Robot Joint Research Articles

Sensor fault estimation based on L∞ unknown input observer

Abstract This study investigates the fault estimation problem for a class of Lipschitz non-linear systems with sensor faults and disturbances. An augmented system consisting of system states and sensor faults is constructed to estimate sensor faults by designing an augmented state. A novel L∞ performance index is presented to reduce the influence of disturbances on fault estimation results. An unknown input observer (UIO) is proposed based on the L∞ performance index to achieve an asymptotic estimation of sensor faults. The design conditions of the L∞ UIO are deduced and solved using linear matrix inequalities. The effectiveness of the proposed L∞ UIO and the fault estimation performance are demonstrated via numerical simulations of a single-link flexible joint robot.

Vibration characteristics of industrial robot joint servo transmission system based on electromechanical coupling

The complex electromechanical coupling effect of an industrial robot’s electromechanical system significantly influences the dynamic characteristics and stability of the joint servo transmission system. This study proposes a kind of electromechanical coupling dynamic model for the industrial robot’s joint servo transmission system, incorporating multiple parameters, and investigates the vibration response characteristics of the system. Firstly, an electromechanical coupling analysis of the industrial robot’s joint servo transmission system is conducted, simplifying it as a ‘motor-reducer-load’ system and establishing the corresponding electromechanical coupling vibration mathematical model. Secondly, a dynamic model of the robot’s joint servo transmission system is developed to analyze its speed characteristics. Finally, the model is used to analyze the electromechanical coupling vibration process of the joint servo transmission system with the introduction of mechanical and electrical parameters and other factors to obtain the vibration characteristics of the industrial robot joint servo transmission system under electromechanical coupling. The experimental results show that this model can better analyze the electromechanical coupling vibration characteristics of the joint servo transmission system, which is significant for improving the performance and fault traceability of the industrial robot joint transmission.

Application of Impedance Control of the Free Floating Space Manipulator for Removal of Space Debris

A broad and significant class of space debris can be mitigated by means of a satellite, capable of capturing a large non-cooperating object by using a robotized arm with a gripper. The capture operation typically comprises of an approach, a close-on manoeuvre, establishing contact between the robotic grappler arm and a suitable feature on the target satellite, and finally it is concluded when a positive mechanical connection is achieved by the gripper closed on that feature. The phase of establishing contact poses a critical challenge in this scenario, since the target typically will be tumbling with respect to the chaser satellite causing high forces on the gripper and the robotic arm. A family of control methods known collectively as impedance control is typically employed in terrestrial robots for tasks involving an interaction with an environment, especially the dynamic contact. In this work, we present the model-based impedance control applied to a robotic manipulator on a free floating base. The derivation of impedance control law for a robotic manipulator on a free floating satellite, involving Generalized Jacobian Matrix (GJM), is presented, followed by simulation results comparing the loads in the manipulator joints against a classical GJM-based Cartesian controller. The simulation results show that the impedance controlled free floating robotic manipulator completes the task of trajectory following amid contact with unknown target with lower torques in the robot joints.

Design of Adaptive Sensor Coupling-Based Upper Limb 7-DOF Exoskeleton for Smooth Human Motion Tracking: ASC-EXO

The conventional sensor coupling of the sensor in an exoskeleton robot suffers from inaccurate human motion tracking due to irregular inertial loading on the sensor axis. It may cause sensor axis misalignment, performance degradation and damage the sensor permanently, especially in the multiple degrees of freedom (DOF) exoskeleton robot. In this paper, we proposed a novel design of an adaptive sensor coupling-based 7-DOF exoskeleton (ASC-EXO) for motion tracking and tele-manipulation. The adaptive sensor coupling mechanism has been designed based on the flexure structure cantilever with a slew ring, which suppresses irregular inertial loading on the sensor and is stiff enough along the sensor rotation in the z-axis and compliant on the other axes. The slew ring allows smooth joint rotation. Further, the sensor values are improved by a Butterworth filter. As a result, the adaptive sensor coupling suppressed the sensor noise 20 times more than the conventionally coupled sensor with the robot joint. Finally, we developed a prototype of the 7-DOF exoskeleton (ASC-EXO) using adaptive sensor coupling for smooth human motion tracking. The performance evaluation of adaptive sensor coupling and human motion tracking is presented. In addition, we demonstrated smooth control of the slave robot (KUKA) using the exoskeleton (ASC-EXO) as a master device.

