Robotic Assembly Cell Research Articles

A comparative analysis of constraint and connectivity graph techniques for assembly sequence generation in robotic assembly cells

The paper describes a comparative study on the assortment of appropriate assembly-generating techniques for robotic assembly cells. Several techniques have been developed and tested for the creation of viable sequences. Most of these techniques employ computational techniques to generate assembly sequences. Nevertheless, these techniques rely on a large number of assumptions and use insignificant data during the development of assembly sequences. In the present study, two techniques, viz., the connectivity graph technique and the constraint technique are selected, since they are very general, expedient, and easy to implement. Further, selected techniques can easily be automated to increase the productivity of robotics assembly cell. Each technique was applied to four products with different complexities to check their suitability in the context of robotic assembly cell. In the connectivity graph technique, the disassembly of components begins with the sink node in the connectivity graph and ends when no sink node is available. The constraint technique chooses two constraints: the G-constraint and the C-constraint. The process of disassembling the components starts with a component that does not have any G-constraint and satisfies the C-constraint. The G-constraint checks for geometric interference, and the C-constraint checks for connectivity relationships among the components. Both techniques are automated using C++ programming to obtain rapid and efficient results. The automation of assembly techniques is expected to lead to a revolutionary transformation in the assembly sector. The connectivity graph technique is found to be suitable for the robotic assembly cells as described in this article.

A new redundancy strategy for enabling graceful degradation in resilient robotic flexible assembly cells

The development of resilience in manufacturing systems has drawn more attention than ever. Using redundant components is one of the key strategies for building and enhancing the resilience of a manufacturing system. However, current redundancy strategies require duplicated machinery employed either in active or in standby status. This in turn causes extra costs in designing and achieving resilience. Achieving an efficient deployment of the redundant component in the face of failures is also challenging. In this paper, we introduce a novel redundancy strategy, called adaptive standby redundancy (ASR), to achieve resilient performance for discrete manufacturing systems while reducing the cost of employing the duplicated components that are typically used in traditional systems. This novel strategy permits achievement of high levels of utilisation of the system and graceful degradation in case of failure, keeping the system functional. The strategy is then validated in a developed robotic flexible assembly cell (RFAC), which is tested and results on its efficacy and performance enhancement are discussed.

Multi-agent cooperative swarm learning for dynamic layout optimisation of reconfigurable robotic assembly cells based on digital twin

AbstractTo meet the requirement of product variety and short production cycle, reconfigurable manufacturing system is considered as an effective solution in addressing current challenges, such as increasing customisation, high flexibility and dynamic market demand. Dynamic factory layout design and optimisation are the crucial factors in response to rapid change in the mechanical structure, software and hardware integration, as well as production capability and functionality adjustment. Nevertheless, in the current research, the layout design for reconfigurable manufacturing systems is usually simplified with autonomous devices being regarded as 2D shapes. Issues such as overlapping and transportation distance are also addressed in an approximate form. In this paper, we present a novel multi-agent cooperative swarm learning framework for dynamic layout optimisation of reconfigurable robotic assembly cells. Based on its digital twin established in the proposed learning environment (constructed in Visual Components and controlled by TWINCAT), the optimisation framework uses 3D digital representation of the facility models with minimal approximation. Moreover, instead of using a traditional centralised learning manner, multi-agent system could provide an alternative way to address the layout issues combined with the proposed decentralised multi-agent cooperative swarm learning. In order to verify the application feasibility of the learning framework, two aerospace manufacturing use cases were implemented. In the first use case, the layout compactness is reduced by 3.8 times compared with the initial layout setting, the simulated production time is reduced by 2.3 times, and the rearrangement cost decreased by 33.4$$\%$$ % . In addition, all manufacturing activity within the cell can be achieved with a feasible robot path, meaning without any joint limits, reachability or singularity issue at each key assembly point. In the second use case, we demonstrated that with the proposed dynamic layout optimisation framework, it is possible to flexibly adjust learning objectives by selecting various weight parameters among layout compactness, rearrangement cost and production time.

