Robotic Assembly Research Articles

Type-1 Robotic Assembly Line Balancing Problem That Considers Energy Consumption and Cross-Station Design

Robotic assembly lines are widely applied to mass production because of their adaptability and versatility. As we know, using robots will lead to energy-consumption and pollution problems, which has been a hot-button topic in recent years. In this paper, we consider an assembly line balancing problem with minimizing the number of workstations as the primary objective and minimizing energy consumption as the secondary objective. Further, we propose a novel mixed integer linear programming (MILP) model considering a realistic production process design—cross-station task, which is an important contribution of our paper. The “cross-station task” design has already been applied to practice but rarely studied academically in type-1 RALBP. A simulated annealing algorithm is developed, which incorporates a restart mechanism and an improvement strategy. Computational tests demonstrate that the proposed algorithm is superior to two other classic algorithms, which are the particle swarm algorithm and late acceptance hill-climbing algorithm.

Robust counterpart mathematical models for balancing, sequencing, and assignment of robotic U-shaped assembly lines with considering failures and setup times

Robust counterpart mathematical models for balancing, sequencing, and assignment of robotic U-shaped assembly lines with considering failures and setup times

Maintenance robot for remote assembly of protective jackets on live gas risers

Combining high-rise robotics research and polyurethane (PU) repair techniques presents new opportunities and unique challenges for utility pipe maintenance. Aiming at solving the complex pose alignment issue of robots and control PU curing, this paper automates the repair process by assembling protective jackets with PU foam. A wire-hoisted robot was equipped with anchors and a twin-gripper mechanism for workspace definition, along with a manipulator that positioned a pair of mirrored end effectors to perform surface polishing, jacket installation, and PU curing onto the pipe. Consequently, the robot pose was stabilized. The jacket pair was assembled onto the pipe with high precision, yielding high-quality repair, a success rate >96%, short cycle time, and low material wastage. This research may inspire innovative automation approaches for building maintenance, while utility companies can deploy the robot system for sustainable pipe service life.

Robotic Peg-in-Hole Assembly Strategy Research Based on Reinforcement Learning Algorithm

To improve the robotic assembly effects in unstructured environments, a reinforcement learning (RL) algorithm is introduced to realize a variable admittance control. In this article, the mechanisms of a peg-in-hole assembly task and admittance model are first analyzed to guide the control strategy and experimental parameters design. Then, the admittance parameter identification process is defined as the Markov decision process (MDP) problem and solved with the RL algorithm. Furthermore, a fuzzy reward system is established to evaluate the action–state value to solve the complex reward establishment problem, where the fuzzy reward includes a process reward and a failure punishment. Finally, four sets of experiments are carried out, including assembly experiments based on the position control, fuzzy control, and RL algorithm. The necessity of compliance control is demonstrated in the first experiment. The advantages of the proposed algorithms are validated by comparing them with different experimental results. Moreover, the generalization ability of the RL algorithm is tested in the last two experiments. The results indicate that the proposed RL algorithm effectively improves the robotic compliance assembly ability.

Design and experiment of on-orbit assembly ground simulation robot

This paper proposes a space telescope assembly scheme and a ROS-based on-orbit assembly robot simulation system. First, the system fuses the hand-eye camera and the global camera ArUco marked image information to complete the identification and position and pose estimation of the structural parts to be assembled. Then, the pose information is transmitted to the ROS motion control node through Modbus-TCP communication, and the dual-arm robot system is controlled to complete motion planning and obstacle avoidance, arriving at the designated position for precise pick and place. After many simulation experiments of the designed space telescope model assembly scheme, it has been proved that the target object pose estimation algorithm is accurate, and the dual-arm collaborative robot has high position accuracy, which can realize the ground simulation autonomous assembly of large-scale space facilities. The system has certain practical application value with high integration, strong expansibility and good portability.

Determining Exception Context in Assembly Operations from Multimodal Data.

Robot assembly tasks can fail due to unpredictable errors and can only continue with the manual intervention of a human operator. Recently, we proposed an exception strategy learning framework based on statistical learning and context determination, which can successfully resolve such situations. This paper deals with context determination from multimodal data, which is the key component of our framework. We propose a novel approach to generate unified low-dimensional context descriptions based on image and force-torque data. For this purpose, we combine a state-of-the-art neural network model for image segmentation and contact point estimation using force-torque measurements. An ensemble of decision trees is used to combine features from the two modalities. To validate the proposed approach, we have collected datasets of deliberately induced insertion failures both for the classic peg-in-hole insertion task and for an industrially relevant task of car starter assembly. We demonstrate that the proposed approach generates reliable low-dimensional descriptors, suitable as queries necessary in statistical learning.

