Robot Identification Research Articles

A method for dynamic parameter identification of an industrial robot based on frequency response function

AbstractHaving accurate values of the dynamic parameters is necessary to characterize the dynamic behaviors of mechanical systems and for the prediction of their responses. To accurately describe the dynamic characteristics of industrial robots (IRs), a new method for dynamic parameter identification is proposed in this study with the goal of developing a real IR dynamics model that combines the multibody system transfer matrix method (MSTMM) and the nondominated sorting genetic algorithm‐II (NSGA‐II). First, the multibody dynamics model of an IR is developed using the MSTMM, by which its frequency response function (FRF) is calculated numerically. Then, the experimental modal analysis is conducted to measure the IR's actual FRF. Finally, the objective function of the minimum errors between the calculated and measured eigenfrequencies and FRFs are constructed to identify the dynamic parameters of the IR by the NSGA‐II algorithm. The simulated and experimental results illustrate the effectiveness of the methodology presented in this paper, which provides an alternative to the identification of IR dynamic parameters.

Lie-theory-based dynamic model identification of serial robots considering nonlinear friction and optimal excitation trajectory

Abstract Accurate dynamic model is essential for the model-based control of robotic systems. However, on the one hand, the nonlinearity of the friction is seldom treated in robot dynamics. On the other hand, few of the previous studies reasonably balance the calculation time-consuming and the quality for the excitation trajectory optimization. To address these challenges, this article gives a Lie-theory-based dynamic modeling scheme of multi-degree-of-freedom (DoF) serial robots involving nonlinear friction and excitation trajectory optimization. First, we introduce two coefficients to describe the Stribeck characteristics of Coulomb and static friction and consider the dependency of friction on load torque, so as to propose an improved Stribeck friction model. Whereafter, the improved friction model is simplified in a no-load scenario, a novel nonlinear dynamic model is linearized to capture the features of viscous friction across the entire velocity range. Additionally, a new optimization algorithm of excitation trajectories is presented considering the benefits of three different optimization criteria to design the optimal excitation trajectory. On the basis of the above, we retrieve a feasible dynamic parameter set of serial robots through the hybrid least square algorithm. Finally, our research is supported by simulation and experimental analyses of different combinations on the seven-DoF Franka Emika robot. The results show that the proposed friction has better accuracy performance, and the modified optimization algorithm can reduce the overall time required for the optimization process while maintaining the quality of the identification results.

Tracking the trajectory of a swarm of mobile robots with a computer vision system

Swarm robotics research uses a range of tools for evaluating the behaviors and metrics of robot collectives. One crucial tool involves the capability to track each robot’s position and orientation at various intervals, enabling the reconstruction of individual robot poses and trajectories. Comprehensive analysis of swarm behavior hinges on the study of the collective trajectories of each robot within the group. This paper demonstrates the implementation of a computer vision system, utilizing a webcam and Python scripts, to effectively track a mobile robot group within a swarm. This shows the feasibility of developing such research tools using commonplace computing equipment. The design and development of the vision system, including a detailed calibration procedure, robot identification methods, and practical examples, are also shown. Furthermore, it offers an exhaustive explanation of the robot tracking process. Experimental trials with three robots validate the system’s ability to extract images from video feeds and accurately identify each robot. Subsequently, after image processing, the system generates a dataset encompassing image numbers, robot IDs, x and y positions, and orientations.

Nonlinear dynamic model identification of robots: application to collision detection using a finite-time nonlinear momentum observer

Nonlinear dynamic model identification of robots: application to collision detection using a finite-time nonlinear momentum observer

Parameters identification and contact interaction control of redundant robot based on dynamic model

Human-redundant robot contact interaction control based on joint current is conducted. This is a sensorless control method. The key issues involved are dynamic parameters identification and interaction control strategy of robot. Firstly, kinematics model and linear dynamic model of robot are established and dynamic parameter matrix to be identified is obtained. Pattern search method is adopted to optimize motion trajectory of robot. Dynamic parameters is obtained by using least square method. The results indicate that the calculated current has a consistent trend with actual measured value, and the current error is small. In order to achieve independent control in null space, motion decoupling model of robot is established, and impedance control is applied to null space. When the force is applied to robot, based on impedance control and joint current changes, robot can move along the force direction, so the motion in null space can be controlled. Aiming at null space contact control during end force control task, comprehensive interaction control based on force sensor and joint current is conducted. The results show that robot can also move in compliance with human hand and achieve compliant interaction of null space when force is applied to the joint space.

