Link Robot Research Articles

Application of Barycentric Coordinates and the Jacobian Matrix to the Analysis of a Closed Structure Robot

A new approach is presented to study the kinematic properties of stationary robots with a closed structure. It combines the application of conventional methods from kinematics with geometric parameters represented in a barycentric coordinate system. This allows examining the influence of the proportions of the robot’s links on its basic mechanical characteristics. Each point from the newly introduced barycentric space corresponds to a set of robots with the same link proportions. The proposed approach is used to study three aspects: the link proportions for which the robot can exist; the shape of the robot’s workspace; and the possible singular configurations. This is valuable when evaluating the qualities of existing robots and could be applied to the design of new mechanical systems. An example of a 5-link robot with a closed structure is considered. The conditions for the existence of the mechanism and the conditions under which certain types of singular configurations can occur are defined. The example reveals the great potential of combining barycentric coordinates and Jacobian properties. The barycentric coordinates of 10 robots with a 5-link closed structure known from the literature are determined, and their properties are analyzed. The results are presented graphically. An extension of the application area of the approach is discussed.

Adaptive performance optimal control for flexible-joint robots with random noises: Design and experiment

This study developes a flexible performance optimal control scheme via reinforcement learning strategy and event-triggered mechanism for flexible-joint robots with random noise and non-affine input. It is notable that an event-triggered optimization mechanism is developed, which meets the optimality principle and saves communication resources. Nevertheless, the existing event-triggered strategy is unable to handle non-affine input, which limits the applicability of this method. To overcome the above problems, a modified event-triggered mechanism is proposed. At the same time, the optimal solution of the system is given by an optimized control algorithm based on the improved performance index function. In the controller design, neural network is used to deal with random disturbances and uncertainties, and an adaptive law is designed to replace the identifier structure. Besides, a flexible prescribed performance function is constructed to yield multiple prescribed performance behaviors by adjusting the key parameters, while the tracking error is stayed within a prescribed boundary. Finally, the effectiveness of the proposed control scheme is further demonstrated by simulation and the experiment of the 2-link flexible-joint robot on the Quanser platform.

A Method for Simulating the Positioning Errors of a Robot Gripper

The research is aimed at creating a methodology for increasing the positioning accuracy of an industrial robot and minimizing the vibration of the robot gripper by applying machine learning based on the developed mathematical model for estimating the positioning error. Two components of positioning accuracy are considered: geometric and kinematic errors and elastic static deformations. The dynamic error in the partial system of motion of the robot manipulator links is analyzed. The equation of partial motions is obtained from Lagrange’s differential equation of motion of the II kind. The system of differential equations for the positioning error was solved analytically by Euler’s method. An example of modeling the position and orientation error of the gripper due to temperature deformations of the third link for the manipulator scheme is given. An example of the modeling of static deformations and errors of the manipulator with elastic pliability of the robot links is given. An example of dynamic error modeling in a partial system of motion of the robot links is given. The proposed method of modeling robot gripper positioning errors makes it possible to increase the positioning accuracy of the industrial robot and minimize the vibration of the gripper. Having a mathematical model of positioning errors, it is possible to compensate for the positioning error by changing the speed of movement of the gripper reference point before determining the direct kinematic task.

Study on Dynamic Scanning Trajectory of Large Aerospace Parts Based on 3D Scanning

The aim of manufacturing large aerospace parts for the three-dimensional scanning field demands high precision and efficiency. However, it may be more challenging to meet the full coverage of the measurement problems for large aerospace parts with the scanning range of traditional three-dimensional scanning methods. This paper establishes a dynamic posturing scanning measurement system for large aerospace parts with a six-degree-of-freedom posturing platform and a six-degree-of-freedom industrial robot linkage. It establishes a mathematical model of dynamic three-dimensional scanning posturing. It proposes a platform attitude adjustment strategy based on the field of view angle of a 3D scanner during the adjustment of a six-degree-of-freedom platform. The dynamic scanning path planning is carried out using the three-dimensional spatial decomposition method, and the vector coordinates of the critical points at the edges of the missing areas of the scan are used to re-scan the missing areas to establish the dynamic scanning paths of large aerospace parts. It is experimentally verified that the system can realize the dynamic scanning of complex curved large aerospace parts. The experimental results show that the measurement efficiency is improved by more than 75%, and the point cloud coverage of the scanning reconstruction is improved by 18% for large aerospace components with complex surfaces.