Configuration of drive mechanisms with fewer wires in a surgical robot considering operation trajectory and delay

ABSTRACT Surgical robots have contributed greatly to the development of minimally invasive surgery. Surgical robots mostly use a wire cable drive mechanism because wire has high responsiveness, however, the wires often loosen or break, resulting in high maintenance costs. In contrast, flexible-shaft, timing-belt, and pneumatic-actuator mechanisms have low maintenance costs but lengthen the mechanical delay. In this study, we derived an appropriate configurations of drive mechanisms with fewer wires for each joint of a surgical robot, considering the delay of the mechanisms. We assumed the configuration of the four drive mechanisms at six joints of the surgical robot. Using a virtual surgical simulator, we measured the operation trajectory of a robot in virtual space without delay. From this operation trajectory, the trajectory of the robot’s tip was calculated by simulating the delay for 4096 configurations of the drive mechanisms. We compared the suturing accuracy of the robot with and without delay for all combinations and determined the four configurations whose accuracy was within requirements. Finally, we conducted a verification experiment to compare theoretical and experimental values. The small relative errors validate the feasibility of the method proposed for designing the configuration of drive mechanisms in a surgical robot.

Sequential calibration of transmission ratios for joints of 6-DOF serial industrial robots based on laser tracker

PurposeIn 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.Design/methodology/approachThe 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.FindingsIn 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.Originality/valueThe 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.

Strain Gauge Neural Network-Based Estimation as an Alternative for Force and Torque Sensor Measurements in Robot Manipulators

When a robotic manipulator interacts with its environment, the end-effector forces need to be measured to assess if a task has been completed successfully and for safety reasons. Traditionally, these forces are either measured directly by a 6-dimensional (6D) force–torque sensor (mounted on a robot’s wrist) or by estimation methods based on observers, which require knowledge of the robot’s exact model. Contrary to this, the proposed approach is based on using an array of low-cost 1-dimensional (1D) strain gauge sensors mounted beneath the robot’s base in conjunction with time series neural networks, to estimate both the end-effector 3-dimensional (3D) interaction forces as well as robot joint torques. The method does not require knowledge of robot dynamics. For comparison reasons, the same approach was used but with 6D force sensor measurements mounted beneath the robot’s base. The trained networks showed reasonably good performance, using the long-short term memory (LSTM) architecture, with a root mean squared error (RMSE) of 1.945 N (vs. 2.004 N; 6D force–torque sensor-based) for end-effector force estimation and 3.006 Nm (vs. 3.043 Nm; 6D force–torque sensor-based) for robot joint torque estimation. The obtained results for an array of 1D strain gauges were comparable with those obtained with a robot’s built-in sensor, demonstrating the validity of the proposed approach.

Automatic vibration control method for grasping end of flexible joint robot

Because flexible robots have flexible components such as reducers, there are problems of accuracy deviation and end vibration in the process of external interference and trajectory tracking. This leads to the proposal of a Sliding Mode Control Approach Based on RBF Neural Network (SMC-RBF) parameter optimization. This method is mainly applied to reduce the end vibration and running position error of flexible robot. Firstly, the Newton-Euler method is used to establish the dynamic model of robot considering joint flexibility. At the same time, the experiment optimizes the Sliding Mode Control (SMC) method through RBF neural network. The experiments verify the control methods of the two-joint flexible robot and the six-joint flexible robot respectively. In the control of two-joint robot, the maximum tracking curve error of SMC is only about 0.25 rad under the interference of pulse signal; And the recovery time is only about 1 s. In the control of 6-joint robot, the maximum error of RBF-sliding mode control method on XYZ axis is 0.7 mm, 0.25 mm and 1.25 mm respectively; The error on three axes is smaller than that of traditional PD control method. The results demonstrate that the tracking error of the improved mode control is small, the chattering phenomenon of the robot system is weakened as well.