Kinematic Parameter Identification and Error Compensation of Industrial Robots Based on Unscented Kalman Filter with Adaptive Process Noise Covariance

Kinematic calibration plays a pivotal role in enhancing the absolute positioning accuracy of industrial robots, with parameter identification and error compensation constituting its core components. While the conventional parameter identification method, based on linearization, has shown promise, it suffers from the loss of high-order system information. To address this issue, we propose an unscented Kalman filter (UKF) with adaptive process noise covariance for robot kinematic parameter identification. The kinematic model of a typical 6-degree-of-freedom industrial robot is established. The UKF is introduced to identify the unknown constant parameters within this model. To mitigate the reliance of the UKF on the process noise covariance, an adaptive process noise covariance strategy is proposed to adjust and correct this covariance. The effectiveness of the proposed algorithm is then demonstrated through identification and error compensation experiments for the industrial robot. Results indicate its superior stability and accuracy across various initial conditions. Compared to the conventional UKF algorithm, the proposed approach enhances the robot’s accuracy stability by 25% under differing initial conditions. Moreover, compared to alternative methods such as the extended Kalman algorithm, particle swarm optimization algorithm, and grey wolf algorithm, the proposed approach yields average improvements of 4.13%, 26.47%, and 41.59%, respectively.

Identification and Control of Flexible Joint Robots Based on a Composite-Learning Optimal Bounded Ellipsoid Algorithm and Prescribe Performance Control Technique

This paper presents an indirect adaptive neural network (NN) control algorithm tailored for flexible joint robots (FJRs), aimed at achieving desired transient and steady-state performance. To simplify the controller design process, the original higher-order system is decomposed into two lower-order subsystems using the singular perturbation technique (SPT). NNs are then employed to reconstruct the aggregated uncertainties. An adaptive prescribed performance control (PPC) strategy and a continuous terminal sliding mode control strategy are introduced for the reduced slow subsystem and fast subsystem, respectively, to guarantee a specified convergence speed and steady-state accuracy for the closed-loop system. Additionally, a composite-learning optimal bounded ellipsoid algorithm (OBE)-based identification scheme is proposed to update the NN weights, where the tracking errors of the reduced slow and fast subsystems are integrated into the learning algorithm to enhance the identification and tracking performance. The stability of the closed-loop system is rigorously established using the Lyapunov approach. Simulations demonstrate the effectiveness of the proposed identification and control schemes.

Adhesion Coefficient Identification of Wheeled Mobile Robot under Unstructured Pavement.

Because of its uneven and large slope, unstructured pavement presents a great challenge to obtaining the adhesion coefficient of pavement. An estimation method of the peak adhesion coefficient of unstructured pavement on the basis of the extended Kalman filter is proposed in this paper. The identification accuracy of road adhesion coefficients under unstructured pavement is improved by introducing the equivalent suspension model to optimize the calculation of vertical wheel load and modifying vehicle acceleration combined with vehicle posture data. Finally, the multi-condition simulation experiments with Carsim are conducted, the estimation accuracy of the adhesion coefficient is at least improved by 3.6%, and then the precision and effectiveness of the designed algorithm in the article are verified.

Meaningful human control and variable autonomy in human-robot teams for firefighting.