Kinematic modelling of three link robot manipulator and joint torque optimization using genetic algorithm in MATLAB

This research article presents the non-linear dynamic of a three-link robotic manipulator formulated by the Newton-Euler method. The planar manipulator is composed of three links and three revolute joints rotating about the z-axis. The three nonlinear non-homogeneous dynamic equations have been solved graphically with the help of MATLAB by phase variable method. The work represents the graphical solution of the transient response of angular position, and angular velocity of each link member for a predetermined interval of time. With the help of simulated value from MATLAB, torque characteristics have been determined for different torque ratios and optimum torque has been derived using a genetic algorithm to move the manipulator in a proper direction.

Topology optimisation of multiple robot links considering screw connections

AbstractThis paper presents a method for the lightweight design of robotic links subject to dynamic loads and requirements on the overall system stiffness. It includes (1) a decomposition scheme to enable separate component optimization and (2) an approach based on topology optimization for optimal load path design of screw connections. The approach reduces computing cost and mass of designs with screw connections.

A Reconfigurable Soft Linkage Robot via Internal "Virtual" Joints.

Traditional robots derive their capabilities of movement through rigid structural "links" and discrete actuated "joints." Alternatively, soft robots are composed of flexible materials that permit movement across a continuous range of their body and appendages and thus are not restricted in where they can bend. While trade-offs between material choices may restrain robot functionalities within a narrow spectrum, we argue that bridging the functional gaps between soft and hard robots can be achieved from a hybrid design approach that utilizes both the reconfigurability and the controllability of traditional soft and hard robot paradigms. In this study, we present a hybrid robot with soft inflated "linkages," and rigid internal joints that can be spatially reconfigured. Our method is based on the geometric pinching of an inflatable beam to form mechanical pinch-joints connecting the inflated robot linkages. Such joints are activated and controlled via internal motorized modules that can be relocated for on-demand joint-linkage configurations. We demonstrate two applications that utilize joint reconfigurations: a deployable robot manipulator and a terrestrial crawling robot with tunable gaits.

Machine Learning-Based Multiagent Control for a Bunch of Flexible Robots

In this paper, two novel methodologies of employing machine learning (here, the type-2 fuzzy system) are presented to control a multiagent system in which the agents are flexible joint robots. In the previous methods, the static mode controller has been investigated, which has little flexibility and cannot measure all the states of the system, but in the method presented in this paper, we can eliminate these disadvantages. The control signal is consisting of feedback from the output and the estimated states of the system. In the first method, the control signal coefficients are calculated from the linear matrix inequality (LMI), followed by a type-2 fuzzy system that adds the compensation signal to the control signal. In the second method, the type-2 fuzzy system is directly used to estimate the control signal coefficients which do not employ LMI. Both methods have their disadvantages and benefits, so in general, one of these two methods cannot be considered superior. To prove the effectiveness of the two proposed methods, a topology with four robots has been considered. Both proposed methods have been evaluated for controlling the angle and speed of the robot link. Also, another simulation was made without using the fuzzy system to verify the importance of our methods. Simulation results indicate the proper efficiency of proposed methods, especially in presence of uncertainty in the system.

Kinematic and Joint Compliance Modeling Method to Improve Position Accuracy of a Robotic Vision System.