A graph neural network-based data cleaning method to prevent intelligent fault diagnosis from data contamination

The success of deep learning (DL) based-mechanical fault diagnosis hinges on the high quality of training data. However, it is difficult to acquire high-quality mechanical monitoring data due to data contamination: 1) Monitoring device irregularities, such as sensor malfunction and signal transmission disruption, bring anomalies into the training data; 2) human labour-based data annotation inevitably produces incorrectly labeled data. These two types of data contamination degrade the performance of DL models. To address the aforementioned issue, this paper proposes a graph neural network-based data-cleaning method. In the first stage, a group anomaly detector is designed to identify the presence of anomalous data. This detector incorporates affinity graphs for depicting data groups and subsequently calculates the group anomaly score to determine the abnormal group. In the second stage, a graph clustering model is developed to relabel the mislabeled data. This model takes advantage of the graph neural network's proficiency in handling affinity graphs to prepare clean labels for subsequent network training. Experimental results, conducted on a pump and an industrial robot joint reducer, show the proposed method's ability to effectively detect anomalous data and rectify incorrect labeling, surpassing the performance of baseline methods in mechanical fault diagnosis.

Remaining Useful Life Estimation for High Speed Industrial Robots Using an Unknown Input Observer for Feature Extraction

In industrial robots, a performance issue is backlash, which is the clearance between mating gears of its joints. Over time, backlash grows through wear and tear, causing inaccuracies in robot positioning. Current methods in backlash detection are performed in low-speed and laboratory settings, or require offline diagnostics. These methods are impractical in actual manufacturing environments, where industrial robots operate continuously at high speeds. Other methods require additional sensors unavailable in typical industrial robots. In this article, we present an online method to quantify backlash and predict the remaining useful life (RUL) in an industrial robot performing cyclic production tasks, using only standard available sensors. To achieve the robot's target position, the input torque oscillates; these oscillations grow as the backlash becomes more severe. We modeled the oscillations as an unknown input, and used an unknown input observer to estimate them and detect/quantify the backlash. Then, a health indicator (HI) is plotted over time and a failure threshold is set based on historical data. Finally, an exponential degradation model is used to predict the RUL of the robot joint. The UIO successfully detected and quantified the backlash through the HI. The degradation model gave a good estimate of the RUL with an accuracy of 20 days after 250 days of operation.

Selection/control concurrent optimization of BLDC motors for industrial robots.

This paper aims to concurrently select and control off-the-shelf BLDC motors of industrial robots by using a synergistic model-based approach. The BLDC motors are considered with trapezoidal back-emf, where the three-phase (a,b,c) dynamics of motors are modeled in a mechatronic powertrain model of the robot for the selection and control problem, defining it as a multi-objective dynamic optimization problem with static and dynamic constraints. Since the mechanical and electrical actuators' parameters modify the robot's performance, the selection process considers the actuators' parameters, their control input, operational limits, and the mechanical output to the transmission of the robot joints. Then, three objective functions are to be minimized, the motor's energy consumption, the tracking error, and the total weight of installed motors on the robot mechanism. The control parameterization approach via a cascade controller with PI controllers for actuators' voltage and a PID controller for actuators' torque is used to solve the multi-objective dynamic optimization problem. Based on simulations of the closed-loop system, a Pareto front is obtained to examine trade-offs among the objective functions before implementing any actuators in the existing robotic system. The proposed method is tested on an experimental platform to verify its effectiveness. The performance of an industrial robot with the actuators originally installed is compared with the results obtained by the synergic approach. The results of this comparison show that 10.85% of electrical power can be saved, and the trajectory tracking error improved up to 57.41% using the proposed methodology.

Development of a Compliant Lower-Limb Rehabilitation Robot Using Underactuated Mechanism

Most existing lower-limb rehabilitation robots (LLRR) for stroke and postoperative rehabilitation are bulky and prone to misalignments between robot and human joints. These drawbacks hamper LLRR application, leading to poor arthro-kinematic compatibility. To address these challenges, this paper proposes a novel robot with portability and compliance features. The developed robot consists of an underactuated mechanism and a crus linkage, respectively corresponding to the hip and knee joints. The underactuated mechanism is a new type of remote center of motion (RCM) mechanism with two sets of contractible slider cranks that can reduce the misalignments between robot and human joints. The underactuated mechanism is then optimized using the particle swarm optimization method, and the developed robot’s kinematic analysis is presented. The proposed robot can be simplified as a two-link mechanism with the ability to easily plan its trajectory using the modified Denavit–Hartenberg method. Finally, passive exercise trials demonstrate that the mismatch angles between the human and robot knee joints are less than 2.1% of the range of motion, confirming the feasibility and effectiveness of the proposed robot.

