Parallel Robot Manipulator Research Articles

Noise-Suppressing Newton Algorithm for Kinematic Control of Robots

In this paper, armed with the integral control method, a new noise-suppressing Newton (NSN) algorithm is proposed for the redundancy resolution of redundant robot manipulators efficiently. For practical hardware implementation, the discrete-time noise-suppressing Newton (abbreviated as DTNSN) algorithm is discretized from the continues NSN algorithm. Specifically, the distinguishing feature of the proposed DTNSN algorithm is that it can rigorously converge with inherent tolerance to noises induced by communication jamming and computational systematical errors. In contrast, considerable traditional algorithms often dispose of noises with the high-degree filter from the viewpoint of signal processing, which requires a complex system structure and further results in a heavy computational burden. Note that theoretical analyses are provided to elaborate the convergent property of the DTNSN algorithm polluted with constant bias, time-dependent linear noises and bounded random noises. Besides, by the proposed DTNSN algorithm, the end effector of both serial and parallel redundant robot manipulators complete the allocated motion planning and are impervious to the noisy simulated environment.

Adaptive Event-Triggered Exponential Tracking Control of Parallel Robot Manipulators

Adaptive Event-Triggered Exponential Tracking Control of Parallel Robot Manipulators

FORCE BASED IMPEDANCE CONTROL OF 5-BAR PARALLEL ROBOT MANIPULATOR

The interaction of robots with the environment is increasing in many sectors. In particular, position and force-dependent interactions are frequently used in sensitive applications. In this interaction control, impedance control method answer the need. The parallel robot manipulator is one of the models that is widely used in this field and constitutes the basic of many robot designs. In this study, an exemplary 5-limb robot manipulator is designed. Position change according to the amount of load applied on this manipulator was realized with impedance control. Force amount measurement was applied using loadcell. The position change was calculated with the forward kinematic calculations of the manipulator. This calculation and control process was realized on the Arduino Mega embedded system board. With this experiment application study, the behaviour analysis of the robot manipulator was examined according to the stiffness and damping coefficients that affect the impedance control, and the ideal coefficients for the designed manipulator were determined.

Evolutionary Optimization of Fuzzy Reinforcement Learning and Its Application to Time-Varying Tracking Control of Industrial Parallel Robotic Manipulators

This paper contributes to the development of evolutionary optimization of fuzzy reinforcement learning and its application to time-varying tracking control for industrial parallel robotic manipulators. An improved evolutionary social spider optimization (SSO) paradigm with Cauchy mutation is presented to address the optimal fuzzy reinforcement learning problem. The SSO swarm intelligence incorporated with the fuzzy Q-learning strategy, called SSOFQ, is applied to the time-varying tracking control of parallel robotic Stewart manipulators with six degrees of freedom (DOFs). Having the derived kinematics and dynamics of six-DOF Stewart platforms, the SSOFQ is employed to synthesize an intelligent real-time tracking control scheme. More importantly, a custom experimental Stewart platform is constructed using hardware/software codesign and the system-on-a- programmable chip (SoPC) technique. Finally, the simulations, experimental results and comparative works are provided to validate the efficacy and superiority of the presented SSOFQ control method.

Development and performance investigations of the planar parallel robotic manipulators using shape memory alloy linear actuation technique

Development and performance investigations of the planar parallel robotic manipulators using shape memory alloy linear actuation technique

Using an Improved Output Feedback MPC Approach for Developing a Haptic Virtual Training System

Haptic training simulators generally consist of three major components, namely a human operator, a haptic interface, and a virtual environment. Appropriate dynamic modeling of each of these components can have far-reaching implications for the whole system's performance improvement in terms of transparency, the analogy to the real environment, and stability. In this paper, we developed a virtual-based haptic training simulator for Endoscopic Sinus Surgery by doing a dynamic characterization of the phenomenological sinus tissue fracture in the virtual environment, using an input-constrained linear parametric variable model. A parallel robot manipulator equipped with a calibrated force sensor is employed as a haptic interface. A lumped five-parameter single-degree-of-freedom mass-stiffness-damping impedance model is assigned to the operator’s arm dynamic. A robust online output feedback quasi-min–max model predictive control framework is proposed to stabilize the system during the switching between the piecewise linear dynamics of the virtual environment. The simulations and the experimental results demonstrate the effectiveness of the proposed control algorithm in terms of robustness and convergence to the desired impedance quantities.

