Robot Identification Research Articles

Dynamic Parameters Identification of an Industrial Robot With and Without Payload

This paper brings an identified model for a 6 degrees of freedom (dof) industrial robot, the Denso VP-6242G robot, first without payload, then with a payload. This last is composed of a force sensor fixed between a spherical handle and the robot end-effector. This equipped end-effector is intended to experiments in the field of Physical Human-Robot Interactions (PHRI), for co-manipulation purposes. The control algorithms that are necessary to achieve a good PHRI, require a good knowledge of the robot dynamical model, especially the inertia matrix which should be positive definite whatever the configuration of the robot. However, most of industrial robots are supplied without any datasheet containing the inertial parameters nor Computer-Aided-Design (CAD) model. Hence, we propose to apply an identification procedure to experimental data, based on the Inverse Dynamic model Identification Method (IDIM). To ensure the positive definiteness of the inertia matrix, the used optimization step addresses the problem of nonlinear Weighted Least Squares (WLS), derived from the mathematical formulation of the identification problem, under a set of nonlinear constraints in the parameters.

Review of robotic manipulators and identification of the main problems

One of the main elements of automation of industrial enterprises is the use of robotic systems consisting of mechanical manipulators and control systems. In recent years, the market of service robotics has been actively developing. The main part of the market of professional service robots in value terms is occupied by medical devices. Agriculture and logistics are also actively developing areas. The success of the automation systems implementation depends on the solution of complex scientific and technical problems, primarily in the following areas: machine vision; sensor networks; navigation systems. Thus, one of the fundamental problems, the solution of which largely depends on the success in creating the perfect adaptive and intelligent robots, is the use of such types of sensors of sensory information, which allow obtaining a sufficiently large amount of information about the problem environment in a short time. This is a problem of creating means of perception.

Study on Parameter Identification of Assembly Robot based on Screw Theory

The kinematic model of assembly robot is one of the most important factors affecting repetitive precision. In order to improve the accuracy of model positioning, this paper first establishes the exponential product model of ER16-1600 assembly robot on the basis of screw theory, and then based on iterative least squares method, using ER16-1600 model robot parameter identification. By comparing the experiment before and after the calibration, it is proved that the method has obvious improvement on the positioning accuracy of the assembly robot.

Kinematical and dynamical modeling of a multipurpose upper limbs rehabilitation robot

Abstract Knowing accurate model of a system is always beneficial to design a robust and safe control while allowing reduction of sensors-related cost as the system outputs are predictable using the model. In this context, this paper addresses the kinematical and dynamical model identification of the multipurpose rehabilitation robot, Universal Haptic Pantograph (UHP), and present experimental validations of the identified models. The UHP is a Pantograph based innovative robot actuated by two SEAs (Series Elastic Actuator), aiming at training impaired upper limbs after a stroke. This novel robot, thanks to its lockable/unlockable joints, can change its mechanical structure so that it enables stroke patient to perform different training exercises of the shoulder, elbow and wrist. This work focuses on the ARM mode, which is a training mode used to rehabilitate elbow and shoulder. The kinematical model of UHP is identified based on the loop vector equations, while the dynamical model is derived based on the Lagrangian formulation. To demonstrate the accuracy of the models, several experimental tests were performed. The results reveal that the mean position error between estimated values with the model and actual measured values stays in 3 mm (less than 2% of the maximum motion range). Moreover, the error between estimated and measured interaction force is smaller than 10% of maximum force range. So, the developed models can be adopted to estimate motion and force of UHP as well as control it without the need of additional sensors such as a force sensor, resulting in the reduction of total robot cost.