Inventive redesign for automatic assembly in the household appliances industry

PurposeIn the redesign process of assembly components that need adaptation to robotic assembly, designers can find support from structured methodologies for innovation, such as the theory of inventive problem solving (TRIZ). This paper aims to illustrate the authors’ methodology for redesigning gas hobs components for adaptation to robotic assembly.Design/methodology/approachA designer approaching a redesign task of an assembly component of any kind for adaptation to robotic assembly must consider, first of all, the features and limitations of existing robotic assembly systems; the generation of new design ideas that best fit the requirements may result to be a very challenging task. Here, the TRIZ methodology has proven useful for generating design ideas and finding the best solution.FindingsThe authors’ methodology approaches the challenges of redesign tasks for robotic assembly adaptation, which exploits knowledge of automatic and robotic assembly systems and the TRIZ method for innovation; it has proven useful in the redesign, checks and prototyping of gas hobs components.Originality/valueThis paper shows how the TRIZ methodology can be integrated into the redesign process and its impact on an industrial environment. The work’s main value is to provide a set of steps to help the designers change their design components approach that is necessary but not still implemented to optimize the use of the automation.

Task board for the World Robot Summit 2020

The World Robot Summit (WRS) Industrial Robotics Category tries to evaluate the performances of assembly robot systems using a task board. The task board comprises an assembly board (task board or board) and a set of parts assembled to the board. Utilizing an appropriate task board expects to facilitate the development of assembly robot systems. This paper examines the components necessary for the design of the task board through the results and analyzes of the task board competition in the WRS 2020 (held in September 2021). The task board will serve as a benchmark in developing an assembly robot system that can assemble the belt drive unit, an assembly model for the WRS 2020 assembly competitions. The success rate of each part of the competition and the correlation with assembly competitions reinforces the validity of the task board design. This paper describes that the WRS 2020 task board had a larger correlation with the belt drive unit used in the assembly competitions than that of the WRS 2018 (0.54 vs. 0.29). The design in the WRS 2020 is more appropriate than that in the WRS 2018 as a benchmark for assembling the belt drive unit.

Planning to Build Block Structures With Unstable Intermediate States Using Two Manipulators

The work is inspired by the assembly of Soma block puzzles. Soma block puzzles usually include unstable intermediate states that require additional support to maintain stability temporarily. In the puzzles’ solution manual, we can observe that designers consider the characteristics that humans have two hands and can avoid an unstable intermediate state by using one hand to support the finished component and using the other hand to assemble an upcoming workpiece. Motivated by human behavior, this paper develops a planner that automatically finds an optimal assembly sequence for a dual-arm robot to build a woodblock structure while considering various constraints and supporting grasps from a second hand. It uses the mesh model of wood blocks and the final assembly state to generate possible assembly sequences and evaluate the optimal assembly sequence by considering the stability, graspability, assemblability, and the need for a second hand. Especially, the need for a second hand is resolved when supports from worktables and other workpieces are not enough to produce a stable assembly. A second hand can hold and support the unstable components so that the robot can further assemble new workpieces until the structure state becomes stable again. The output of the planner includes the optimal assembly orders, candidate grasps, assembly directions, and the supporting grasps (if needed). The output can help guide a dual-arm robot to perform motion planning and thus generate assembly motion. Experiments using various blocks and structures show the effectiveness of the proposed planner. Note to Practitioners—The presented planner can generate an optimal assembly order for a large variety of structures like decoration accessories, furniture, home interiors, frames, etc., in the practices. They can also be used for scenarios that need stacking or piling up multiple objects. The generated optimal assembly order is more friendly to dual-arm robot systems than previous assembly planners that ignored the merits of robotic collaboration. Also, the proposed assembly planner generates the necessary information for the motion planner, such as grasp poses and optimal assembly directions. A motion planner can directly use the generated results to plan robotic assembly motion.. The proposed assembly planner is expected to significantly reduce human effort and increase the efficiency of robotic assembly lines.

