K.N. Toosi University of TechnologyInternational Journal of Robotics, Theory and Applications2008-71448120220501Experiments on a Novel Hybrid Elastic Joint Robot with a Robust and Fast Control Strategy for Minimally Invasive Surgery116165714ENMahdiSouzanchi-KCenter of Excellence on Soft Computing and Intelligent Information Processing, Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, IranMohammad-R.Akbarzadeh-T.Center of Excellence on Soft Computing and Intelligent Information Processing, Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran0000-0001-5626-5559Journal Article20210117Design and control of surgical robots for minimally invasive surgery have considerable challenges in handling unknown robot dynamics, environmental uncertainties, desirable workspaces, and fast motion reactions. Realistic operational requirements regarding kinematics and dynamics of the robot and its actuators also lead to joint elasticity; therefore, further concerns regarding sustained chattering, vibration, and instability are raised. Accordingly, the robot proposed here is designed based on a hybrid structure, consisting of two parallel and serial manipulators, aiming for the desired rigidity and acceptable manipulability requirements of robotic surgeries. Furthermore, a decentralized sliding mode controller with an indirect adaptive fuzzy estimator based on a voltage control strategy is employed to tackle the challenges of controlling such as chattering phenomenon, control loop's speed, and stability against uncertainties. In contrast to the traditional sliding mode controllers, the control law is free from robot dynamics, the control signals' chattering is reduced, and bounded input-bounded output stability is shown without knowing the uncertainty bounds on robot dynamics. Simulation and experimental studies indicate that this approach is realistic.https://ijr.kntu.ac.ir/article_165714_25a9421c4fb7b83e78f43254a79b6b16.pdfK.N. Toosi University of TechnologyInternational Journal of Robotics, Theory and Applications2008-71448120220501Design and Development of a Mechanical Safety Stop Mechanism and an Adaptive Variable Impedance Controller for a Soft Hand Exoskeleton1732165913ENFaezehNazariIntelligent Systems and Advanced Control Lab, Faculty of Mechanical Engineering, University of Guilan, Rasht, Guilan, Iran0000-0003-4964-2840AliChaibakhshIntelligent Systems and Advanced Control Lab, Faculty of Mechanical Engineering, University of Guilan, Rasht, Guilan, Iran0000-0001-9529-1784FaridNajafiFaculty of Mechanical Engineering, Guilan University, Rasht, Iran0000-0003-3083-0411KhalilAlipourAdvanced Service Robots (ASR) Laboratory, Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran0000-0003-4456-1179Journal Article20210620In this paper, the design and performance evaluation of a new safety stop mechanism for a soft-hand exoskeleton are presented. The considered wearable orthosis comprises an under-actuated tendon-driven mechanism to perform flexion and extension on each finger using a separate electrical motor. The flexor and extensor tendons move through different paths connected to the motors with custom-made double-groove rollers to flex and extend the thumb, index, and middle fingers. A series flexible actuator unit is designed, including a mechanical module and a flexible one, fulfilling the hand-wearable robot’s functional and safety requirements. The actuator system’s flexible module uses a novel safety stop mechanism to prevent hyper-flexion, hyper-extension, or large forces applied to the user’s hand. Experiments are conducted on a healthy subject to evaluate the effectiveness of the design exoskeleton for patients with hand movement disorders. The experimental results show that the robot can follow the desired path by preserving its back-drivability and compliant properties. The efficacy of the novel safety stop mechanism is also evaluated on the prototype version of the robot, which shows that it can mechanically restrict the range of motion to a safe range. Finally, an adaptive variable impedance controller is designed to achieve assist-as-needed characteristics for the robot. The proposed controller overcomes the need to direct measurement of the participation of the patient via force sensors, which could ease the rehabilitation process.https://ijr.kntu.ac.ir/article_165913_e5bed4cb0c963344a925886f4b87feaa.pdfK.N. Toosi University of TechnologyInternational Journal of Robotics, Theory and Applications2008-71448120220501Eye In-hand Stereo Image Based Visual Servoing for Robotic Assembly and Set-Point Calibration used on 4 DOF SCARA robot3344165713ENMahmoudJeddiFaculty of Material and Manufacturing Technologies, Malek Ashtar University of Technology,Tehran, Iran0000-0001-7926-7856AhmadrezaKhoogarFaculty of Material and Manufacturing Technologies, Malek Ashtar University of Technology, Tehran, IranAliMehdipoor OmraniFaculty of Material and Manufacturing Technologies, Malek Ashtar University of Technology, Tehran, IranJournal Article20201127The method is presented for object assembling via a manipulator robot. This method was designed to track the pieces based on image feedback for pick and placement tasks. The depth of the pieces is calculated by stereo triangulation. Vision-guided robotics is most often based on the training steps, therefore it is a time-consuming process. For this reason, we have proposed a modified stereo vision method to predict the movement of the part in the image space. The linear and angular velocities of moving objects predict by a state estimator. The object velocity components predicted by estimation algorithms such as Kalman filter, Recursive least square and Extended Kalman Filter. Results show that in the case of, Extended Kalman filter estimator shows better tracking and