Closed-form control oriented model of highly flexible manipulators

• A closed-form dynamic model of highly flexible 3D manipulators with links of arbitrary shape is proposed. • In order to account for large deformations a substructuring method is formulated. • The data describing the links flexibility are obtained as result of a FE preprocessing stage. • The model is validated by comparison with experimental results and multibody software. • An integral manifold model, suitable for the design of advanced control systems is finally derived. This paper first presents a highly flexible 3D manipulator with links of arbitrary shape, then develops a closed-form dynamic model that best describes it. The model is based on a Newton–Euler formulation and the substructuring method is used to account for large deformations. The formulation of the motion equations starts from a data set which can be either analytically or numerically computed by finite elements(FE) codes. Simulation has been used to validate the model and compare the results with those of two different multibody software and one experimental, which was obtained from the Multi-Elastic-Link Robot Identification Dataset (MERIt), developed by the TU Dortmund. Then, thanks to the approach here adopted, an integral manifold model is derived, suitable for advanced control system design.

Mutual acoustic identification in the swarm of e-puck robots

This article analyses and mathematically describes a method for mutual identification of multiple mobile robots. The method interchanges identification numbers among multiple robots via audible beeps multiplexed by frequency. The identification sound is sent only when robots approach to each other. The algorithm was primary designed for e-puck robots, but it can be used in any multi-agent robotic system; each mobile robot has to be equipped with three microphones for determination of the sound’s direction, speaker and signal processing microcontroller.

Geometric parameter identification of a dual-arm robot by using closed-chain constraint and optimization technique

The positioning accuracy of the empirical robot manipulators is determined by various factors, such as kinematic accuracy, structure rigidity, and controller performance. Here, we report on the development of a new and straightforward technique to calibrate the kinematic parameters of a dual-arm robot under uncertainty. In comparison with other techniques, which generally rely on using other instruments to calibrate the manipulators, the proposed method utilizes the intrinsic characteristics of the dual-arm robot for calibration. In particular, when the two arms grasp each other, a formed closed chain can be operated as the constraint equation for the kinematic parameter optimization of the two arms. In the optimization process, the dual-arm robot has to pose in various configurations to yield better performance, and thus a motion generation strategy of the dual-arm robot is proposed, where one arm serves as the master to track the designated trajectory and the other arm serves as the slave to track the motion of the master arm by using a compliance control strategy. The proposed calibration method was experimentally validated, and the results confirm that the positioning accuracy of both arms can be improved.

Position control of a pneumatic surgical robot using PSO based 2-DOF H∞ loop shaping structured controller

This research proposed novel development of a 2-DOF H∞ loop shaping structured controller based on Particle Swarm Optimization (PSO) that considers the closed-loop dynamic response, robustness, stability, and minimal control input in design criteria to control position of 3-DOF pneumatic surgical robot. Unlike other conventional H∞ controllers, the proposed controller offers robustness, high performance, but cost-effective simple structure, which has recently received attention from several researchers and preferred in industrial applications. The proposed technique is simulated and experimented on a nonlinear system of a pneumatic 3-DOF surgical robot for a Minimally Invasive Surgery (MIS). Mechanical design, dynamics modeling, and system identification of the surgical robot are conducted. The simulation results verify that the proposed controller can gain a better H∞ sub-optimal solution than the conventional 2-DOF H∞ loop shaping controller. Also, the experiments confirm that the proposed controller is capable to tolerate the perturbed conditions and can be alternative to the conventional controllers in pneumatic controlled system

System identification of neural mechanisms from trial-by-trial motor behaviour: modelling of learning, impairment and recovery

System identification is an engineering technique used to build mathematical models of dynamic systems from observed data. In neuroscience research, system identification has been used to adapt robotic devices for measurements and to apply perturbations. These techniques have been shown to be useful tools to investigate brain mechanisms of motor learning processes and motor dysfunctions. This paper reviews the application of system identification to motor behavioural data pertaining to the interruption of specific neural mechanisms. This system identification technique could reveal adaptation and generalisation functions in neural mechanisms for motor tasks to fit experimental data measured by the robotic devices in a discrete state-space model. When comparing constructed models between persons with neurological disabilities and healthy control subjects, system identification could be used to identify impaired abilities. Furthermore, a recent study indicated that such a discrete state-space model may predict recovery of motor function after stroke. Therefore, we suggest that the marriage between robotic devices and system identification will comprise a novel approach to clinical assessment and aid in the design of patient rehabilitation protocols.

