Industrial Robot Manipulator Research Articles

Mathematical model and evaluation of dynamic stability of industrial robot manipulator: Universal robot

Robot is an indispensable technology within the context of the Industrial 4.0 revolution, providing a wide range of applications in industry. The stability of a robot's manipulator is contingent upon its associated manipulator settings, hence influencing its overall quality. The assessment of stability has been examined in prior studies using a limited set of manipulator parameters, leading to a deficient comprehension of this phenomenon. In this study, a mathematical model of a flexible six-link manipulator is formulated using Lagrangian mechanics and various parameters. An innovative mathematical framework is developed to establish a correlation between stability and the motor acceleration and moment of inertia for the Universal Robot (UR5). Furthermore, extensive research has been conducted to examine the correlation between stiffness, damping, and deflection in the context of stability. In spite of this, fuzzy logic inference methods are employed to ascertain the relative significance of stiffness, damping, and deflection with regards to stability. Mathematical approaches are employed to validate the numerical values of several manipulator parameters. The findings of the study demonstrate that there is a positive correlation between motor acceleration and stability, indicating that as motor acceleration grows, stability also increases. Conversely, there is a negative correlation between stability and moment of inertia, suggesting that stability reduces as moment of inertia increases. In the case of stiffness, damping, and deflection, stiffness is the most important factor. When stiffness is high, stability is also high. However, when stiffness is minimal, stability is low. The implications of these findings are expected to have a positive impact on enhancing industrial productivity.

Evolutionary Optimization of Fuzzy Reinforcement Learning and Its Application to Time-Varying Tracking Control of Industrial Parallel Robotic Manipulators

This paper contributes to the development of evolutionary optimization of fuzzy reinforcement learning and its application to time-varying tracking control for industrial parallel robotic manipulators. An improved evolutionary social spider optimization (SSO) paradigm with Cauchy mutation is presented to address the optimal fuzzy reinforcement learning problem. The SSO swarm intelligence incorporated with the fuzzy Q-learning strategy, called SSOFQ, is applied to the time-varying tracking control of parallel robotic Stewart manipulators with six degrees of freedom (DOFs). Having the derived kinematics and dynamics of six-DOF Stewart platforms, the SSOFQ is employed to synthesize an intelligent real-time tracking control scheme. More importantly, a custom experimental Stewart platform is constructed using hardware/software codesign and the system-on-a- programmable chip (SoPC) technique. Finally, the simulations, experimental results and comparative works are provided to validate the efficacy and superiority of the presented SSOFQ control method.

Generalized algorithm for designing the mechanical part of an industrial robot manipulator

The paper presents a method that allows you to design and calculate the design of an industrial robot manipulator using modern computer systems (CAD, CAE). Due to the versatility, the scope of application of this method can be different from the design of turntables to flexible production complexes. But the main focus of the method is the design of an industrial robot manipulator. The paper presents an example of the design and calculation of an in-dustrial robot manipulator, which will later be manufactured in a single copy.

Multirobot collaborative task dynamic scheduling based on multiagent reinforcement learning with heuristic graph convolution considering robot service performance

To address the problem of multirobot collaborative task scheduling considering the degradation of industrial robot performance and the recovery of robot performance through intervention of compensation measures, a robot collaborative task scheduling method based on multiagent reinforcement learning with heuristic graph convolution is proposed in this paper. Five types of constraints between tasks and robots from the temporal and spatial dimensions are designed, and a graph structure with different connection forms is utilized to represent the tasks, robots, and their mutual constraints. The method creates serviceability evaluation (SE) and error compensation (EC) agents to assess the robot's serviceability and compensate for positioning errors. Moreover, it combines heuristic rules and graph convolutional neural networks to create a heuristic graph convolution scheduling (HGCS) agent for task allocation and scheduling. The three agents are trained in a decentralized manner while executing in a centralized manner to achieve dynamic task scheduling that considers the robot's serviceability and error compensation intervention in practical scenarios. Through three case studies involving the evaluation of the robot core drivetrain component degradation status, compensation of industrial robot positioning errors, and multirobot collaborative task scheduling, the proposed method effectively addresses the performance changes in robots during the task allocation and scheduling process. Comparative evaluations with four relevant methods in various task scheduling scenarios demonstrate that the proposed method reduces the average task completion time by 17.99% and improves the resource utilization balance degree by 16.85%.

