Robotic Assembly Research Articles

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).

Assembly of low-stiffness parts through admittance control with adaptive stiffness

Existing studies on robot assembly mostly focus on high-stiffness metal parts, which are unsuitable for assembling low-stiffness parts. Additionally, when assembly is achieved by the conventional admittance control method, the number of parameters to be adjusted is significant, and unstable contact force may occur. This study proposes two algorithms, namely admittance control with variable stiffness (ACVS) and admittance control with adaptive stiffness (ACAS), which are suitable for robotic assembly of low-stiffness parts. The proposed ACVS is achieved without a position trajectory, which reduces the number of parameters that need to be adjusted by the user. Furthermore, overshoot is suppressed by calculating the stiffness parameter in real time. The ACAS scheme adjusts the stiffness value in ACVS through a multilayer feedforward neural network; therefore, its overshoot is lower than that of ACVS, and the contact force stability is enhanced. The ACAS scheme thus characterized by a slight overshoot and stable contact force enables stable assembly of low-stiffness parts via the maximum force generated by the robot.

Positional error compensation for aviation drilling robot based on Bayesian linear regression

The quality of aircraft components is directly affected by the positional accuracy of aviation drilling robot, which may further affect the assembly accuracy, flight performance and service life of aircraft. In this work, positional error compensation based on Bayesian linear regression (BLR) is proposed to improve the positional accuracy of aviation drilling robot. Firstly, the stochastic model of robot positional error is constructed by utilizing kinematic error model involving with random uncertainties. It proves that the positional error of robot joint space follows the Gaussian process (GP) and can be modeled through a linear regression model. Secondly, BLR is utilized to construct the positional error model of aviation drilling robot, which can provide the predicted positional error and the corresponding confidence interval (CI) of target position. These predicted values and CIs are used to realize error compensation. Experimental results show that, after error compensation, the average/maximum absolute positional error of the self-developed aviation drilling robot is decreased from 1.393mm/1.795 mm–0.081mm/0.167 mm. This indicates that the BLR-based positional error compensation is conductive to ameliorating the positional accuracy of aviation drilling robot. This study lays the foundation for the positional error compensation of industrial robots in aircraft assembly.

Configuration design of reconfigurable single-product robotic assembly line for capacity scalability

Rapid product changes and fierce global competition force companies to react quickly to market changes associated with dynamic capacity. This paper investigates how to design and reconfigure configurations of the single-product scalable reconfigurable robotic assembly line (RAL) over multiple demand periods (DPs) with fluctuating demands. A two-phase approach is proposed, in phase 1, the configuration of RAL is designed to minimize the capital cost following the initial demand. In phase 2, the reconfiguration for each DP is performed according to demand fluctuations to minimize the sum of capital costs and reconfiguration costs between consecutive DPs. Mathematical models for the two phases are established. Then a hybrid particle swarm algorithm combined with simulated annealing (PSOSA) is proposed for practical-sized problems, and PSO and SA are chosen for comparison. Two other different approaches are selected for comparison. In approach 1 (robust strategy), each DP of the product lifecycle is calculated separately, and then a fixed configuration that can satisfy the maximal capacity of all DPs is obtained. In approach 2 (changeable strategy), the minimal-cost configuration for each DP is calculated separately first, and then the reconfiguration cost between adjacent DPs is counted. Furthermore, a set of instances is designed and the computational results of three approaches accompanied by PSOSA, PSO and SA versus the devised instances demonstrate the superiority and robustness of the proposed approach and PSOSA.

Assembly Sequence and Assembly Path Planning of Robot Automation Products Based on Discrete Particle Swarm Optimization

The product assembly process in industrial production is a time-consuming and labour-intensive link, so the use of robots for intelligent assembly can achieve high-efficiency operations to a large extent. During the intelligent assembly process, robots need to carry out three key processes: product information modelling, assembly route planning and assembly sequence planning. The experiment examines assembly sequence planning and assembly route planning. In light of the deficiencies of the current assembly sequence planning technique, we propose a new approach, which is difficult to calculate, the experiment proposes to introduce the DPSO algorithm into the assembly sequence planning model. Meanwhile, to prevent the model from entering local optimal, the experiment adds disturbance operation on the basis of DPSO. That is, a part is randomly selected to be inserted into another position in the disassembly sequence and at the same time, the rest of the parts are moved to form a new disassembly sequence to update the particle's location. In addition to the assembly sequence planning, the experiment aims to improve the planning path obtained by the existing RRT algorithm, which is not smooth enough, that is, to simplify the installation path by reducing the change of direction. The simulation experiment findings demonstrate that the addition of the disturbance to DPSO algorithm successfully prevents the model from entering a local optimum state. The moving distance of the parts before path optimization is 52.0012mm and the number of moving direction changes is 8 times, while the moving distance of the parts after path optimization is reduced to 42.8094mm and the moving direction is changed only 2 times.