Introduction: Humans and robots are increasingly collaborating on complex tasks such as firefighting. As robots are becoming more autonomous, collaboration in human-robot teams should be combined with meaningful human control. Variable autonomy approaches can ensure meaningful human control over robots by satisfying accountability, responsibility, and transparency. To verify whether variable autonomy approaches truly ensure meaningful human control, the concept should be operationalized to allow its measurement. So far, designers of variable autonomy approaches lack metrics to systematically address meaningful human control. Methods: Therefore, this qualitative focus group (n = 5 experts) explored quantitative operationalizations of meaningful human control during dynamic task allocation using variable autonomy in human-robot teams for firefighting. This variable autonomy approach requires dynamic allocation of moral decisions to humans and non-moral decisions to robots, using robot identification of moral sensitivity. We analyzed the data of the focus group using reflexive thematic analysis. Results: Results highlight the usefulness of quantifying the traceability requirement of meaningful human control, and how situation awareness and performance can be used to objectively measure aspects of the traceability requirement. Moreover, results emphasize that team and robot outcomes can be used to verify meaningful human control but that identifying reasons underlying these outcomes determines the level of meaningful human control. Discussion: Based on our results, we propose an evaluation method that can verify if dynamic task allocation using variable autonomy in human-robot teams for firefighting ensures meaningful human control over the robot. This method involves subjectively and objectively quantifying traceability using human responses during and after simulations of the collaboration. In addition, the method involves semi-structured interviews after the simulation to identify reasons underlying outcomes and suggestions to improve the variable autonomy approach.

Research on space attitude recognition and control method for soft robotics based on neural networks

In the modern field of biomedical robotics, there is increasing attention on the use of soft robots as carriers for surgical devices. Compared to rigid robots, soft robots exhibit more complex postures and work environments, requiring new methods for recognizing and controlling their postures within biological organisms. This paper investigates the recognition and control of the posture of soft robots using a neural network learning approach, utilizing a soft robot equipped with resistive sensors. By establishing the relationship between changes in resistance values and posture variations, the study successfully achieves the identification and control of the soft robot's posture. The obtained posture data based on resistance values are validated for reliability. Thus, by combining resistive sensors with soft robots and employing neural network analysis, the recognition and control of soft robots postures within biological organisms can be achieved.

Modular Model and Simulation for Process Optimisation in Advanced Material Recovery Facilities (MRFs)

At a time when the supply of critical materials is threatened, waste recycling and reuse is an essential solution for human development. The role of Material Recovery Facilities (MRFs) to deliver efficiently high-purity material fractions as feedstock cannot be underestimated. However, MRF sorting processes need to remain adaptive with evolving smart technologies and systems that further enhance their effectiveness. For example, a re-designed MRF with AI-based robotics can improve the performance of waste recycling, leading to significant economic and environmental benefits. This study assesses the performance of potential optimisation methods for future proofing MRFs using modular simulation methods. The authors set out to review current robotics sorting technology and pointed out the challenge of efficiency analysis with multiple variables. The study develops a new conceptual model of efficiency analysis considering the identification and sorting limitations of robots, as well as the coordination requirements between robots and conveyor belts. A computational model is designed and developed by modularity program codes to help practitioners gain insight into the MRF performance by modifying the variables (composition of input waste, separation coefficients and configurations) and analysing the resulting assessment factors (purity and recovery). In the end, this study demonstrates the performance of the optimisation methods of MRF (two target materials for one robot and recirculation loops) through simulation.

Identification and localization of transmission lines for live working in distribution network