Introducing convex BiLSTM-based controller applied to 3-PSP spatial parallel robot manipulator

The idea of modeling first and then finding a control approach might have made sense when systems were changing on a vast scale, but not in today’s world where we are in danger of being run over on a highway called the information highway. The feedback phenomenon, which involves a system’s future being influenced by its past, presents a rational conflict in this context. However, we can move beyond these challenges by leveraging the system’s inherent ability to randomly repeat and re-create parts, allowing new features to emerge and the best repetition to become the standard. To address the mentioned challenges, this paper proposes a new structure based on bilateral long short-term memory (BiLSTM) to provide supervising feedback on the nonlinear components comprising friction, Coriolis, and Centrifugal forces in addition to gravity for adapting uncertain variations in the robotic system dynamics. A key characteristic of the proposed BiLSTM-based controller is the incorporation of memory. A new formulation is developed for determining when to recover old memories and to what extent, ensuring the controller remains both adaptive and efficient. Furthermore, given that for system identification, speed is vital; the proposed approach has dealt with the speed category by introducing a convex combination of BiLSTM layers. The stability of the whole structure has also been analyzed, and the updated learning rules have been derived. The simulation results show improved tracking performance and learning time compared to alternative approaches.

Structural synthesis of the new type 4DOF robot manipulators for industry and medicine

ABSTRACT Different types of overconstrained robot manipulators are widely applied in industry and medicine. By applying advanced scientific approaches, the design of the new type of serial and parallel overconstrained robot manipulators with wider working zone, high speed and less singular configuration is an actual issue. In this research work, the structural synthesis of the new type of spatial serial and parallel-type robot manipulators with four degrees of freedom (4DOF) is performed by using the transformation of a plane-cylinder () higher kinematic pair. In this case, the number of spatial mobility and the number of constraints for the plane-cylinder higher kinematic pair are found with analytical method. For each of the designed spatial serial and parallel type 4DOF robot manipulators, along the gripper has the same mobility in the subspace, the direction of their constraints is determined by applying different configurations on the coordinate axes (revolute and translation on axes). The 3D models of the newly designed 4DOF robot manipulators are indicated in the tables, and new 4DOF robot manipulators specially designed to be used in industry and medicine are presented.

Modeling of parallel manipulators with flexible links and joints driven by electric actuators

This paper presents the approach of building a mathematical model for a parallel robotic manipulator with flexible links and elastic joints. The links to the base are assumed to be rigid bodies, and the thin connecting rods are assumed to be flexible links. The elasticity of the transmission from the actuators to the transmission is modeled by a torsional spring and viscous damper. This is a mixed system of rigid bodies, spring, and flexible links. The deformation motion of the elastic link is approximated by shape functions similar to the finite element method. The differential equations of motion are established by combining the substructure method and the Lagrange equation of the 2nd kind for the serial multibody system. Based on the differential equation established for the parallel robot manipulator of five bars, numerical simulations were carried out to investigate the response of the system.

Development of a novel parallel soft manipulator with sensing capabilities

It is challenging to equip a soft manipulator with sensors without compromising its mechanical properties. In this paper, we explain the design, analysis, and building of an innovative, low-cost soft parallel manipulator with sensing and actuation capabilities. Innovative bubble artificial muscle actuators serve as the connecting rod and contraction type actuator for the manipulator, as well as an opto-mechanical tactile sensor (TacTip) for objects detection. We propose a simulation method for the key structural design parameters of TacTip, and the influence of TacTip’s pin height, pin diameter, and surrounding pin angle on perceptual sensing has been examined via testing, which can provide a design guideline for the TacTip’s the structure. The impact of the counterweight on the soft parallel manipulator end-ascending effector’s and descending velocity, as well as its vertical and horizontal workspace, has been the topic of dynamic tests using a range of counterweights. The soft parallel manipulator’s workspace has been analyzed. In addition, an innovative, cost-effective, and simple soft-smart parallel robotic manipulator (PSM) control system has been built, and a demonstration of the detection and movement of the soft parallel manipulator is shown. The results demonstrate that the soft parallel manipulator described in this research is capable of detecting motion and collisions. It is anticipated that this soft-smart PSM would expand the employment of optical tactile sensors and artificial muscle actuators in material handling and size/shape sorting assembly lines.

Silicon-based soft parallel robots 4D printing and multiphysics analysis

Four-dimensional printing has set the stage for a new generation of soft robotics. The applications of rigid planar parallel robotic manipulators are also significant because of their various desirable characteristics, such as lower inertia, higher payload, and high accuracy. However, rigid planar parallel robots are heavy and require different actuators and components. This study introduces a novel technique to produce a light three degrees of freedom soft parallel manipulator at a low cost, which can be stimulated easily. This technique allows researchers to customize the actuator’s design based on the requirement. The robot is made by 3D printing based on fused deposition modelling and a direct ink writing process. The design, development, and additive manufacturing of a soft parallel robot electrothermally driven by a linear silicon-based actuator and polylactic acid parts are presented. Silicon-based soft actuators replace the rigid conventional linear actuators in this study to drive the planar parallel manipulator. The actuation of actuators is conducted using simple heating compared to the conventional rigid actuator. Various heating approaches and configurations are compared and analysed to find the most suitable one for the effective linear stroke of the soft actuator. The finite element model is used to analyse the performance of the electrothermally silicon-ethanol soft actuators in ABAQUS. The kinematics of the planar parallel robotic manipulator are simulated in MATLAB to achieve its workspace. The final soft parallel robot mechanism and the active and passive links are fabricated and tested experimentally.