In the field of robotic automation, achieving high position accuracy in robotic vision systems (RVSs) is a pivotal challenge that directly impacts the efficiency and effectiveness of industrial applications. This study introduces a comprehensive modeling approach that integrates kinematic and joint compliance factors to significantly enhance the position accuracy of a system. In the first place, we develop a unified kinematic model that effectively reduces the complexity and error accumulation associated with the calibration of robotic systems. At the heart of our approach is the formulation of a joint compliance model that meticulously accounts for the intricacies of the joint connector, the external load, and the self-weight of robotic links. By employing a novel 3D rotary laser sensor for precise error measurement and model calibration, our method offers a streamlined and efficient solution for the accurate integration of vision systems into robotic operations. The efficacy of our proposed models is validated through experiments conducted on a FANUC LR Mate 200iD robot, showcasing notable improvements in the position accuracy of robotic vision system. Our findings contribute a framework for the calibration and error compensation of RVS, holding significant potential for advancements in automated tasks requiring high precision.

Application of the crow search algorithm to achieve an optimal solution for a double link manipulator

Robotic manipulators are mechanical devices that are electronically controlled and have several linkages that allow them to do specific jobs under specified situations. Here, a double-link manipulator is explored and modified using uncertainty factors that impact the robot manipulator's performance in monitoring the angular position. Unlike, the standard Lagrangian technique is employed to derive the motion equations and calculate the performance of the Two Link Robot Manipulator. Because the motion equation involves a nonlinear system of ordinary differential equations, getting to the target place is challenging. Meta-heuristic search algorithms were standard methodologie to solve any optimization problem, mainly real-time problems. Meta-heuristic algorithms depend upon the natural behaviour of the animals or birds. This paper uses a Crow search algorithm to minimize the trajectory and position of the double link manipulator. It also demonstrates how the Crow search algorithm can easily be used to solve optimal control problems (OCP) with constraints in MATLAB simulation. The Crow search algorithm depends on the crow's behaviour of keeping their excess food in a safe place and getting it as needed. The Crow search algorithm (CSA) was compared with particle swarm optimization (PSO), differential evolution (DE), and Grey Wolf Optimization (GWO). The double link manipulator's fundamental Model's performance is computed to find a solution. The Crow search method has produced notable results in terms of performance, i.e. 25 % to 98 % better, compared to other algorithms such as DE, PSO, and GWO.

Development of a low-cost upper-body rehabilitation robot for home use.

The inability to use one's hands or arms greatly restricts the ability to perform daily activities. After a developmental or acquired injury, the intensity and frequency of rehabilitation exercises are essential. To alleviate the burden on the healthcare system, robotic systems have been developed to support clinicians' interventions. However, these systems are often bulky and expensive, limiting their use to specific clinical settings and making them impractical for home use. This paper presents the development of an affordable and easy to install 2-DOF five-bar linkage robot designed to be used at home. This work aims to reduce the cost of the robot through actuation optimization, mechanical optimization and 3D printing. The architecture and links length are chosen to optimize the robot's performance in the required workspace. Using sensor feedback, impedance control algorithms and multiple types of exercise such as virtual walls guidance are implemented. Finally, a user interface was programmed to facilitate the robot's use.

Novel robot arm concept for lightweighting high-performance machinery

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

Parametric optimization of a multi-output electromagnetic mechatron module

The article is devoted to the problem of determining the optimal dimensions of the design of reciprocating linear electromagnetic engine. As an optimization criterion, the parameters of the mechatron module based on the multi-output linear electromagnetic motor are determined by means of a constructive calculation scheme and its variable parameters are determined by the method of non-linear programming, the geometric dimensions are simplified, the mass of the electromagnetic motor body, the mass of the motor armature and the masses of the motor stator and the restrictions on their dimensions are determined the issues of finding optimal solutions are considered. Optimization of mechatronic modules based on multi-output linear electromagnetic motors on various parameters leads to increase in dynamic performance of the executive organ, saving energy consumption, and allows to improve performance and service quality. Also, based on the analysis of the existing electromagnetic linear motors, the structural scheme of the mechatron module based on the multi-output linear electromagnetic engines, which allows to perform step movements according to spatial coordinates with high accuracy and speed, is presented. The algorithmic basis of computing is based on the weight and size characteristics of execution mechanisms, and it has been proposed to allow solving optimal design tasks of execution mechanisms taking into account zvenolar lengths, displacements, speeds and accelerations during movement, as well as creating robotic systems with high performance in service. This structure serves to ensure the interaction of the executive body with the object in the links of industrial robots.

