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The purpose of this paper is to improve the positioning accuracy of 6-Dof serial robot by the way of error compensation and sensitivity analysis.

In this paper, the Denavit–Hartenberg matrix is used to construct the kinematics models of the robot; the effects from individual joint and several joints on the end effector are estimated by simulation. Then, an error model based on joint clearance is proposed so that the positioning accuracy at any position of joints can be predicted for compensation. Through the simulation of the curve path, the validity of the error compensation model is verified. Finally, the experimental results show that the error compensation method can improve the positioning accuracy of a two joint exoskeleton robot by nearly 76.46%.

Through the analysis of joint error sensitivity, it is found that the first three joints, especially joint 2, contribute a lot to the positioning accuracy of the robot, which provides guidance for the accuracy allocation of the robot. In addition, this paper creatively puts forward the error model based on joint clearance, and the error compensation method which decouples the positioning accuracy into joint errors.

It provides a new idea for error modeling and error compensation of 6-Dof serial robot. Combining sensitivity analysis results with error compensation can effectively improve the positioning accuracy of the robot, and provide convenience for welding robot and other robots that need high positioning accuracy.

In traditional calibration methods of kinematics parameters of industrial robots, dozens of model parameters are identified together based on an optimization procedure. Due to different contributions of model parameter errors to the tool center point positioning error of industrial robots, obtaining good results for all model parameters is very difficult. Therefore, the purpose of this paper is to propose a sequential calibration method specifically for transmission ratio parameters, which includes reduction ratios and coupling ratios of industrial robot joints.

The ABB IRB 1410 industrial robot is considered as an example in this study. The transmission ratios for each joint of the robot are identified using the spatial circle fitting method based on spatial vectors, which fit the center and radius of joint rotation with the least squares optimization algorithm. In addition, a method based on the Rodrigues’ formula is designed and presented for identifying the actual coupling ratio of the robot. Subsequently, an experiment is carried out to verify the proposed sequential calibration method of transmission ratios.

In this experiment, the actual positions of the linkages before and after joint rotations are measured by a laser tracker. Accurate results of the reduction ratios and the coupling ratios are calculated, and the results are verified experimentally. The results show that by calibrating the reduction ratios and coupling ratios of the ABB robot, the rotation angle errors of the robot joints can be reduced.

The authors propose a sequential calibration method for transmission ratio parameters, including reduction ratios and coupling ratios of industrial robot joints. An experiment is carried out to verify this proposed sequential calibration method. This study may be beneficial for calibrating the kinematic parameters of industrial robots and improving their positioning accuracy.

The purpose of this paper is to describe a calibration method developed to improve the accuracy of a six degrees-of-freedom medical robot. The proposed calibration approach aims to enhance the robot’s accuracy in a specific target workspace. A comparison of five observability indices is also done to choose the most appropriate calibration robot configurations.

The calibration method is based on the forward kinematic approach, which uses a nonlinear optimization model. The used experimental data are 84 end-effector positions, which are measured using a laser tracker. The calibration configurations are chosen through an observability analysis, while the validation after calibration is carried out in 336 positions within the target workspace.

Simulations allowed finding the most appropriate observability index for choosing the optimal calibration configurations. They also showed the ability of our calibration model to identify most of the considered robot’s parameters, despite measurement errors. Experimental tests confirmed the simulation findings and showed that the robot’s mean position error is reduced from 3.992 mm before calibration to 0.387 mm after, and the maximum error is reduced from 5.957 to 0.851 mm.

This paper presents a calibration method which makes it possible to accurately identify the kinematic errors for a novel medical robot. In addition, this paper presents a comparison between the five observability indices proposed in the literature. The proposed method might be applied to any industrial or medical robot similar to the robot studied in this paper.

The purpose of this paper is to propose an error model for serial robot kinematic calibration based on dual quaternions.

The dual quaternions are the combination of dual-number theory and quaternion algebra, which means that they can represent spatial transformation. The dual quaternions can represent the screw displacement in a compact and efficient way, so that they are used for the kinematic analysis of serial robot. The error model proposed in this paper is derived from the forward kinematic equations via using dual quaternion algebra. The full pose measurements are considered to apply the error model to the serial robot by using Leica Geosystems Absolute Tracker (AT960) and tracker machine control (T-MAC) probe.

Two kinematic-parameter identification algorithms are derived from the proposed error model based on dual quaternions, and they can be used for serial robot calibration. The error model uses Denavit–Hartenberg (DH) notation in the kinematic analysis, so that it gives the intuitive geometrical meaning of the kinematic parameters. The absolute tracker system can measure the position and orientation of the end-effector (EE) simultaneously via using T-MAC.

The error model formulated by dual quaternion algebra contains all the basic geometrical parameters of serial robot during the kinematic calibration process. The vector of dual quaternion error can be used as an indicator to represent the trend of error change of robot’s EE between the nominal value and the actual value. The accuracy of the EE is improved after nearly 20 measurements in the experiment conduct on robot SDA5F. The simulation and experiment verify the effectiveness of the error model and the calibration algorithms.

Real-time implementation of sophisticated algorithms on robotic systems demands a rewarding interface between hardware and software components. Individual robot manufacturers have dedicated controllers and languages. However, robot operation would require either the knowledge of additional software or expensive add-on installations for effective communication between the robot controller and the computation software. This paper aims to present a novel method of interfacing the commercial robot controllers with most widely used simulation platform, e.g. MATLAB in real-time with a demonstration of visual predictive controller.