Collision Avoidance for a Selective Compliance Assembly Robot Arm Manipulator Using Topological Path Planning

Industrial applications with robotic manipulators have grown and made production systems increasingly efficient. However, there are still some limitations that can delay production, causing losses. Several factors, such as accidents and collisions of manipulator robots with operators and other machines, can cause unforeseen stops. Thus, this work aims to develop a trajectory planning method to avoid collisions applied to a selective compliance assembly robot arm (SCARA) robotic manipulator in the context of collaborative robotics. The main contribution of this paper is a path planning method based on mathematical morphology, named topological path planning (TPP). Through some evaluation metrics such as the number of path points, computing time, distance, standard deviation of the joint acceleration, and maximum acceleration rate along the path, we show that TPP is a collision-free, deterministic, and predictable route planning. In our experiments, our proposal presented better results for applications in industrial robotic manipulators when compared to the probabilistic roadmap method (PRM) and TPP*, a particular case of TPP that is similar to the generalized Voronoi diagram (GVD).

A Multi-Fidelity Integration Method for Reliability Analysis of Industrial Robots

Abstract A multi-fidelity integration method is proposed to analyze the reliability of multiple performance indicators (MPI) for industrial robots. In order to high-fidelity mapping the performance of industrial robots, a unified multi-domain model (UMDM) is first established. The contribution-degree analysis is then used to classify the input random variables into interacting and non-interacting ones. Thus, the high-dimensional integration of reliability analysis is separated into a low-dimensional integration and multiple one-dimensional integrations in an additive form. Here, the low-dimensional integration consisting of the interacting variables is calculated using the high-precision mixed-degree cubature formula (MDCF), and the computational results are treated as high-fidelity data. The one-dimensional integration consisting of non-interacting variables is then computed by the highly efficient five-point Gaussian Hermite quadrature (FGHQ), and the computational results are named low-fidelity data. A multi-fidelity integration method is constructed by fusing the high-fidelity data and the low-fidelity data to obtain the statistical moments of the MPI. Subsequently, the probability density function and the failure probability of the MPI are estimated using the saddlepoint approximation method. Finally, some representative methods are performed to verify the superiority of the proposed method.

Context-Aware Robotic Assistive System: Robotic Pointing Gesture-Based Assistance for People with Disabilities in Sheltered Workshops

People with disabilities are severely underrepresented in the open labor market. Yet, pursuing a job has a positive impact in many aspects of life. This paper presents a possible approach to improve inclusion by including a robotic manipulator into context-aware Assistive Systems. This expands the assistance possibilities tremendously by adding gesture-based feedback and aid. The system presented is based on the intelligent control system of behavior trees, which—together with a depth camera, specifically designed policies, and a collaborative industrial robotic manipulator—can assist workers with disabilities in the workplace. A developed assistance node generates personalized action sequences. These include different robotic pointing gestures, from simple waving, to precisely indicating the target position of the workpiece during assembly tasks. This paper describes the design challenges and technical implementation of the first Context-Aware Robotic Assistive System. Moreover, an in-field user study in a Sheltered Workshop was performed to verify the concept and developed algorithms. In the assembly task under consideration, almost three times as many parts could be assembled with the developed system than with the baseline condition. In addition, the reactions and statements of the participants showed that the robot was considered and accepted as a tutor.

High-Precision Control of Industrial Robot Manipulator Based on Extended Flexible Joint Model

High-precision industrial manipulators are essential components in advanced manufacturing. Model-based feedforward is the key to realizing the high-precision control of industrial robot manipulators. However, traditional feedforward control approaches are based on rigid models or flexible joint models which neglect the elasticities out of the rotational directions and degrade the setpoint precision significantly. To eliminate the effects of elasticities in all directions, a high-precision setpoint feedforward control method is proposed based on the output redefinition of the extended flexible joint model (EFJM). First, the flexible industrial robots are modeled by the EFJM to describe the elasticities in joint rotational directions and out of the rotational directions. Second, the nonminimum-phase EFJM is transformed into a minimum-phase system by using output redefinition. Third, the setpoint control task is transformed from Cartesian space into joint space by trajectory planning based on the EFJM. Third, a universal recursive algorithm is designed to compute the feedforward torque based on the EFJM. Moreover, the computational performance is improved. By compensating the pose errors caused by elasticities in all directions, the proposed method can effectively improve the setpoint control precision. The effectiveness of the proposed method is illustrated by simulation and experimental studies. The experimental results show that the proposed method reduces position errors by more than 65% and the orientation errors by more than 62%.