Hybrid controller with neural network PID/FOPID operations for two-link rigid robot manipulator based on the zebra optimization algorithm.

The performance of the robotic manipulator is negatively impacted by outside disturbances and uncertain parameters. The system's variables are also highly coupled, complex, and nonlinear, indicating that it is a multi-input, multi-output system. Therefore, it is necessary to develop a controller that can control the variables in the system in order to handle these complications. This work proposes six control structures based on neural networks (NNs) with proportional integral derivative (PID) and fractional-order PID (FOPID) controllers to operate a 2-link rigid robot manipulator (2-LRRM) for trajectory tracking. These are named as set-point-weighted PID (W-PID), set-point weighted FOPID (W-FOPID), recurrent neural network (RNN)-like PID (RNNPID), RNN-like FOPID (RNN-FOPID), NN+PID, and NN+FOPID controllers. The zebra optimization algorithm (ZOA) was used to adjust the parameters of the proposed controllers while reducing the integral-time-square error (ITSE). A new objective function was proposed for tuning to generate controllers with minimal chattering in the control signal. After implementing the proposed controller designs, a comparative robustness study was conducted among these controllers by altering the initial conditions, disturbances, and model uncertainties. The simulation results demonstrate that the NN+FOPID controller has the best trajectory tracking performance with the minimum ITSE and best robustness against changes in the initial states, external disturbances, and parameter uncertainties compared to the other controllers.

Neural networks-based adaptive command filter control for nonlinear systems with unknown backlash-like hysteresis and its application to single link robot manipulator

<abstract><p>In this paper, an adaptive neural network control problem for nonstrict-feedback nonlinear systems with an unknown backlash-like hysteresis and bounded disturbance was presented. Radial basis function neural networks (RBFNN) were used to approximate the unknown functions and the problem of the explosion of complexity problem was handled by utilizing the command filter method. Furthermore, the influence of an unknown backlash-like hysteresis input was addressed by approximating an intermediate variable. Based on the backstepping method and the command filter technique, an adaptive neural network controller was designed via the approximation abilities of RBFNN. With the help of the Lyapunov stability theory, the proposed controller ensures that all of the signals in closed-loop systems are bounded and that the tracking error fluctuates close to the origin within a bounded area. Finally, a real-world example based on the single-link manipulator was shown to demonstrate the viability of the presented approach.</p></abstract>

A 2-Link Robot with Adaptive Sliding Mode Controlled by Barrier Function

A 2-Link Robot with Adaptive Sliding Mode Controlled by Barrier Function

ROBOTIC TECHNOLOGICAL CELLULTRASONIC FLAW DETECTION

Ultrasonic flaw detection is the most versatile non-destructive testing method for detecting defects in various materials. The aim of this work is to develop a robotic technological cell for ultrasonic flaw detection of welded products. To achieve this goal, we selected the necessary equipment, developed an algorithm for the control system, and wrote control and data collection programs. In addition to identifying the product defect, the PC-based control system also ensures safety during the diagnostic process.
 The technical support of the cell consists of an ultrasonic flaw detector "EPOCH 650" with a rectangular pulse generator and a digital receiver, an industrial robot Mitsubishi Electric MELFA RV-FR series, a work table for the required part orientation, inductive sensors for identifying the installation of the part, light barriers to ensure safe operation of the cell and a control system.
 The cell control system is implemented in the form of a chalkboard and includes a central processing device for cell control, a robot control processor for implementing the robot's trajectory and communication with the robot controller, a module for communication with peripheral equipment, and a data logging module for registration, recording and sharing of events in the control system, a digital input module for receiving digital signals of 0 or 24 V DC from contact type sensors, manual control buttons, status signals from alarm systems, positioning drives, etc.
 The cell software ensures the implementation of control algorithms and consists of a control program to control the robot's movements, an executive program to ensure the control system's analysis of signals from the flaw detector, a security system, and user interaction via the system interface, The interface system receives data from the PLC and is responsible for graphically displaying information about the control system.
 The paper presents the results of experimental research and simulation, which confirm the efficiency and effectiveness of the developed algorithms and control programs. By determining the optimal values of the speeds of movement and rotation of the robot links during their simultaneous movement, the speed and productivity of the cell are increased.