Vision-force-fused curriculum learning for robotic contact-rich assembly tasks.

Contact-rich robotic manipulation tasks such as assembly are widely studied due to their close relevance with social and manufacturing industries. Although the task is highly related to vision and force, current methods lack a unified mechanism to effectively fuse the two sensors. We consider coordinating multimodality from perception to control and propose a vision-force curriculum policy learning scheme to effectively fuse the features and generate policy. Experiments in simulations indicate the priorities of our method, which could insert pegs with 0.1 mm clearance. Furthermore, the system is generalizable to various initial configurations and unseen shapes, and it can be robustly transferred from simulation to reality without fine-tuning, showing the effectiveness and generalization of our proposed method. The experiment videos and code will be available at https://sites.google.com/view/vf-assembly.

Analysis of the performance of robotic assembly systems considering the usage of tools or jigs for the task-board task in the WRS 2018

ABSTRACT A task-board task is a task in which robots perform a set of fundamental assembly operations related to the assembly of a specific product such as inserting a part into another part, tightening bolts and nuts, attaching flexible parts, etc. In the task-board task competition in the industrial robotics category of the World Robot Summit (WRS) 2018, 15 teams from around the world performed the task. In our previous work, we observed recorded videos of the competition frame by frame and manually investigated the performance of robotic systems of the top four teams, focusing only on whether robots contact target parts. In this paper, we introduce frame sets to represent mainly contact states between robots, tools, or jigs and target parts to be handled for such a frame-by-frame analysis of the recorded videos, and based on the frame sets, we analyze the performance of robotic assembly systems of other teams as well as the top four teams in the task-board task competition in the WRS 2018, considering the usage of tools or jigs.

Linear multi-degree of freedom variable stiffness actuator for robotic assembly tasks

Linear multi-degree of freedom variable stiffness actuator for robotic assembly tasks

Digital twin model-based smart assembly strategy design and precision evaluation for PCB kit-box build

Research concerning microelectronics assembly has attracted increasing attention from the manufacturing industry. In this context, achieving precise placement of the printed circuit board (PCB) inside the enclosure is the most critical aspect of the kit-box build assembly task. However, automating the PCB kit-box build assembly (PKBA) process remains a challenging work. This study presents a Digital Twin (DT) model that enables accurate perception of object pose for intelligent PKBA assembly, facilitating real-time monitoring and evaluation of its service status. In our system, a symmetry-drive method is proposed to optimize the initial pose in 6-DoF matching during DT assembly. Based on the developed technology, a three-stage learning method is established to achieve grasping point localization and robotic assembly trajectory planning. To ensure accurate and robust robot assembly, we developed a PKBA quality prediction model based on the DT system, which predicts the uncertainty of actual PCB assembly positioning by small displacement torsor (SDT) theory and Monte Carlo methods. Particularly, the assembly quality of the actual product is effectively monitored when the state of the virtual simulation model corresponds to the physical assembly object. Finally, a prototype system and a case study involving dexterous assembly tasks are conducted to verify the effectiveness and feasibility of the proposed method. The results indicate that the proposed PKBA strategy achieves an 82% assembly success rate. By employing well-designed strategies, our method ensures that the majority of errors are below 0.8 mm and 0.6 degrees.