Learning to Assemble Noncylindrical Parts Using Trajectory Learning and Force Tracking

The purpose of this article is to teach a robot assembly skills from demonstrations, and we attempt to train both the trajectory and the insertion force simultaneously. We encode human demonstrations data via motion primitives and, then, generate a reference trajectory and a prescribed force profile for a new assembly task using the combination of the motion primitives. We then propose an adaptive impedance controller to track the prescribed force with unknown environment stiffness, where the impedance parameters are estimated by the optimal solution of an equivalent linearization model. Our approach combines adaptive impedance control techniques with learning from demonstration on the same that makes it tractable and applicable to a real robot. Experiments on several typical noncylindrical parts illustrate the efficiency of the proposed method.

Creation of More Efficient Work Environment through the New Design of the Automatic Robotic Assembly Station

Creation of More Efficient Work Environment through the New Design of the Automatic Robotic Assembly Station

Task level disentanglement learning in robotics using βVAE

Humans observe and infer things in a disentanglement way. Instead of remembering all pixel by pixel, learn things with factors like shape, scale, colour etc. Robot task learning is an open problem in the field of robotics. The task planning in the robot workspace with many constraints makes it even more challenging. In this work, a disentanglement learning of robot tasks with Convolutional Variational Autoencoder is learned, effectively capturing the underlying variations in the data. A robot dataset for disentanglement evaluation is generated with the Selective Compliance Assembly Robot Arm. The disentanglement score of the proposed model is increased to 0.206 with a robot path position accuracy of 0.055, while the state-of-the-art model (VAE) score was 0.015, and the corresponding path position accuracy is 0.053. The proposed algorithm is developed in Python and validated on the simulated robot model in Gazebo interfaced with Robot Operating System.

Design considerations for robotically assembled through-tenon timber joints

This research investigates the robotic assembly of timber structures connected by wood–wood connections. As the digitization of the timber construction sector progresses, digital tools, such as industrial robotic arms and Computer Numerical Control machines, are becoming increasingly accessible. The new-found ease with which wood can be processed stimulates a renewed interest in traditional joinery, where pieces are simply interlocked instead of being connected by additional metallic parts. Previous research established a computational workflow for the robotic assembly of timber plate structures connected by wood–wood connections. This paper focuses on determining the physical conditions that allow inserting through-tenon joints with a robot. The main challenge lies in minimizing the clearance between the tenon and the mortise in order to keep the connections as tight as possible. An experimental protocol has, therefore, been developed to quantitatively assess the performance of the insertion according to different geometric parameters. Robotic insertion tests have been carried out on over 50 samples of 39 mm Laminated Veneer Lumber. Results showed the interest of tapering the joint with a 5 degrees angle, in addition to introducing an offset of 0.05 mm, to minimize friction forces during the insertion. This configuration was confirmed by successfully assembling a 2,50 m long box girder with the same parameters.

Autonomous Obstacle Avoidance Path Planning for Grasping Manipulator Based on Elite Smoothing Ant Colony Algorithm

Assembly robots have become the core equipment of high-precision flexible automatic assembly systems with a small working range. Among different fields of robot technology, path planning is one of the most important branches. In the present study, an elite smoothing ant colony algorithm (ESACO) is proposed for spatial obstacle avoidance path planning of the grasping manipulator. In this regard, the state transition probability and pheromone update strategies are improved to enhance the search capability of path planning symmetry and the convergence of the algorithm. Then a segmented B-spline curve is presented to eliminate path folding points and generate a smooth path. Finally, a manipulator control system based on the Arduino Uno microcontroller is designed to drive the manipulator according to the planned trajectory. The experimental results show that the performance of the ESACO algorithm in different scenarios has symmetry advantages, and the manipulator can efficiently complete the simulation trajectory with high accuracy. The proposed algorithm provides a feasible scheme for the efficient planning of manipulators in equipment manufacturing workshops.

Development of a robust Wi-Fi/4G-based ROS communication platform for an assembly and repair mobile robot with reliable behavior under unstable network or connection failure

This paper presents various perspectives on designing and implementing a communication platform for a teleoperated mobile robot. The deployment of a communication network for a mobile robot and the integration of robot components in the developed communication platform are discussed in this article. A Wi-Fi-based communication network has been established, and to secure remote control over long distances, Internet-based communication via the 4G protocol has been launched using a virtual private network setup. Since an unstable network can damage the robot or the surrounding environment, an algorithm has been developed to monitor the state of the network connection through different protocols. The developed algorithm is able to detect network failure independently of the wireless communication technology used and notify the system of any disruptive communication. The robot's primary reactions to connection failure have been programmed to keep the robot under control until the communication with the control station is restored. Various experiments were carried out to validate the performance of the designed algorithm and statistical analysis was performed for each experiment. One of the main contributions of this study is the development of an algorithm for communication between the mobile robot and the control station based on both Wi-Fi and 4G, which is capable to keep the robot behavior safe and reliable in the presence of an unstable network or connection failure.