A remote personal computer (PC), running MATLAB, is connected with the IRC5 controller of an ABB robotic arm through the File Transfer Protocol (FTP). FTP server on the IRC5 responds to a request from an FTP client (MATLAB) on a remote computer. MATLAB provides the basic platform for programming and control algorithm development. The controlled output is transferred to the robot controller through Ethernet port as files and, thereby, the proposed scheme ensures connection and control of the robot using the control algorithms developed by the researchers without the additional cost of buying add-on packages or mastering vendor-specific programming languages.

New control strategies and contrivances can be developed with numerous conditions and constraints in simulation platforms. When the results are to be implemented in real-time systems, the proposed method helps to establish a simple, fast and cost-effective communication with commercial robot controllers for validating the real-time performance of the developed control algorithm.

The proposed method is used for real-time implementation of visual servo control with predictive controller, for accurate pick-and-place application with different initial conditions. The same strategy has been proven effective in supervisory control using two cameras and artificial neural network-based visual control of robotic manipulators.

This paper elaborates a real-time example using visual servoing for researchers working with industrial robots, enabling them to understand and explore the possibilities of robot communication.

The purpose of this paper is to enhance control accuracy, energy efficiency and productivity of customized industrial robots by the proposed multi-objective trajectory optimization approach. To obtain accurate dynamic matching torques of the robot joints with optimal motion, an improved dynamic model built by a novel parameter identification method has been proposed.

This paper proposes a novel multi-objective optimal approach to minimize the time and energy consumption of robot trajectory. First, the authors develop a reliable dynamic parameters identification method to obtain joint torques for formulating the normalized energy optimization function and dynamic constraints. Then, optimal trajectory variables are solved by converting the objective function into relaxation constraints based on second-order cone programming and Runge–Kutta discrete method to reduce the solving complexity.

Extensive experiments via simulation and in real customized robots are conducted. The results of this paper illustrate that the accuracy of joint torque predicted by the proposed model increases by 28.79% to 79.05% over the simplified models used in existing optimization studies. Meanwhile, under the same solving efficiency, the proposed optimization trajectory consumes a shorter time and less energy compared with the existing optimization ones and the polynomial trajectory.

A novel time-energy consumption optimal trajectory planning method based on dynamic identification is proposed. Most existing optimization methods neglect the effect of dynamic model reliability on energy efficiency optimization. A novel parameter identification approach and a complete dynamic torque model are proposed. Experimental results of dynamic matching torques verify that the control accuracy of optimal robot motion can be significantly improved by the proposed model.

Removing the bone flap is a compulsory step in open skull surgeries and is very cumbersome and time-consuming. Exerting large forces during the milling and cutting of the skull renders the surgeon exhausted and consequently increases probable errors in further task of manipulating the sensitive brain tissue. This paper aims to present the development of a robotic system capable of perforating and cutting the required bone flap without restraining the surgeon.

For the purpose of optimization, the target workspace is estimated by 3D modeling of the sample skull and bone flaps of targeted surgeries. The optimization considers kinematic performance matrices and the extracted workspace requirements by assigning scores to each possible design and finally selects the design with highest score.

The design utilizes a parallel remote center of motion mechanism. Coordinating the remote center of motion (RCM) of the mechanism with the center of a sphere which circumscribes the skull, the milling tool is always nearly perpendicular to the skull bone. The paper presents the concept design, optimization criteria and finally the optimal design of the robot and the fabricated prototype. Tests indicate that the prototype is able to sweep the target workspace and to exert the required forces for bone milling.

The workspace requirements of the craniotomy/craniectomy surgeries are investigated and converted into one quantitative target workspace. An optimized design for a surgical robot is developed which satisfies the workspace requirements of the targeted surgeries.

In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.

A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.

Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.

First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

The characteristic of static is quite important especially for the manipulator with force feedback. This paper aims to improve the traditional static model by considering the limitations such as lacking of versatility and ignoring gravity of links. For this purpose, a new asymmetric mass distribution method on the analysis of universal “force-sensing” model has been put forward to overcome the limitations.

Through the forces and torques analysis of every link and the moving platform, the static model of 3-RUU manipulator is acquired. Then, based on the physical meaning analysis of every part in the static model of 3-RUU manipulator, a new asymmetric mass distribution method on the analysis of universal “force-sensing” model can be obtained.

The correctness of the static model of 3-RUU manipulator is verified by simulation results based on Pro/Engineer software and Adams software. Furthermore, experiment results based on a manipulator similar to the Omega.3 manipulator indicate that the universal “force-sensing” model can be applicable to the above manipulator.

A new asymmetric mass distribution method on the analysis of universal static mathematical model has been put forward. Based on physical meaning of the above method, the “force-sensing” model can be established quickly and it owns versatility, which can be applicable to the 3-RUU manipulator, the Omega.3 parallel manipulator and other similar manipulators with force feedback. In addition, it can improve the accuracy of the “force-sensing” model to a great extent.

This paper aims to develop a new real‐time effective method for solving the inverse kinematics (IK) problem, especially for those manipulators with high‐dimensional nonlinear kinematic equations.

The paper transforms the IKs problem into a minimization problem. Then, a novel meta‐heuristic algorithm, called the electromagnetism‐like method (EM), is used to solve this equivalent problem. Moreover, in order to further improve the computational efficiency and accuracy of EM, a hybrid method which combines EM with the Davidon‐Fletcher‐Powell (DFP) method is proposed.

The results showed that EM is a powerful yet easy algorithm for solving the IKs problem of robot manipulators. Its complexity is independent on the characteristics of the kinematic equations involving dimensionality and the degree of nonlinearity. Moreover, EM can be used as an accompanying algorithm for DFP method to get better precision at a lower iteration number.

The method developed in this paper is a generalized approach that is efficient enough to obtain IK solutions independent of robot geometry and the number of degrees of freedom.