Design and motion planning of a 7-DOF assembly robot with heavy load in spacecraft module

Currently, there is great demand for automated assembly of equipment inside spacecraft module, which would require an assembly robot with small size, large workspace, high load capacity, and high flexibility. To meet this demand, a redundant seven-degree-of-freedom (7-DOF) assembly robot with three prismatic joints and four rotating joints is proposed in this study. First, a kinematic model of the assembly robot is established, and the positional forward kinematics are derived. Then, the positional inverse kinematic equations are derived by using the fixed joint method and the coordinate inverse transformation method for the characteristics of the robot with redundant degrees of freedom. The inverse solution optimization model of the assembly robot is designed based on genetic algorithm, and B-spline interpolation algorithm is used for joint trajectory planning. Next, NSGA-2 algorithm is used to construct the trajectory optimization model, and the time-pulsation multi-objective Pareto optimal frontier is obtained. Finally, the joint simulation model of mechatronics and the prototype experiment are established to verify the effectiveness of the optimization results. Analysis shows that the proposed assembly robot has better load-bearing capacity and dynamic characteristics than similar existing assembly robots. Furthermore, the segmented trajectory optimization strategy for the assembly robot using inverse solution optimization, interpolation algorithm, and NSGA-2 based multi-objective optimization gets each motion joint to realize the trajectory tracking function quickly and stably, and the trajectory tracking error is small enough to meet the requirements of end positioning accuracy.

A Novel Diagnostic Tool for Human-Centric Quality Monitoring in Human–Robot Collaboration Manufacturing

Abstract The manufacturing industry is currently facing an increasing demand for customized products, leading to a shift from mass production to mass customization. As a result, operators are required to produce multiple product variants with varying complexity levels while maintaining high-quality standards. Further, in line with the human-centered paradigm of Industry 5.0, ensuring the well-being of workers is equally important as production quality. This paper proposes a novel tool, the “Human–Robot Collaboration Quality and Well-Being Assessment Tool” (HRC-QWAT), which combines the analysis of overall defects generated during product variant manufacturing with the evaluation of human well-being in terms of stress response. The HRC-QWAT enables the evaluation and monitoring of human–robot collaboration systems during product variant production from a broader standpoint. A case study of collaborative human–robot assembly is used to demonstrate the applicability of the proposed approach. The results suggest that the HRC-QWAT can evaluate both production quality and human well-being, providing a useful tool for companies to monitor and improve their manufacturing processes. Overall, this paper contributes to developing a human-centric approach to quality monitoring in the context of human–robot collaborative manufacturing.

Modular robotic manipulator and ground assembly system for on-orbit assembly of space telescopes

In the field of astronomical observation, large space telescopes are key tools for deep space exploration. However, the aperture of space telescopes is limited by the ability to fabricate large monolithic mirrors or lenses and by severe restrictions on the overall volume and mass capacity of the launch vehicle. The use of robots for autonomous assembly while in orbit is a promising option for the deployment of large-aperture space telescopes. In this paper, we describe a modular robotic manipulator that offers high precision and flexibility. A space telescope ground assembly system is then designed, the key components of which are a redundant robotic manipulator and a reconfigurable telescope unit. The ground-based assembly of the space telescope is implemented through a combination of trajectory planning, visual perception, and robotic supple control techniques, validating the ability of the robotic manipulator to automatically assemble modular space telescopes. Finally, the developed robotic manipulator is used to conduct ground assembly demonstration experiments, and the modular space telescope is assembled according to the assembly task plan, accumulating technical experience for future space telescope assembly tasks in orbit.

Positioning accuracy enhancement of a robotic assembly system for thin-walled aerostructure assembly

Aircraft assembly demands efficient and accurate drilling and fastening of a large number of complex thin-walled aircraft parts. In this paper, a robotic assembly system is developed. The machine design and component functions of the robotic system are first discussed. Subsequently, the formation of the relative positioning error between the end-effector and an aircraft part is analyzed and modeled. Measuring instruments are used for relative error measurement to compensate for drilling positions. To achieve high positioning accuracy, we theoretically compare relative positioning errors across instrument configurations from a tolerance management perspective to recommend a good hand-eye configuration. Besides, the impact of hand-eye offset on positioning accuracy is explored based on positioning error Jacobian to guide the end-effector design and the setting of the vision coordinate system. After that, the on-machine hand-eye calibration and system positioning methods are proposed and modeled, eliminating the effect of the dissimilarity of the robot's absolute positioning error. For positioning and processing, the control system of the multifunctional robotic assembly system is built. Finally, experiments for accurate positioning are conducted on the robotic assembly system developed to assemble an aircraft. With the recommended hand-eye configuration, the minimized hand-eye offset, and the on-machine hand-eye calibration, the maximal positioning error is 0.08 mm, which can adequately meet the accuracy standard of the aircraft assembly.