In this paper, aiming at the current distribution network with power operation in the presence of outdoor bright light, the operating scene is complex and not fixed, the transmission lines and lead samples are small and uneven, leading to poor accuracy of machine learning and other methods of distribution network with power operation robot identification and localization, this paper selects three-dimensional LIDAR to design a set of support vector machine (SVM) classifier and correlation analysis based on the target recognition method. This paper selects three-dimensional LIDAR to design a set of support vector machine (SVM) classifier and correlation analysis based on the target recognition method. First, in the context of power distribution network operations, we have explored various filtering methods suitable for preprocessing transmission line data. These methods aim to remove outliers and clutter from point clouds, thus enhancing both point cloud quality and processing speed. This, in turn, improves the efficiency of real-time on-site detection. We conducted experimental comparisons of various clustering algorithms and opted for a region-based point cloud clustering algorithm to achieve the segmentation of individual point clouds. Secondly, we proposed a multi-feature composite approach based on the Viewpoint Feature Histogram (VFH) features, which helps maintain the scale-invariant characteristics of the features. We then introduced a multi-feature composite criterion based on VFH features to extract features from segmented point clouds. Subsequently, an SVM classifier based on this composite feature criterion was developed to achieve target identification and classification. Experimental results have demonstrated improved accuracy in target identification. Finally, we integrated this system with a bucket-arm vehicle for coordinate conversion and precise navigation of the robot to the target station. This method significantly improves the overall operational efficiency of power distribution tasks.

Curriculum-based humanoid robot identification using large-scale human motion database

Identifying an accurate dynamics model remains challenging for humanoid robots. The difficulty is mainly due to the following two points. First, a good initial model is required to evaluate the feasibility of motions for data acquisition. Second, a highly nonlinear optimization problem needs to be solved to design movements to acquire the identification data. To cope with the first point, in this paper, we propose a curriculum of identification to gradually learn an accurate dynamics model from an unreliable initial model. For the second point, we propose using a large-scale human motion database to efficiently design the humanoid movements for the parameter identification. The contribution of our study is developing a humanoid identification method that does not require the good initial model and does not need to solve the highly nonlinear optimization problem. We showed that our curriculum-based approach was able to more efficiently identify humanoid model parameters than a method that just randomly picked reference motions for identification. We evaluated our proposed method in a simulation experiment and demonstrated that our curriculum was led to obtain a wide variety of motion data for efficient parameter estimation. Consequently, our approach successfully identified an accurate model of an 18-DoF, simulated upper-body humanoid robot.

Ultrasound Integrated Robotic Identification of Sigmoid Colon Deep Endometriosis in the Absence of Posterior Cul De Sac Obliteration

Ultrasound Integrated Robotic Identification of Sigmoid Colon Deep Endometriosis in the Absence of Posterior Cul De Sac Obliteration

Uncertainty Inverse Analysis of Positioning Accuracy for Error Sources Identification of Industrial Robots

Uncertainties widely exist in modeling parameters of industrial robots due to wear during service. These uncertainties are too small to be directly measured and may severely affect the positioning accuracy of end-effector through multi-stage propagation. An uncertainty inverse analysis method of positioning accuracy is proposed to identify uncertainty of typical structural parameters with the selected optimal position information. The Denavit–Hartenberg method is employed to establish the kinematic model. For the probability uncertainty of modeling parameters, the inverse analysis model, which reflects propagation mapping between uncertain variables and end-effector position response is obtained by first-order second-moment method. The partial-derivative information is used to improve calculation efficiency through removing low-contributing parameters. To further reduce measurement noise interference, orthogonal matching pursuit strategy is presented to search optimal sensor placement with the objectives of maximum coefficient modulus and minimum correlation, then pseudoinverse matrix method is employed to obtain the exact solution of identification equation. Finally, numerical simulation results for 6 degrees-of-freedom robots indicate that the proposed method with improved solution efficiency can accurately identify uncertainties of joint angles within the error 5% under large measurement errors.

Research on visual recognition and positioning of industrial robots based on big data technology

Abstract This paper proposes a fast recognition and positioning algorithm based on deep learning for the problems of slow recognition of complex workpieces, low accuracy, and inaccurate positioning of industrial robots. The image grayscale and parameter calibration are performed by building an industrial high-precision vision system on the target image information. The target image is then localized and segmented by boundary pixel detection, and the trained improved SSD algorithm is used to identify the target, obtain the coordinates of the target location and the category to which it belongs, and realize the industrial robot sorting. The results show that the target recognition algorithm based on the improved SSD algorithm has an error of less than 0.5 mm, the fastest recognition speed of 0.045 sec/each, and the recognition accuracy can be maintained above 98% in the experimental environment, and the distance error between the real point and the calculated point is 8.09 mm on average, indicating that the algorithm has good accuracy and stability. Building a prototype system based on the improved SSD algorithm for industrial robots with complex processes is expected to provide an automated robot identification and positioning solution for production lines.