Alternative identification of wheeled mobile robots with skidding and slipping

This paper proposes the new identification of dynamics for Type(2,0) wheeled mobile robots(WMRs) in the presence of wheel skidding and slipping. This paper presents dynamics models that explicitly relate perturbations to the vehicle skidding and slipping. They cause parameter uncertainties, input-additive, matched and unmatched perturbations of WMRs. To solve the problem, this paper determines the new identification of dynamics for WMRs with skidding and slipping. The proposed dynamics lay a base for the deployments of various control design techniques to overcome the addressed perturbations. Finally, this paper verifies the tracking performance of the proposed feedback linearization controller for simulation results.

Toward Patients’ Motion Intention Recognition: Dynamics Modeling and Identification of iLeg—An LLRR Under Motion Constraints

In order to implement model-based recognition of human motion intention, dynamics modeling and identification of a lower limb rehabilitation robot named iLeg is investigated. Due to the relatively strong motion constraints, the traditional identification methods become insufficient for iLeg in three aspects: 1) the coupling factors among joints have not been considered in the traditional joint friction models, which makes the structural error and the torque estimation errors relatively large; 2) because of the small and complicated feasible region caused by the motion constraints, the traditional initialization strategy, for searching the valid initial solutions of the optimization problem for the exciting trajectories, becomes very inefficient; and 3) the condition number of the observation matrix, calculated from the preliminary dynamic model and the associated optimized exciting trajectory, is too large for the identification, and, however, further reduction of the condition number has not been considered in the literature. Therefore, corresponding contributions are presented to overcome the limitation. First, the coupling factors among joints are considered in the joint friction model by using the Palmgren empirical formulation and a polynomial fitting method. Then, an indirectly generating strategy is designed, by which the valid initial solutions of the optimization problem can be found with good efficiency. Moreover, a recursive optimization method based on the optimization of the dynamic model and the exciting trajectories, is proposed to further reduce the condition number. Finally, the performance of the proposed methods is demonstrated by several experiments.

Partitioning de Bruijn graphs into fixed-length cycles for robot identification and tracking

We propose a new camera-based method of robot identification, tracking and orientation estimation. The system utilises coloured lights mounted in a circle around each robot to create unique colour sequences that are observed by a camera. The number of robots that can be uniquely identified is limited by the number of colours available, q, the number of lights on each robot, k, and the number of consecutive lights the camera can see, ℓ. For a given set of parameters, we would like to maximise the number of robots that we can use. We model this as a combinatorial problem and show that it is equivalent to finding the maximum number of disjoint k-cycles in the de Bruijn graph dB(q,ℓ).We provide several existence results that give the maximum number of cycles in dB(q,ℓ) in various cases. For example, we give an optimal solution when k=qℓ−1. Another construction yields many cycles in larger de Bruijn graphs using cycles from smaller de Bruijn graphs: if dB(q,ℓ) can be partitioned into k-cycles, then dB(q,tℓ) can be partitioned into tk-cycles for any divisor t of k. The methods used are based on finite field algebra and the combinatorics of words.

A revised Durbin-Wu-Hausman test for industrial robot identification

This paper addresses the topic of robot identification. The usual identification method makes use of the inverse dynamic model (IDM) and the least squares (LS) technique while robot is tracking exciting trajectories. Assuming an appropriate bandpass filtering, good results can be obtained. However, the users are in doubt whether the columns of the observation matrix (the regressors) are uncorrelated (exogenous) or correlated (endogenous) with the error terms. The exogeneity condition is rarely verified in a formal way whereas it is a fundamental condition to obtain unbiased LS estimates. In Econometrics, the Durbin-Wu-Hausman test (DWH-test) is a formal statistic for investigating whether the regressors are exogenous or endogenous. However, the DWH-test cannot be straightforwardly used for robot identification because it is assumed that the set of instruments is valid. In this paper, a Revised DWH-test suitable for robot identification is proposed. The revised DWH-test validates/invalidates the instruments chosen by the user and validates the exogeneity assumption through the calculation of the QR factorization of the augmented observation matrix combined with a F-test if required. The experimental results obtained with a 6 degrees-of-freedom (DOF) industrial robot validate the proposed statistic.