Integrating force and vision feedback for flexible assembly system

The Industrial Assembly Challenge at the World Robot Summit 2020 was a competition showcasing the latest technologies in robot assembly tasks. Teams had two days to set up an autonomous assembly system and had to manipulate surprise parts on the competition day. The manufacturing industry has trended towards high-mix low-volume production, which often requires frequent tool changes and can be difficult to set up with industrial robots. Small- and medium-sized enterprises (SMEs) may struggle to use robots for a variety of products, particularly in assembly tasks requiring high accuracy and specialized tools. To improve assembly performance, we propose an assembly method using multiple types of sensors, including an RGB camera and six-axis force-torque sensor. This system allows for flexible assembly tasks without strict alignment, does not require jigs or dedicated hands for each object, and can be easily adapted to different production scenarios. In this paper, we present the motion strategy of our proposed assembly system that participated in the World Robot Summit 2020 and demonstrate how it was able to robustly accomplish tasks such as screw tightening and peg-in-hole by combining information from the camera with information from force-torque sensors.

Manual assembly actions segmentation system using temporal-spatial-contact features

PurposeAccurate segmentation of artificial assembly action is the basis of autonomous industrial assembly robots. This paper aims to study the precise segmentation method of manual assembly action.Design/methodology/approachIn this paper, a temporal-spatial-contact features segmentation system (TSCFSS) for manual assembly actions recognition and segmentation is proposed. The system consists of three stages: spatial features extraction, contact force features extraction and action segmentation in the temporal dimension. In the spatial features extraction stage, a vectors assembly graph (VAG) is proposed to precisely describe the motion state of the objects and relative position between objects in an RGB-D video frame. Then graph networks are used to extract the spatial features from the VAG. In the contact features extraction stage, a sliding window is used to cut contact force features between hands and tools/parts corresponding to the video frame. Finally, in the action segmentation stage, the spatial and contact features are concatenated as the input of temporal convolution networks for action recognition and segmentation. The experiments have been conducted on a new manual assembly data set containing RGB-D video and contact force.FindingsIn the experiments, the TSCFSS is used to recognize 11 kinds of assembly actions in demonstrations and outperforms the other comparative action identification methods.Originality/valueA novel manual assembly actions precisely segmentation system, which fuses temporal features, spatial features and contact force features, has been proposed. The VAG, a symbolic knowledge representation for describing assembly scene state, is proposed, making action segmentation more convenient. A data set with RGB-D video and contact force is specifically tailored for researching manual assembly actions.

Digital twin for a robot assembly line: Architecture and implementation

As an enabling technology and means to practice the concept of intelligent manufacturing, digital twin has attracted extensive attention from both industry and academia. In view of the lack of concrete implementation methods of digital twin robot assembly lines, and the complex implementation process, and long development cycle using existing methods, the implementation of digital twin robot assembly lines is a significant research problem. In view of this, this paper proposes an architecture as well as an implementation method of a digital twin system for a robot assembly line. Taking a robot assembly production line as an example, a digital twin architecture and modular development approach are proposed, and three key technologies for implementing the digital twin system, namely virtual model construction, real-time data acquisition and accurate virtual-real mapping, are elaborated. The architecture and implementation method can quickly and effectively facilitate building a digital twin system of the robot assembly line, and realizing real-time monitoring and intelligent analysis of all elements of the physical robot production line.

Optimal assembly sequence planning with tool uncertainties

Developing a practical and feasible assembly sequence plan for industrial applications is still challenging during product design and planning. Because of the need for more consideration of practical uncertainties, most of the assembly sequence plans generated by planners are inapplicable to real industrial-based problems. We have considered a crucial practical uncertainty attribute, tool uncertainties, in the current research. A novel approach has been proposed to generate optimal robotic assembly sequence plans by combining tool accessibility and part geometry. This research introduces two essential assembly tool accessibility attributes: tool-integrated geometric feasibility and tool-swept volume. In addition, the automatic extraction of tool accessibility attributes has been proposed. The proposed method is explained with a proper model and essential details such as liaison data, tool-integrated bounding box coordinates, tool-integrated geometric feasibility, and tool-swept volume data. Finally, the practical feasibility of the proposed method is verified for a different product configuration.