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

Multi-Agent Reinforcement Learning for Real-Time Dynamic Production Scheduling in a Robot Assembly Cell

As industry rapidly shifts towards mass personalisation, the need for a decentralised multi-agent system capable of dynamic flexible job shop scheduling (FJSP) is evident. Traditional heuristic and meta-heuristic scheduling methods cannot achieve satisfactory results and have limited application to static environments. Recent Reinforcement Learning (RL) approaches that consider dynamic FJSP, lack flexibility and autonomy as they use a single-agent centralised model, assuming global observability. As such, we propose a Multi-Agent Reinforcement Learning (MARL) system for scheduling dynamically arriving assembly jobs in a robot assembly cell. We applied a Double DQN-based algorithm and proposed a generalised observation, action and reward design for the dynamic FJSP setting. Using a centralised training phase, each agent (i.e., robot) in the assembly cell executes decentralised scheduling decisions based on local observations. Our solution demonstrated improved performance against rule-based heuristic methods, for optimising makespan. We also reported the impact of different observation sizes of each agent on optimisation performance.

Engineering Method and Tool for the Complete Virtual Commissioning of Robotic Cells

Intelligent robotic manufacturing cells must adapt to ever-varying operating conditions, developing autonomously optimal manufacturing strategies to achieve the best quality and overall productivity. Intelligent and cognitive behaviors are realized by using distributed controllers, in which complex control logics must interact and process a wide variety of input/output signals. In particular, programmable logic controllers (PLCs) and robot controllers must be coordinated and integrated. Then, there is the need to simulate the robotic cells’ behavior for performance verification and optimization by evaluating the effects of both PLC and robot control codes. In this context, this work proposes a method, and its implementation into an integrated tool, to exploit the potential of ABB RobotStudio software as a virtual prototyping platform for robotic cells, in which real robots control codes are executed on a virtual controller and integrated with Beckhoff PLC environment. For this purpose, a PLC Smart Component was conceived as an extension of RobotStudio functionalities to exchange signals with a TwinCAT instance. The new module allows the virtual commissioning of a complete robotic cell to be performed, assessing the control logics effects on the overall productivity. The solution is demonstrated on a robotic assembly cell, showing its feasibility and effectiveness in optimizing the final performance.

Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems.

Smart manufacturing systems are being advocated to leverage technological advances that enable them to be more resilient to faults through rapid diagnosis for performance assurance. In this paper, we propose a co-simulation approach for engineering digital twins (DTs) that are used to train Bayesian Networks (BNs) for fault diagnostics at equipment and factory levels. Specifically, the co-simulation model is engineered by using cyber–physical system (CPS) consisting of networked sensors, high-fidelity simulation model of each equipment, and a detailed discrete-event simulation (DES) model of the factory. The proposed DT approach enables injection of faults in the virtual system, thereby alleviating the need for expensive factory-floor experimentation. It should be emphasized that this approach of injecting faults eliminates the need for obtaining balanced data that include faulty and normal factory operations. We propose a Structural Intervention Algorithm (SIA) in this paper to first detect all possible directed edges and then distinguish between a parent and an ancestor node of the BN. We engineered a DT research test-bed in our laboratory consisting of four industrial robots configured into an assembly cell where each robot has an industrial Internet-of-Things sensor that can monitor vibrations in two-axes. A detailed equipment-level simulator of these robots was integrated with a detailed DES model of the robotic assembly cell. The resulting DT was used to carry out interventions to learn a BN model structure for fault diagnostics. Laboratory experiments validated the efficacy of the proposed approach by accurately learning the BN structure, and in the experiments, the accuracy obtained by the proposed approach (measured using Structural Hamming Distance) was found to be significantly better than traditional methods. Furthermore, the BN structure learned was found to be robust to variations in parameters, such as mean time to failure (MTTF).

Comparison of visual servoing technologies for robotized aerospace structural assembly and inspection

This work presents a novel approach for visual servoing of robotized aerospace manufacturing cells, based on the combined use of a camera and a 2D-beam scanner and a 1-D beam distance sensor attached to the end-effector of a collaborative robot. The proposed system can detect features associated with mechanical bounds over the aircraft structure, making possible the robot automatic online trajectory/path generation when the robot performs a target task over an aeronautical part. The effectiveness of this method is demonstrated by means of experimental evaluations carried out in unstructured environments without illumination and temperature control (simulating real shop floor conditions), evincing that the proposed approach is more robust. We also show that it is able to automatically generate and follow a target path with an accuracy of 0.40 mm and repeatability of 0.59 mm, which is roughly 2 times more accurate than the classical computer vision servoing used in the experiments. The proposed solution is suitable to applications in modern collaborative robotized aerospace assembly cells.