Dynamic performance of industrial robots in the secondary carbon fiber-reinforced plastics machining

Carbon fiber-reinforced plastics (CFRPs) find many applications given their superior properties. These materials are usually formed using a near-net-shape method that requires secondary machining, such as drilling and trimming, after molding. Industrial robots are becoming increasingly popular machining tools in industries exhibiting high demand for CFRPs. However, it remains challenging to achieve high dimensional accuracy when using such robots and dynamic performance is poor. We experimentally investigated the dynamic properties of the tool tip according to the dominant robot posture during CFRP secondary machining. Based on the results, multi-layer perceptron models were developed to predict the dominant natural frequency and dynamic stiffness of the tool tip. The minimum and maximum mean absolute percentage errors were 1.99 and 7.94, respectively; the error changed markedly with robot posture. Our models improved CFRP robotic machinability. Experimentally, the delamination rates of drilled holes decreased by 15 % and 75 % in terms of length and area, respectively, and the trimmed surface roughness improved by 27 %.

Trajectory planning and control algorithm of industrial robot manipulator

As the industrial robot task becomes more complex, the difficulty of trajectory planning and tracking control of manipulator is gradually increasing. To minimize the vibration during the manipulator motion and improve the planning accuracy, the method of quintic polynomial combined with non-uniform B-spline interpolation is studied for joint space (JS) planning. The trajectory tracking system is easily affected by friction nonlinearity and parameters. So a JS trajectory tracking controller based on based on fuzzy neural network (FNN) is designed. Through simulation experiments, the curve obtained by the planning method studied is smoother and the planning error is minimum. The maximum position error is 0.09 rad, and the speed error is not more than 0.1 rad/s. The controller performance test results under different parameters show that the W^, c^, κ^ parameter in FNN can be adjusted in real time, and the value will not affect the performance of the controller. The fluctuation range of trajectory error of different joints is within ±0.2×10-5rad, which indicates that the performance of AFNNC controller studied is better. And its response time is the shortest and its robustness is better when the load changes suddenly.

A robotized 6-DOF dry test rig for wave power

Wave power has the potential to contribute significantly to sustainability by reducing our global dependence on fossil fuels. Due to harsh conditions and high costs associated with offshore testing, lab experiments are favourable for resource-efficient validation and optimization in developing Wave Energy Converter (WEC) technologies. The limited scale and availability of existing wave tanks, and the limited flexibility of existing dry test rigs does however put significant restraints on such experiments. In this paper we introduce an alternative novel robotized dry test rig concept for wave power, evaluate its performance and discuss its potential. A full-scale robotized dry test rig demonstrator is constructed and used for experiments with a WEC prototype device. High motion flexibility and accuracy is thereby validated, also for repeating recorded wave and buoy motions. Compared to other dry test rigs, no special components were used and the motion trajectories were defined in full 6-Degrees-Of-Freedom. Two real-time hydrodynamic motion response methods are also demonstrated in the test rig, enabling emulation of actual offshore operation as well as development of advanced WEC control strategies. With a larger industrial robot manipulator, the introduced test rig concept could achieve realistic scaled force and power experiments with most point absorber WECs.