Auxetic Behaviour of Rigid Connected Squares.

The paper presents an analysis of rotating rigid unit (RRU) auxetic structures, the special property of which is negative Poisson's ratio. The crucial features of such modified structures are the well-functioning linkages of the square units at their pivot points. This ensures the stable functioning of such structures in tension or compression. The presented geometrical analysis of these auxetic structures may facilitate their adequate construction and allow one to determine the expected values of their expansion as well as the desired porosity. The results are confirmed based on the behaviour of physical models produced by the assembly of square units. The change in the dimensions of the physical models when moving from a closed to an open position is consistent with the predictions of the geometric models. By modifying the well-known 'rotating squares' model, physical structures with auxetic properties are obtained that can be utilised in industrial conditions, where a simultaneous change of linear dimensions is needed-either in compression or in tension. The assembly method may prove efficient in building such structures, given the abilities of assembly robots to regularly arrange the unit cells in lines and rows and to connect them with rings at the designated positions (evenly spaced perforations). The presented auxetic structures might find their potential application in, e.g., expansion joints or structures in which the porosity is mechanically changed, such as mesoscale structures. The tested structures subjected to high compressive forces buckle when the yield strength of the rigid unit material is exceeded.

Design and development of a low-cost 5-DOF robotic arm for lightweight material handling and sorting applications: A case study for small manufacturing industries of Pakistan

Due to the ever-increasing demand for higher production rates and the shortage of skilled labor in small industries, material handling and sorting have become extremely tedious and challenging. Industrial automation-led effective material-handling solutions like robotic arms have gained immense importance as they provide an alternative to human involvement, contribute to higher sorting accuracy, and provide enhanced safety. However, the adaptability of these robotic arms in small manufacturing industries in Pakistan is mainly hindered due to their higher costs and concerns with their structural durability. This paper presents the development of a low-cost 5-DOF robotic arm with a designed payload limit of 1 kg and automatically sorts objects fed through a conveyer belt. Catering to the compact sizing, high strength, and lower payload requirements of small industries, aluminum was selected as the material of the robotic arm due to its superior strength-to-weight ratio while being lightweight. Arm geometry was developed using SOLIDWORKS® software, which was further processed in ANSYS® software to perform the static structural analysis of the robotic arm using Finite Element Analysis (FEA). The fine meshing of the robotic arm assembly was done using triangular elements with the total number of elements and total nodes 52134 and 89104, respectively. A single point load was applied on the end effector, and the force was kept downward with an incremental loading of 1 kg starting from 100 g. These FEA simulations show that the robotic arm can hoist considerable weight while maintaining its structural integrity and directionality. The proposed robotic arm is also well-suited for manipulating objects in tight spaces due to its compact size and customizable range of motion, making it an ideal choice for applications that require precise manipulation of light loads.

A Stereo-Vision-Based Spatial-Positioning and Postural-Estimation Method for Miniature Circuit Breaker Components

This paper proposes a stereo-vision-based method that detects and registers the positions and postures of muti-type, randomly placed miniature circuit breaker (MCB) components within scene point clouds acquired by a 3D stereo camera. The method is designed to be utilized in the flexible assembly of MCBs to improve the precision of gripping small-sized and complex-structured components. The proposed method contains the following stages: First, the 3D computer-aided design (CAD) models of the components are converted to surface point cloud models by voxel down-sampling to form matching templates. Second, the scene point cloud is filtered, clustered, and segmented to obtain candidate-matching regions. Third, point cloud features are extracted by Intrinsic Shape Signatures (ISSs) from the templates and the candidate-matching regions and described by Fast Point Feature Histogram (FPFH). We apply Sample Consensus Initial Alignment (SAC-IA) to the extracted features to obtain a rough matching. Fourth, fine registration is performed by employing Iterative Closest Points (ICPs) with a K-dimensional Tree (KD-tree) between the templates and the roughly matched targets. Meanwhile, Random Sample Consensus (RANSAC), which effectively solves the local optimal problem in the classic ICP algorithm, is employed to remove the incorrectly matching point pairs for further precision improvement. The experimental results show that the proposed method achieves spatial positioning errors smaller than 0.2 mm and postural estimation errors smaller than 0.5°. The precision and efficiency meet the requirements of the robotic flexible assembly for MCBs.