A Separable Prediction Error Method for Robot Identification

The Prediction Error Method, developed in the field of system identification, handles the identification of discrete time noise model for systems linear with respect to the states and the parameters. However, robots are represented by continuous time models, which are not linear with respect to the states. In this article, we consider the issue of robot identification, taking into account the physical parameters as well as the noise model in order to improve the accuracy of the estimates. Thus, we developed a new technique to tackle this problem. The experimental results tend to show a real improvement in the estimation accuracy.

An experimental study on the vision-based control and identification of planar cable-driven parallel robots

This paper presents the implementation of a vision-based position control for planar cable-driven parallel robots. The main contribution of this paper contains three objectives. First, a method is used toward kinematic modeling of the robot using four-bar linkage kinematic concept, which could be used in online control approaches for real-time purposes. In order to ensure positive tension in cables, as a basic essential property, static and dynamic equations of robot are also obtained. Second, in order to track the position of End-Effector, an online image processing procedure is developed and implemented. Finally, as the third contribution, different control approaches are applied in order to validate the model with plant and obtain the most promising controller. As classic controller, pole placement approach is suggested and results reveal weaknesses in modeling the uncertainties. Due to the latter incapability, sliding mode controller is utilized and experimental tests represent effectiveness of this method. However, the chattering phenomenon in the beginning of the robot operation is the main insufficiency of this controller. Hence, in order to present a more accurate controller, adaptive sliding mode controller working alongside with an identification method on the model is applied. The provided identification procedure is based on Recursive Least Square approach, which rebates the effects of uncertainties in the parameters of the model. Moreover, results of these controllers confirm accommodation between the model and robot. The proposed procedure could be well applied for any kind of planar cable-driven parallel robot.

A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor.

Internet of Things (IoT) is driving innovation in an ever-growing set of application domains such as intelligent processing for autonomous robots. For an autonomous robot, one grand challenge is how to sense its surrounding environment effectively. The Simultaneous Localization and Mapping with RGB-D Kinect camera sensor on robot, called RGB-D SLAM, has been developed for this purpose but some technical challenges must be addressed. Firstly, the efficiency of the algorithm cannot satisfy real-time requirements; secondly, the accuracy of the algorithm is unacceptable. In order to address these challenges, this paper proposes a set of novel improvement methods as follows. Firstly, the ORiented Brief (ORB) method is used in feature detection and descriptor extraction. Secondly, a bidirectional Fast Library for Approximate Nearest Neighbors (FLANN) k-Nearest Neighbor (KNN) algorithm is applied to feature match. Then, the improved RANdom SAmple Consensus (RANSAC) estimation method is adopted in the motion transformation. In the meantime, high precision General Iterative Closest Points (GICP) is utilized to register a point cloud in the motion transformation optimization. To improve the accuracy of SLAM, the reduced dynamic covariance scaling (DCS) algorithm is formulated as a global optimization problem under the G2O framework. The effectiveness of the improved algorithm has been verified by testing on standard data and comparing with the ground truth obtained on Freiburg University’s datasets. The Dr Robot X80 equipped with a Kinect camera is also applied in a building corridor to verify the correctness of the improved RGB-D SLAM algorithm. With the above experiments, it can be seen that the proposed algorithm achieves higher processing speed and better accuracy.