Uncertainty and velocity observer-based predefined-time nonsingular terminal sliding mode control of the underwater robot manipulators

This paper introduces an observer-based predefined-time nonsingular terminal sliding mode control to the position tracking control of the n-degree-of-freedoms underwater robot manipulators subject to the uncertainty compensation and sensor reduction. The proposed sliding surface has only the sliding phase, while the reaching phase is eliminated using a continuous augmented function. Besides, the sliding phase is predefined-time, where it is independent of the initial conditions and control parameters. As a result, it has led to the development of a predefined-time control scheme, in which not only its convergence time is fast, but also (due to the elimination of the reaching phase) the effect of uncertainties in this phase is eliminated. Naturally, there is a wide range of uncertainties such as structured-unstructured uncertainties and/or un-modeled dynamics, ocean current, waves, etc., in the underwater robot manipulators and underwater conditions. Therefore, the proposed predefined-time nonsingular terminal sliding mode control is combined with a finite-time observer to estimate the underwater robot manipulator's joint velocity and eliminate the destructive effect of existing uncertainties. Under this combination, the stability analysis could guarantee predefined-time global asymptotic stability of the system in the presence of existing uncertainties. Simulation results are conducted on a 2-link underwater robot manipulator to validate the proposal.

Adaptive control based neural network sliding mode approach for two links robot

<span lang="EN-US">In order to improve the control accuracy of the robot manipulator, the sliding mode control combined with the adaptive neural network (ANNSMC) is proposed. Sliding mode control (SMC) is a nonlinear control recognized for its efficiency, easy tuning and implementation, accuracy and robustness. However, higher amplitude of chattering is produced due to the higher switching gain to handle the large uncertainties. For the purpose of reducing this gain, the uncertain parts of the system are estimated using neural network (NN) with on-line training using back propagation (BP) technique. The results of the online interconnection weights between the input and the hidden layers and between the hidden and the output layers are injected offline in order to improve the network performance in term of the convergence speed. In order to reduce the response time caused by the online training, the obtained output and input weights are updated using the adaptive laws derived from the Lyapunov stability approach</span><span lang="EN-US">the proposed control ANNSMC has improved the convergence speed with 41.13% for the first link and 40.15% for the second link comparing to NNSMC. The simulation result illustrates the performance of the proposed approach by using MATLAB and the control action suggested did not manifest any chattering behavior.</span>

A decoupled Bayesian method for snake robot control in unstructured environment

This paper presents a method which avoids the common practice of using a complex coupled snake robot model and performing kinematic analysis for control in cluttered environments. Instead, we introduce a completely decoupled dynamical Bayesian formulation with respect to interacted snake robot links and environmental objects, which requires much lower complexity for efficient and robust control. When a snake robot does not interact with obstacles, it runs by a simple serpenoid controller. However, when it exhibits interaction with environments, defined as close proximity or collision with targets and/or obstacles, we extend the conventional Bayesian framework by modeling such interactions in terms of stimuli. The proposed ‘multi-neural-stimulus function’ represents the cumulative effect of both external environmental influences and internal constraints of the snake robot. It implicitly handles the ‘unexpected collision’ problem and thus solve the difficult data association and shape adjustment problems for snake robot control in an innovative way. Preliminary experimental results have demonstrated promising performance of the proposed method comparing with the state-of-the-art.