Development of a spherical parallel manipulator for brain surgery applications: preliminary study on the dynamic analysis and verification

AbstractThis study focuses on development, task planning, and dynamic analysis of a previously proposed spherical parallel robot manipulator that is conceptually enhanced to adapt various brain surgery scenarios. Conceptual design of the proposed manipulator was briefly introduced and explained. In order to simulate one of the possible surgery scenarios, a case study of craniotomy was designed along with its trajectory planning. Dynamic analysis of proposed manipulator was performed by Lagrange method, and required actuator torque values were calculated for the desired trajectory. At the end of the study, hardware verification was carried out on the manufactured prototype of the system by comparing both calculated/acquired torque values and desired/actual trajectories. Promising verification results in terms of system dynamics and trajectory execution were introduced.

Predictive hierarchical harmonic emotional neuro-cognitive control of nonlinear systems

Emotion enables biological organisms to respond quickly and reasonably to uncertain and unpredictable events. This basic survival instinct is remarkably in line with the theory of model predictive control (MPC) that predicts the future occurrences of events to determine a present appropriate action. Specifically, here we propose to resolve the basic challenge of nonlinear MPC in keeping low computational cost by using a modified computational model of the limbic brain. For this purpose, we modify the thalamus–amygdala expansion link by a harmonic function to reach a differentiable and smooth mathematical model. The proposed harmonic emotion-based neuro-cognitive network (HENN) avoids the typical hidden layers in neural networks, leading to a lower computational cost. We then extend this architecture to a hierarchical HENN (H2ENN) to reach higher modeling accuracy. Theoretical analysis proves the training algorithm’s convergence for the general case of brain emotional learning (BEL). Specifically, the learning rules are shown to converge in a finite number of iterations if a solution exists. These theoretical results are general and equally applicable to HENN and hierarchical harmonic emotional neuro-cognitive network (H2ENN). Predictive control based on H2ENN is then applied to control a 3-Prismatic-Spherical-Prismatic (3-PSP) nonlinear parallel robot manipulator and compared with the same general structure that uses a conventional neural network instead. Results indicate the proposed H2ENN-based nonlinear MPC approach offers better accuracy. At the same time, its computational cost is twenty times less than the competing method.

Nonlinear optimal control for a five-link parallel robotic manipulator

Control and stabilization of parallel robotic manipulators is a non-trivial problem because of nonlinearities and the multi-variable structure. In this article, a nonlinear optimal control approach is proposed for the dynamic model of such robotic systems, using as a case-study the model of a five-link parallel robot. The dynamic model of the parallel robotic manipulator undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization relies on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the system, a stabilizing optimal (H-infinity) feedback controller is designed. This controller stands as a solution to the nonlinear optimal control problem under model uncertainty and external perturbations. To compute the controller’s feedback gains, an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. Finally, to implement state estimation-based control without the need to measure the entire state vector of the parallel robotic manipulator, the H-infinity Kalman filter is used as a robust state estimator. Among the advantages of this control method, one can note that (i) unlike the popular computed-torque method for robotic manipulators, the new control approach is characterized by optimality and is also applicable when the number of control inputs is not equal to the robot’s number of DOFs and (ii) it achieves fast and accurate tracking of reference set points under minimal energy consumption by the robot’s actuators.

Modeling and dynamic analysis of multi-loop RSSR-SS parallel manipulator

A spatial mechanism is frequently utilized in industrial applications such as parallel robot manipulators, machine positioning platforms, etc. The RSSR-SS Platform, with its 2-dof capacity, is a trailblazer in terms of parallel manipulator use. The present study focuses on developing control algorithms to increase performance by utilizing more structural system data. The Newton-Euler technique is used to construct the exact compact closed form of dynamic equations in the dimensionless form for the RSSR-SS manipulator. Rotary Spherical Spherical Rotary - Spherical Spherical (RSSR-SS) is a spatial mechanism with rigid body guidance and a passive degree of freedom. To analyze the dynamics of the RSSR-SS, an appropriate control system was developed using the Simulink and Sim Mechanics 2018b Simulink block technique. A Simulink model was created with the aid of the MATLAB 2018b translator. The simulation results illustrate the efficiency of the suggested approach, the dynamic equations of the RSSR-SS manipulator, and the influence of Leg/ Actuator inertia and its components on the dynamics of the entire system. Solidworks2021 was used to model the RSSR-SS. Finally, the article used to investigate dynamic issues with a certain RSSR-SS manipulator.