Optimized Sliding Mode Control Based on Cuckoo Search Algorithm: Application for 2DF Robot Manipulator

Robot manipulators are used in high-accuracy processes they provide flexibility, adaptability, and a large workspace. Hence, in the presence of uncertainties, disturbance, and high-performance control approaches are necessary to improve their accuracy to achieve high-tracking precision requirements and transient response under load variation. We present in this article, a novel concept of robust nonsingular fast terminal sliding mode controller (NFTSMC). In fact, the use of the NFTSMC offers a fast convergence rate, avoids singularities but still suffers from chattering. To overcome this limitation, two approaches have been developed: robust nonsingular fast terminal sliding mode controller (RNFTSMC) and robust optimized nonsingular fast terminal sliding controller (RONFTSMC). In the first one an additional term called switching signal is added to the equivalent control law. The second one is a combination of the NFTSM technique, and a cuckoo search algorithm as optimum term. Simulations show that the control strategy can overcome the chattering phenomena ensures the accuracy and stability of the joint robot position control.

INCIDENTAL FINDINGS AND CLINICAL SIGNIFICANCE OF PREOPERATIVE CT IMAGING FOR THE PLANNING OF ROBOTIC ARTHROPLASTY

AbstractIntroductionTotal joint arthroplasty (TJA) is one of the commonest and most successful orthopaedic procedures, used for the management of end-stage arthritis. With the recent introduction of robotic assisted joint replacement, Computed Tomography (CT) has become part of required pre-operative planning.The aim of this study is to quantify and characterise incidental CT findings, their clinical significance, and their effect on planned joint arthroplasty.MethodologyAll consecutive patients undergoing an elective TJR (hip or knee arthroplasty) were retrospectively identified, over a 3-year period (December 2019 and December 2022). Data documented and analysed included patient demographics, type of joint arthroplasty, CT findings, their clinical significance, as well as potential delays to the planned arthroplasty because of these findings and subsequent further investigation.ResultsA total of 624 patients (637 studies, 323 (51.8%) female, 301 (48.2%) male) were identified of which 163 (25.6%) showed incidental findings within the long bones or pelvis. Of these 52 (8.2%) were significant, potentially requiring further management, 32 (5.0%) represented potential malignancy and 4 (0.6%) resulted in a new cancer diagnosis.ConclusionIt is not currently national standard practice to report planning CT imaging as it is deemed an unnecessary expense and burden on radiology services. Within the study cohort 52 (8.2%) of patients had a significant incidental finding that required further investigation or management and 4 (0.6%) had a previously undiagnosed malignancy. In order to avoid the inevitability of a missed malignancy on a planning CT, we must advocate for formal reports in all cases.

Parametric Design and Prototyping of a Low-Power Planar Biped Robot.

This study proposes a design approach and the development of a low-power planar biped robot named YU-Bibot. The kinematic structure of the robot consists of six independently driven axes, and it weighs approximately 20 kg. Based on biomimetics, the robot dimensions were selected as the average anthropomorphic dimensions of the human lower extremities. The optimization of the mechanical design and actuator selection of the robot was based on the results of parametric simulations. The natural human walking gait was mimicked as a walking pattern in these simulations. As a result of the optimization, a low power-to-weight ratio of 30 W/kg was obtained. The drive system of the robot joints consists of servo-controlled brushless DC motors with reduction gears and additional bevel gears at the knee and ankle joints. The robot features spring-supported knee and ankle joints that counteract the robot's weight and compensate for the backlash present in these joints. The robot is constrained to move only in the sagittal plane by using a lateral support structure. The robot's feet are equipped with low-cost, force-sensitive resistor (FSR)-type sensors for monitoring ground contact and zero-moment point (ZMP) criterion. The experimental results indicate that the proposed robot mechanism can follow the posture commands accurately and demonstrate locomotion at moderate stability. The proposed parametric natural gait simulation-based design approach and the resulting biped robot design with a low power/weight ratio are the main contributions of this study.

Intelligent Servo Control Strategy for Robot Joints With Incremental Bayesian Fuzzy Broad Learning System

Intelligent servo control significantly reduces the need to adjust control parameters, and is, therefore, widely used in robot joint control. However, existing intelligent servo control strategies for robot joints have problems of computational redundancy, limited prediction accuracy, and insufficient generalization capability. To solve these problems, this article proposes a servo control strategy for robot joints that is based on the incremental Bayesian fuzzy broad learning system (IBFBLS). First, we construct an intelligent servo control strategy with broad learning system on the basis of fuzzy rules to achieve good self-learning and generalization abilities. Second, the learning parameters of the control strategy are optimized by Bayesian inference to achieve precise joint servo control. Finally, the convergence of the control strategy is enhanced by combining it with Lyapunov theory to constrain the learning parameters of the proposed control strategy. The feasibility and superiority of the proposed control strategy are verified by simulation to compare it with existing intelligent servo control methods. In addition, experiments are conducted using robot joint test bed. Both the simulation and experiments verify that the proposed servo control strategy outperforms other servo control methods with respect to tracking accuracy, stability, and convergence. The root-mean-square error in servo control of robot joints was 0.012 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , which has been reduced by 55.56 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> compared with the current state of the art.