convergence behavior. This method does not need to know the three-dimensional model of the parts, and it can be used on pick and place robots if the physical limitations of the joints are considered. The proposed method was experimentally tested in a laboratory environment. The cameras were installed parallel and non-parallel to determine the effect of the field of view on the precision and speed detection. A comparison of the simulation and experimental results showed that the use of the parallel stereo Image-based visual servoing with the Extended Kalman Filter method could be smoother and more accurate than the other methods.https://ijr.kntu.ac.ir/article_165713_fd0b88df50df32809e6ccfd99407f186.pdfK.N. Toosi University of TechnologyInternational Journal of Robotics, Theory and Applications2008-71448120220501Evaluation of Classifiers for a Vision-based Automated Mold Protection System Using Modified LBP Features4556166452ENMohammadrezaAsadi-GandomaniDepartment of Electrical Engineering, Yazd University, Yazd, IranSeyedeh SogandHashemiDepartment of Electrical Engineering, Yazd University, Yazd, IranMohammad TaghiSadeghiDepartment of Electrical Engineering, Yazd University, Yazd, IranJournal Article20230102For decades, plastic components have been the main parts of products in industries such as food, pharmaceutical, automotive, etc. Generally, these components are created by injection molding machines. Using these machines, raw materials are converted to plastic parts, e.g., bottle caps, dosing spoons, and bumpers. The part of the machine that provisionally holds plastic products is called “Mold” which has a unique form for each product. Since molds are sensitive components with high prices, appropriate care is required. When mold is used as the dynamic part of the machine, it’s a high potential for damages due to incomplete product ejection. Utilizing an automated inspection system is a modern solution to prevent possible problems. In this paper, we propose an intelligent system based on machine vision that consists of image capturing, processing, and classification sections. In the processing section, we have used a novel modified Local Binary Pattern algorithm which leads to the suitable features for classifying images into two categories. To achieve the best classifier, four potent machine learning-based methods are evaluated: KNN, SVM, Random Forest, and Gradient Boosting. This evaluation is based on criteria like F1-score, training and processing time, and the experimental results claim that the SVM method is the best classifier with 11.87ms training time, 9.04us processing time, and F1-Score of 0.96.https://ijr.kntu.ac.ir/article_166452_081e4d2f709c471412a2464a3f729dd6.pdfK.N. Toosi University of TechnologyInternational Journal of Robotics, Theory and Applications2008-71448120220501Real-time Compensatory Movement Detection in Upper Limb Rehabilitation Using Deep Learning Methods5770166691ENJavadKhoramdelDepartment of Mechanical Engineering, Tarbiat Modares University, Tehran, IranAhmadMooriFaculty of Mechanical Engineering, K.N. Toosi University of Technolog, Tehran, IranMajidMohammadi MoghaddamDepartment of Mechanical Engineering, Tarbiat Modares University, Tehran, IranEsmaeilNajafiFaculty of Mechanical Engineering, K.N. Toosi University of Technolog, Tehran, IranJournal Article20220318This paper presents a real-time approach for detecting compensatory movements in upper limb rehabilitation for stroke patients using deep learning algorithms. The study applied Recurrent Neural Networks (RNN), Gated Recurrent Unit (GRU), Long-Short-Term-Memory (LSTM), and Transformer to analyze Microsoft Kinect data from the Toronto Rehab Stroke Pose dataset. The models were trained with focal loss to address imbalanced data distribution. The simulation results showed that the proposed deep learning algorithms are effective in detecting compensatory movements. The GRU-based models provide the fastest results and the transformer models exhibit the best accuracy and fastest inference time on the employed CPU .https://ijr.kntu.ac.ir/article_166691_3ca4be5ed520011720a344e6340261dc.pdfK.N. Toosi University of TechnologyInternational Journal of Robotics, Theory and Applications2008-71448120220501alpha -Stabilized Optimal Controller for Feedback Linearized Feedback-Decoupled Quaternion Based Model of Stewart Platform7179166976ENJavadSharifiElectrical and Computer Engineering Department, Qom University of Technology, Qom, IranMohammadDoustaniElectrical and Computer Engineering Department, Qom University of Technology, Qom, IranJournal Article20180616Here, we are going to design a stabilized controller for the Stewart platform. A new model of the Stewart platform will be presented which has many applications in the industry. The dynamics of the Stewart platform are presented in two separate systems. One system for the linear motion of the Stewart platform and another system for its angular moment. In addition, we use a quaternion-based method to analyze the dynamics of the Stewart platform. The 6-DOF Stewart-Platform dynamics is nonlinear, then at first by using the feedback linearization method we convert the nonlinear dynamics in new space as a linear state-space. Then we design an -stabilized controller for this platform in linear space. A linear controller for linear motion systems will be designed but for the second system, it must first be linearization and then design controller for it. After design of stabilized-LQR controller for linearized space system, we convert our design to original nonlinear space and exert on system for simulations. The simulations results show that we will succeed to design a controller for the Stewart platform.https://ijr.kntu.ac.ir/article_166976_556ac0710cd11de82ae31cd4e142e421.pdf