A Hybrid Inverse Kinematic Solution for Trajectory Tracking of Industrial Robot Manipulator

A Hybrid Inverse Kinematic Solution for Trajectory Tracking of Industrial Robot Manipulator

A collision-free path planning method for industrial robot manipulators considering safe human–robot interaction

A collision-free path planning method for industrial robot manipulators considering safe human–robot interaction

Assembly task execution using visual 3D surface reconstruction: An integrated approach to parts mating

This paper is focused on assembly tasks executed by an industrial robotic manipulator in the presence of uncertainties. The goal is to achieve higher levels of autonomy and flexibility of robotic systems in the execution of such tasks. In particular, as a well-established paradigm of assembly tasks, a Peg-in-Hole task has been considered, where the pose of the target object with respect to the robot is known with uncertainties far larger than the task tolerance, e.g., due to manual positioning of the object in the workcell. The proposed approach is based on the reconstruction of the object surface by means of a number of point clouds provided by a depth sensor. The reconstruction is then compared with a known CAD model of the surface, in order to localize the position and tilt of the holes. Finally, the peg insertion is performed in two steps: a search phase, in which the peg tip gently slides on the surface following a trajectory described by Lissajous functions, and a mechanical coupling phase, in which a compliant behavior is imposed to the peg. Experiments on a collaborative manipulator confirm that the proposed approach allows to achieve a better degree of autonomy and flexibility for a class of robotic tasks in partially structured environments.

Adaptive Manipulability-Based Path Planning Strategy for Industrial Robot Manipulators

In this article, a novel manipulability-based optimal rapidly exploring random tree (RRT*) path planning strategy is proposed for industrial robot manipulators. When sampling in the search space, two constraints, namely, path length and manipulability measure, are imposed to find a minimal-cost path connecting the start and goal points. By tracking the generated path, a robot manipulator's end-effector can traverse the workspace with a shorter length and, meanwhile, avoid configuration singularities. A constrained closed-loop inverse kinematics technique is utilized to exploit the kinematic redundancy to assign a higher manipulability to an end-effector position. Additionally, the metrics of path length and manipulability measure are used to determine the adaptive step size for the RRT* planner. This helps the space-filling tree to grow efficiently toward unsearched areas and find an optimal path. Simulation analysis and experimental results of a six-degree-of-freedom FANUC-M-20iA industrial robot illustrate the efficiency of the proposed path planning methods.

Pose-Dependent Cutting Force Identification for Robotic Milling

AbstractCutting force identification is critical to improving industrial robot performance and reducing machining vibration. However, most indirect identification methods of cutting force are not applicable since the modal parameters of the robotic milling system vary with the robot pose. This paper presents a novel pose-dependent method to identify the cutting force using the acceleration signal generated by robotic milling. First, the modal parameters at different machining points are employed as a training dataset to develop the Gaussian Process Regression (GPR) model. Next, the modal parameters predicted by the GPR model are employed to optimize the cutting force estimation based on the minimum variance unbiased estimate method. Then, the Kalman filter method is employed to update the covariance matrix of the cutting force identification error and the state estimation error. Lastly, the effectiveness of the proposed method is verified with robotic milling experiments, and the results show that the identification error and time are acceptable under the condition of variable robot pose.

Transforming Industrial Manipulators via Kinesthetic Guidance for Automated Inspection of Complex Geometries.

The increased demand for cost-efficient manufacturing and metrology inspection solutions for complex-shaped components in High-Value Manufacturing (HVM) sectors requires increased production throughput and precision. This drives the integration of automated robotic solutions. However, the current manipulators utilizing traditional programming approaches demand specialized robotic programming knowledge and make it challenging to generate complex paths and adapt easily to unique specifications per component, resulting in an inflexible and cumbersome teaching process. Therefore, this body of work proposes a novel software system to realize kinesthetic guidance for path planning in real-time intervals at 250 Hz, utilizing an external off-the-shelf force-torque (FT) sensor. The proposed work is demonstrated on a 500 mm2 near-net-shaped Wire-Arc Additive Manufacturing (WAAM) complex component with embedded defects by teaching the inspection path for defect detection with a standard industrial robotic manipulator in a collaborative fashion and adaptively generating the kinematics resulting in the uniform coupling of ultrasound inspection. The utilized method proves superior in performance and speed, accelerating the programming time using online and offline approaches by an estimate of 88% to 98%. The proposed work is a unique development, retrofitting current industrial manipulators into collaborative entities, securing human job resources, and achieving flexible production.