Deep regression of convolutional neural network applied to resolved acceleration control for a robot manipulator

This paper presents a modified resolved acceleration control scheme based on deep regression of the convolutional neural network. The resolved acceleration control scheme can achieve precise motion control of robot manipulators by regulating the accelerations of the end-effector, and the conventional scheme needs the position and orientation of the end-effector, which are obtained through the direct kinematics of the robot manipulator. This scheme increases the computational loads and might obtain inaccurate position and orientation due to mechanical errors. To overcome the drawbacks, a camera is used to capture the images of the robot manipulator, and then a deep regression of convolutional neural network is imposed into the resolved acceleration control to obtain the position and orientation of the end-effector. The proposed approach aims to enhance the positioning accuracy, to reduce the computational loads, and to facilitate the deep regression in real-time control. In this study, the proposed approach is applied to a 3-DOF planar parallel robot manipulator, and the results are compared with those by the conventional resolved acceleration control and a visual servo-based control. The results show that those objectives are achieved. Furthermore, the robustness of the proposed approach is tested through only the partial image of the end-effector available, and the proposed approach still works functionally and effectively.

Workspace analysis of 3-DOF U-shape base planar parallel robotic motion stage using shape memory alloy restoration technique (SMART) linear actuators

The applications of lightweight planar parallel robotic manipulators are increasing enormously because of its various desirable characteristics such as low weight, lower inertia and higher stiffness. Higher accelerations and accuracies can be achieved in planar parallel manipulators. Also, shape memory alloy restoration technique (SMART)-based linear actuators are replacing huge and bulky linear actuators. This study presents the kinematic design of smart linearly actuated family of U-shape base planar parallel robotic manipulator. With the aid of solid modelling software, different available configurations were modelled and their workspace was analysed. The developed 3-DOF motion stages (18 unique configurations) were fabricated using fused deposition modelling process, and the top three configurations having higher workspace were further experimented. It is interesting to observe that the actual or experimental workspace of a particular manipulator configuration is further minimised from the predicted or feasible workspace. It is due to the presence of passive links, singularities, friction between the parts, heat dissipation, force distribution, stiffness, etc. The present study depicts the experimental workspace of the top three configurations, namely PPR-PRP-PRR, PRP-PPR-PRP and PRP-PPR-PRR. Since none of the experimental workspace observed is equal or higher than the model workspace, an efficiency loss in terms of workspace reduction was calculated to understand the acceptability of the configurations in different domains. Apart from the loss, the result disclosed that the actual workspace of all the manipulators was within the feasible workspace domain of mobile platform. The PPR-PRP-PRR manipulator was found to possess highest experimental workspace than other configurations. Note: P, P, and R refer to active prismatic, passive prismatic and passive revolute joints respectively.

Kinematics Analysis of A Parallel Robot with 3 DOF and 4 Segments in Pure Translation.(Dept.M)

In this paper we present the direct and inverse analysis of a geometrical model of an UPS manipulator with three degrees of freedom added to a PPP passive central segment. This structure provides a pure translation motion. We will also determine the relations between generalized and articular velocities by using the inverse Jacobian matrix, Further, we determine the reciprocal relations between cartesian and angular velocities of the end effector via articular velocities by direct re-inversion of the Jacobian matrix. This study aims of implementing the control of a parallel robot manipulator. A prototype of a parallel robot has been built up in our laboratory in order to validate the proposed models.

Radial basis function neural network control for parallel spatial robot

The derivation of motion equations of constrained spatial multibody system is an important problem of dynamics and control of parallel robots. The paper firstly presents an overview of the calculating the torque of the driving stages of the parallel robots using Kronecker product. The main content of this paper is to derive the inverse dynamics controllers based on the radial basis function (RBF) neural network control law for parallel robot manipulators. Finally, numerical simulation of the inverse dynamics controller for a 3-RRR delta robot manipulator is presented as an illustrative example.

Load Parameter Identification for Parallel Robot Manipulator Based on Extended Kalman Filter

Load is the main external disturbance of a parallel robot manipulator. This disturbance will cause dynamic coupling among different degrees of freedom and make heaps of model-based control methods difficult to apply. In order to compensate this disturbance, it is crucial to obtain an accurate dynamic model of load. However, in practice, the load is always uncertain and its dynamic parameters are arduous to know a priori. To cope with this problem, this paper proposes a novel and simple approach to identify the dynamic parameters of load. Firstly, the dynamic model of the parallel robot manipulator with uncertain load is established and the dynamic coupling caused by load is also analyzed. Then, according to the dynamic model, the excitation signal is designed and a weak nonlinear dynamic model is derived. Furthermore, the identification model is presented and the identification algorithm based on the extended Kalman filter is designed. Lastly, numerical simulation results, obtained using a six-degree-of-freedom Gough–Stewart parallel manipulator, demonstrate the good estimation performance of the proposed method.