Dynamic Parameter Identification of Overactuated Parallel Robots

Offline robot dynamic identification methods are based on the use of the inverse dynamic identification model (IDIM), which calculates the joint forces/torques (estimated as the product of the known control signal (the input reference of the motor current loop) with the joint drive gains) that are linear in relation to the dynamic parameters, and on the use of the linear least squares technique to calculate the parameters (IDIM-LS technique). However, as actuation-redundant parallel robots are overactuated, their IDIM has an infinity of solution for the force/torque prediction, depending on the value of the desired overconstraint that is a priori unknown in the identification process. As a result, the IDIM cannot be used as it is for such a class of parallel robots. This paper proposes a procedure for the dynamic parameter identification of actuation-redundant parallel robots. The procedure takes advantage of two possible modified formulations for the IDIM of actuation-redundant robots that can be used for identification purpose. The modified IDIM formulations project some or all input torques/forces onto the robot bodies, thus leading to a unique solution of the model that can then be used in the identification process. A systematic and straightforward way to compute these modified IDIM is presented. The identification of the inertial parameters of a planar parallel robot with actuation redundancy, the DualV, is then carried out using these modified IDIM. Experimental results show the validity of the methods.

Computationally intelligent strategies for robust fault detection, isolation, and identification of mobile robots

In this paper, new fault detection and isolation/identification (FDI) schemes are proposed by using adaptive threshold bands that are generated with locally linear models (LLM) as well as model error modeling (MEM) techniques. The performance capabilities of our two proposed adaptive threshold bands are compared relative to each other as well as with the performance of a fixed threshold bands. To demonstrate and illustrate the capabilities of our proposed FDI methodology, the developed techniques are applied to a high fidelity model of a two wheeled mobile robot that is subject to the most physically possible faults in these systems. The mobile robot is modeled implicitly by utilizing two computationally intelligent methodologies. Specifically, locally linear models (LLM) as a neuro-fuzzy technique and a radial basis function as a neural network are used to identify and represent the model of the mobile robot. The resulting improvements in the FDI performance by employing our proposed adaptive threshold bands are demonstrated and illustrated through extensive simulation case studies.

Agglomerative Approach for Identification and Elimination of Web Robots from Web Server Logs to Extract Knowledge about Actual Visitors

In this paper we investigate the effectiveness of ensemble-based learners for web robot session identification from web server logs. We also perform multi fold robot session labeling to improve the performance of learner. We conduct a comparative study for various ensemble methods (Bagging, Boosting, and Voting) with simple classifiers in perspective of classification. We also evaluate the effectiveness of these classifiers (both ensemble and simple) on five different data sets of varying session length. Presently the results of web server log analyzers are not very much reliable because the input log files are highly inflated by sessions of automated web traverse software’s, known as web robots. Presence of web robots access traffic entries in web server log repositories imposes a great challenge to extract any actionable and usable knowledge about browsing behavior of actual visitors. So web robots sessions need accurate and fast detection from web server log repositories to extract knowledge about genuine visitors and to produce correct results of log analyzers.

Screw Theory Based Singularity Analysis of Lower-Mobility Parallel Robots considering the Motion/Force Transmissibility and Constrainability

Singularity is an inherent characteristic of parallel robots and is also a typical mathematical problem in engineering application. In general, to identify singularity configuration, the singular solution in mathematics should be derived. This work introduces an alternative approach to the singularity identification of lower-mobility parallel robots considering the motion/force transmissibility and constrainability. The theory of screws is used as the mathematic tool to define the transmission and constraint indices of parallel robots. The singularity is hereby classified into four types concerning both input and output members of a parallel robot, that is, input transmission singularity, output transmission singularity, input constraint singularity, and output constraint singularity. Furthermore, we take several typical parallel robots as examples to illustrate the process of singularity analysis. Particularly, the input and output constraint singularities which are firstly proposed in this work are depicted in detail. The results demonstrate that the method can not only identify all possible singular configurations, but also explain their physical meanings. Therefore, the proposed approach is proved to be comprehensible and effective in solving singularity problems in parallel mechanisms.