Design and Evaluation of an Intuitive Haptic Teleoperation Control System for 6-DoF Industrial Manipulators

Industrial robots are capable of performing automated tasks repeatedly, reliably and accurately. However, in some scenarios, human-in-the-loop control is required. In this case, having an intuitive system for moving the robot within the working environment is crucial. Additionally, the operator should be aided by sensory feedback to obtain a user-friendly robot control system. Haptic feedback is one way of achieving such a system. This paper designs and assesses an intuitive teleoperation system for controlling an industrial 6-DoF robotic manipulator using a Geomagic Touch haptic interface. The system utilises both virtual environment-induced and physical sensor-induced haptic feedback to provide the user with both a higher amount of environmental awareness and additional safety while manoeuvering the robot within its working area. Different tests show that the system is capable of fully stopping the manipulator without colliding with the environment, and preventing it from entering singularity states with Cartesian end effector velocities of up to 0.25 m/s. Additionally, an operator is capable of executing low-tolerance end effector positioning tasks (∼0.5 mm) with high-frequency control of the robot (∼100 Hz). Fourteen inexperienced volunteers were asked to perform a typical object removal and writing task to gauge the intuitiveness of the system. It was found that when repeating the same test for a second time, the participants performed 22.2% faster on average. The results for the second attempt also became significantly more consistent between participants, as the inter quartile range dropped by 82.7% (from 52 s on the first attempt to 9 s on the second).

A Closed-Loop Evaluation Method for Industrial Robot Performance Driven by Health Data

Evaluating robot performance through health data is important for industrial robots in production. In this article, a closed-loop evaluation method based on low-cost health data is proposed to realize the evaluation of robot performance and contribute to robot optimization. Specifically, we first define a closed-loop evaluation structure between a robot system and its performance. Based on this, a multicluster cross-fusion algorithm based on K-means clustering is proposed to extract patterns of the performance based on the direction and magnitude of control errors instead of metrics to describe the robot performance. Then, objective and interpretable criterion sets are designed based on the strategies for robot system maintenance. The evaluation model based on hybrid game theory is proposed to realize the evaluation of the robot overall performance with the pattern of performance and criterion sets. Meanwhile, a closed-loop evaluation method based on an autoregressive nonlinear neural network and Bayesian model is used to evaluate the robot joint performance by mapping the performance to the robot joint. The effectiveness of the method is verified using an actual DoF robot. The results show that the method is sufficient for the evaluation of robot performance and optimization guidance.

Two-stage multi-sensor fusion positioning system with seamless switching for cooperative mobile robot and manipulator system

The stoppage of a mobile platform is generally scheduled to feed parts for machines on production lines, such as fenced industrial robotic manipulators. A non-stop mobile robotic part feeding system can contribute to production efficiency and flexibility but contains several challenging tasks. For example, the industrial robotic manipulator needs to perceive the positions of the mobile robot accurately and robustly before grasping the supplies when the mobile robot moves around. Thus, based on the relative distance between the two robots, an interaction mode of the integrated robotic system consisting of a fixed robotic manipulator and a mobile robot is developed for robotic interaction. In order to accurately and robustly perceive the positions of a mobile robot, two different positioning approaches for the robotic manipulator positioning mobile robot in an indoor environment are utilised. One approach is ultrasonic sensors fused with inertia measurement units (IMU) by extended Kalman filter (EKF). Furthermore, an outlier rejection mechanism is implemented to escape outliers from ultrasonic measurement. Another positioning approach is achieved by detecting an ArUco marker with visual sensor. Lastly, a positioning switching strategy according to the visual sensor state allows the robotic manipulator to reposition the mobile robot seamlessly. According to the static experiments, EKF-based positioning approach fusing IMU with ultrasonic sensor can export high-accuracy (the root mean square error is 0.04 m) and high-precision (the standard deviation is 0.0033 m) in positioning while keeping a high update frequency of 181.9 HZ in static positioning. Evaluations through dynamic experiments demonstrate that the proposed positioning system can suppress the positioning drifts over time in comparison with wheel encoder-based positioning method. The two-stage repositioning strategy can support the robotic manipulator to identify the positions of the mobile robot robustly, even in the case when the visual sensor is occluded.