A Practical Model-Based Robust Control for the Modular Joint of Collaborative Robots

A Practical Model-Based Robust Control for the Modular Joint of Collaborative Robots

Human–Robot Attachment System for Exoskeletons: Design and Performance Analysis

Exoskeleton robots found application in neurorehabilitation, telemanipulation, and power augmentation. The human–robot attachment system of an exoskeleton should transmit all the interaction forces while keeping the anatomical and robotic joint axes aligned. Existing attachment concepts were bounding the performance of modern exoskeletons due to insufficient stiffness for high-performance force control, time-consuming adaption processes, and/or bulkiness. Therefore, we developed an augmented attachment system for a recent fully actuated nine-degree-of-freedom upper limb exoskeleton. The proposed system was compared to a conventional solution in a case study with four participants. The proposed attachment system lowered the relative motion between the human and the robot under static loads for all defined landmarks by 45% on average. The occurrence of undesired contacts in the trials was mitigated by 74%, thus improving conditions for closed-loop force control. Furthermore, the proposed system adapted better to the user's anatomy facilitating more accurate alignment and less obstruction. On average, self-attachment took <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {43(8.3)}$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{s}$</tex-math></inline-formula> to don(doff). Thereby, the alignment of anatomic landmarks had typically less than 15 mm offset to a thorough expert alignment, making self-attachment eligible. The augmented attachment system and the insights gained by the case study are expected to enable improvement of the physical human–robot interaction of exoskeletons.

Robot joint module based on universal joint configuration driven by SMA wires

Modular robots can adapt to different production needs and working conditions. However, the complexity of structures and the choice of driving modes limit the development of modularization. In this paper, a robot joint module with a universal joint configuration driven by shape memory alloy (SMA) wires is proposed, which has 2-DOFs. For lightweight, the main body of the module is made of polylactic acid materials. SMA wires are used as the actuator due to the advantages of large output displacement, high mass-to-energy ratio, and low activation voltage. Adopting antagonistically arranged SMA wires and bias springs combination method increases the recovery speed and driving angles. A locking device is creatively proposed to avoid the influence of self-biasing spring compression. The detailed kinematic model of the module is established. A proportional integral differential controller is used to precisely control module movements, and the attitude angle sensor is used as the monitoring device. Through the maximum rotation angles and trajectory tracking movement experiments, the module is tested comprehensively, and the results show that the module has good movement characteristics and control precision. In addition, analyzing the tracking performance of the module in the time domain shows that the module has a relatively fast tracking speed. This module is beneficial to the research of configurations, actuators, and controls of modular robots.

GAN-Based Editable Movement Primitive From High-Variance Demonstrations

Movement Primitive (MP) is a promising Learning from Demonstration (LfD) framework, which is commonly used to learn movements from human demonstrations and adapt the learned movements to new task scenes. A major goal of MP research is to improve the adaptability of MP to various target positions and obstacles. MPs enable their adaptability by capturing the variability of demonstrations. However, current MPs can only learn from low-variance demonstrations. The low-variance demonstrations include varied target positions but leave various obstacles alone. These MPs can not adapt the learned movements to the task scenes with different obstacles, which limits their adaptability since obstacles are everywhere in daily life. In this paper, we propose a novel transformer and GAN-based Editable Movement Primitive (EditMP), which can learn movements from high-variance demonstrations. These demonstrations include the movements in the task scenes with various target positions and obstacles. After movement learning, EditMP can controllably and interpretably edit the learned movements for new task scenes. Notably, EditMP enables all robot joints rather than the robot end-effector to avoid hitting complex obstacles. The proposed method is evaluated on three tasks and deployed to a real-world robot. We compare EditMP with probabilistic-based MPs and empirically demonstrate the state-of-the-art adaptability of EditMP. Code https://github.com/Xu-Xuanhui/EditMP.