A procedure for the stiffness identification of parallel robots under measurement limitations

This paper introduces a procedure to obtain reliable stiffness model for parallel robots from experimental data and identify its parameters considering measurement limitations. The efficiency of the proposed identification procedure validated via simulation and experimental studies on a 3-DOF Delta parallel robot. Simulation results showed that the proposed simplification and model reduction keeps more than 95% of entire stiffness properties (for the worst-case analysis). The experimental results proved that the obtained model on average describes 95% of compliance errors and for the worst case the error does not overcome 9.8%.

Learning Koopman Embedding Subspaces for System Identification and Optimal Control of a Wrist Rehabilitation Robot

Rehabilitation robots have proven their usefulness in assisting with physical therapy. This paper presents a trajectory tracking controller for a wrist rehabilitation robot with three degrees of freedom. The non-linearity of the human-robot interaction dynamics has been defined as the Koopman linear system in terms of nonlinear observable functions of the state variables. Koopman operators are learned using linear regression to encode the states into object-centric embedding space for a linear approximation of a nonlinear dynamical system. The learned Koopman operators ascertain the system dynamics applied to design the wrist robot's trajectory tracking task controller. This is a data-driven approach that yields an explicit control-oriented model. The efficiency and feasibility of the controller were evaluated through experiments with three healthy human subjects. The experiments demonstrated the ability of the controller to guide the subject's wrist along the reference trajectory.

Positioning failure error identification of industrial robots based on particle swarm optimization and Kriging surrogate modeling

AbstractTo tackle the low identification accuracy of robot positioning, this paper proposes a positioning failure error identification method for industrial robots, combining the Kriging surrogate model and the improved particle swarm optimization algorithm. First, based on the Denavit‐Hartenberg and the small displacement screw methods, the kinematic model of a six‐degree‐of‐freedom industrial robot with joint errors is constructed. Then, the Kriging surrogate model of the kinematic error is constructed, which is trained by generated training samples. Finally, the joint error identification of industrial robots is solved by an improved particle swarm optimization algorithm based on exponential inertia weight and sub‐population cooperation under the multi‐attitude positioning condition. The simulation results show that the proposed optimization algorithm can significantly improve the convergence accuracy and accurately identify the actual errors at each joint of the industrial robot.

Dynamic Parameter Identification of Collaborative Robot Based on WLS-RWPSO Algorithm

Parameter identification of the dynamic model of collaborative robots is the basis of the development of collaborative robot motion state control, path tracking, state monitoring, fault diagnosis, and fault tolerance systems, and is one of the core contents of collaborative robot research. Aiming at the identification of dynamic parameters of the collaborative robot, this paper proposes an identification algorithm based on weighted least squares and random weighted particle swarm optimization (WLS-RWPSO). Firstly, the dynamics mathematical model of the robot is established using the Lagrangian method, the dynamic parameters of the robot to be identified are determined, and the linear form of the dynamics model of the robot is derived taking into account the joint friction characteristics. Secondly, the weighted least squares method is used to obtain the initial solution of the parameters to be identified. Based on the traditional particle swarm optimization algorithm, a random weight particle swarm optimization algorithm is proposed for the local optimal problem to identify the dynamic parameters of the robot. Thirdly, the fifth-order Fourier series is designed as the excitation trajectory, and the original data collected by the sensor are denoised and smoothed by the Kalman filter algorithm. Finally, the experimental verification on a six-degree-of-freedom collaborative robot proves that the predicted torque obtained by the identification algorithm in this paper has a high degree of matching with the measured torque, and the established model can reflect the dynamic characteristics of the robot, effectively improving the identification accuracy.