a control algorithm for a two-degree-of-freedom (2-DOF) manipulator. First, the mathematical model of a 2-DOF articulated manipulator is established, that is, the functional relationship between the input driving force vector and the output rotation angle vector of a 2-DOF manipulator. Then, a set of trajectory planning algorithms are designed by using gradient model control, which can calculate the trajectory of the end-effector of a 2-DOF manipulator according to the user’s task requirements. The experimental results verify the effectiveness of the proposed algorithm.

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.

With the continuous improvement of control technology and the continuous improvement of people’s living standards, the needs of disabled people for high-quality prosthetics have become increasingly strong. A control method of robotic arm based on surface electromyography signal (sEMG) of forearm is proposed. Firstly, the 16-channel EMG data of the forearm is obtained via the multi-channel EMG acquisition instrument and the electrode cuff as input signals, the features are extracted, then the gestures are classified and identified by the support-vector machine (SVM) algorithm, and the signals are finally transmitted to the robotic arm, so that people can teleoperate the robotic arm via sEMG signals in real time. Reduce the number of channels to lower the cost while ensuring a high and usable recognition rate. Experiments were performed by collecting EMG signals from the forearm surface of eight healthy volunteers. The experimental results show that the system’s overall gesture recognition accuracy rate can reach up to 90%, and the system responds fast, laying a good foundation for manipulating artificial limbs in the future.