Optimization on flow field in an operational cleanroom subject to kinematic component and structural parameters

Due to the high uniformity and stability of streamline, unidirectional cleanrooms are widely used in semiconductor clean workshops. However, the moving parts and complex process of production in the cleanrooms may cause disturbance of the streamline, leading to increased contamination. In this study, a small wafer transport cleanroom with a SCARA (selective compliance assembly robot arm) as a moving part is considered, and a numerical method of overset mesh together with the momentum source method is employed to simulate the flow field, which is compensated with experimental measurements. Hence, taking the nonuniformity and air age as the indicators to evaluate the airflow and particulate emission efficiency of the cleanroom, nine influencing factors are investigated by making orthogonal simulations. This includes the velocity, the height, the cross-section area, and the moving range of the SCARA, the velocity of supplied air, the blowing ratio, and the distribution of the Fan Filter Units, the height and the grid porosity of the cleanroom. The optimal results alleviate the average and standard deviation of nonuniformity by 6.22% and 33.39%, respectively, compared with the original design of a factory. The particulate emission efficiency of the flow field is also significantly improved. Furthermore, the results inspire a potential optimal design for cleanrooms with dynamic loads in typical semiconductor processes.

Collision-free trajectory planning for robotic assembly of lightweight structures

This research presents a trajectory planning approach for robotic assembly of lightweight structures for COVID-19 healthcare facilities. The prefabricated building components of COVID-19 healthcare facilities have nonnegligible volume, where the crux of the scientific question lies in how to incorporate geometry-based collision checks in trajectory planning. This research developed an algorithm that refines the RRT* (Rapidly-exploring Random Tree-Star) algorithm to enable the detour of a planned trajectory based on the geometry of prefabricated components to prevent collisions. Testing of the approach reveals that it has satisfactory collision-avoiding and trajectory-smoothing performance, and is time- and labour-saving compared with the traditional human method. The satisfactory results highlight the practical implication of this research, where robots can replace human labour and contribute to the mitigation of COVID-19 spread on construction sites. The subsequent research will investigate the use of a collaborative robot to screw bolt connections after the components are assembled at locations.

Robot Precision Assembly Combining With Passive and Active Compliant Motions

Precision assembly is one of the primary goals of robots in manufacturing. Assembly strategies combining active and passive compliant control are presented herein. In this article, we construct a high-dimensional configuration space of robot assembly and then divide the configuration space to its subspaces. We further map the active compliant motion and the passive compliant motion of the manipulator into different subspaces. In one subspace, we construct the constraint function and design the passive compliant motion of the manipulator in the constraint region, where the uncertainties of the system should be eliminated by the environment constraints. In another subspace, we design a force controller based on the low-resolution force sensory information to control the position of the robot. The proposed method avoids the design of precision mechanism systems and the usage of high-quality sensors. Several experiments pertaining to peg-in-hole insertions are conducted to demonstrate the efficiency of the proposed method.

Spline-based trajectory generation to estimate execution time in a robotic assembly cell

Spline-based trajectory generation to estimate execution time in a robotic assembly cell

A policy iteration method for improving robot assembly trajectory efficiency

Bolt assembly by robots is a vital and difficult task for replacing astronauts in extra-vehicular activities (EVA), but the trajectory efficiency still needs to be improved during the wrench insertion into hex hole of bolt. In this paper, a policy iteration method based on reinforcement learning (RL) is proposed, by which the problem of trajectory efficiency improvement is constructed as an issue of RL-based objective optimization. Firstly, the projection relation between raw data and state-action space is established, and then a policy iteration initialization method is designed based on the projection to provide the initialization policy for iteration. Policy iteration based on the protective policy is applied to continuously evaluating and optimizing the action-value function of all state-action pairs till the convergence is obtained. To verify the feasibility and effectiveness of the proposed method, a noncontact demonstration experiment with human supervision is performed. Experimental results show that the initialization policy and the generated policy can be obtained by the policy iteration method in a limited number of demonstrations. A comparison between the experiments with two different assembly tolerances shows that the convergent generated policy possesses higher trajectory efficiency than the conservative one. In addition, this method can ensure safety during the training process and improve utilization efficiency of demonstration data.