Planning and optimization of robotic pick-and-place operations in highly constrained industrial environments

PurposeThe purpose of this paper is to give a comprehensive solution method for the manipulation of parts with complex geometries arriving in bulk into a robotic assembly cell. As bin-picking applications are still not reliable in intricate workcells, first, the problem is transformed to a semi-structured pick-and-place application, then by collecting and organizing the required process planning steps, a methodology is formed to achieve reliable factory applications even in crowded assembly cell environments.Design/methodology/approachThe process planning steps are separated into offline precomputation and online planning. The offline phase focuses on preparing the operation and reducing the online computational burdens. During the online phase, the parts laying in a semi-structured arrangement are first recognized and localized based on their stable equilibrium using two-dimensional vision. Then, the picking sequence and corresponding collision-free robot trajectories are planned and optimized.FindingsThe proposed method was evaluated in a geometrically complex experimental workcell, where it ensured precise, collision-free operation. Moreover, the applied planning processes could significantly reduce the execution time compared to heuristic approaches.Research limitations/implicationsThe methodology can be further generalized by considering multiple part types and grasping modes. Additionally, the automation of grasp planning and the enhancement of part localization, sequence planning and path smoothing with more advanced solutions are further research directions.Originality/valueThe paper proposes a novel methodology that combines geometrical computations, image processing and combinatorial optimization, adapted to the requirements of flexible pick-and-place applications. The methodology covers each required planning step to reach reliable and more efficient operation.

Visual servo guided cyber-physical robotic assembly cell

Advanced robotic assembly systems are promising great benefits to the manufacturing sector, once the shortcomings of the current technologies are overcome. One of the biggest obstacles to introducing flexible robotic systems is the ability of robots to sense and perceive the semi-structured or unstructured environment. This paper presents a cyber-physical system based framework of a robotic assembly cell that is capable of recognizing resources of assembly processes, based on information from various visual sensors and updating and adapting the assembly plan in the digital twin of the robotic cell. Thus, calculates and executes feasible motion plans of the robots based on accurate digital information. The application of the framework is demonstrated in a physical assembly process.

An Agent-Based System for Automated Configuration and Coordination of Robotic Operations in Real Time—A Case Study on a Car Floor Welding Process

This paper investigates the feasibility of using an agent-based framework to configure, control and coordinate dynamic, real-time robotic operations with the use of ontology manufacturing principles. Production automation agents use ontology models that represent the knowledge in a manufacturing environment for control and configuration purposes. The ontological representation of the production environment is discussed. Using this framework, the manufacturing resources are capable of autonomously embedding themselves into the existing manufacturing enterprise with minimal human intervention, while, at the same time, the coordination of manufacturing operations is achieved without extensive human involvement. The specific framework was implemented, tested and validated in a feasibility study upon a laboratory robotic assembly cell with typical industrial components, using real data derived from a car-floor welding process.

Towards robot cell matrices for agile production – SDU Robotics' assembly cell at the WRC 2018

ABSTRACTTo support shifting to high mix/low volume production, manufacturers in high wage countries aim for robotizing their production operations – with a special focus on the late production phases, where robotic assembly cells are then confronted with any complexities resulting from part and product varieties. The ‘World Robot Challenge 2018’ (WRC 2018) emulated such high mix/low volume production scenarios in a competition taking place in Tokyo, Japan. As part of our activities in SDU's newly founded I4.0 Lab, we integrated and advanced our experiences and developments from our various R & D projects in a novel robotic assembly cell design to compete in the WRC 2018. This article describes the system architecture as well as main aspects of its implementation regarding robot control, robot programming and computer vision and how they contributed to winning the challenge. Due to the application of collaborative robots, the cell design allows for operation without fences. Hence, multiple copies of the cell can be arranged in a highly reconfigurable, highly adaptable matrix structure in which several production flows can be handled concurrently. This concept was demonstrated by the installation of a duplicate cell that allowed for parallel developments on two cells and prolonged development also after shipping the first cell to Japan.

Distributed multi-agent scheduling and control system for robotic flexible assembly cells

This paper deals with the development of a distributed multi-agent system (DMAS) for scheduling and controlling Robotic Flexible Assembly Cells (RFAC). In the proposed system, an approach for solving one of the most challenging decisional problems in RFAC is proposed and implemented. This problem is related to the products operations scheduling which requires their allocation and sequencing on the robots, while satisfying products and robots constraints under makespan minimization. The proposed DMAS addresses this challenge by using a cooperative approach supported by three kinds of autonomous control agents: supervisory agent, local agents, and remote agents. These agents interact by a negotiation protocol based on common dispatching rules for coordinating their individual decisions, satisfying their local objective and providing an optimized global solution. Moreover, because of the dynamic nature of the assembly systems, it is imperative to consider external disturbances on production scheduling and to solve the related issues. Consequently, DMAS has the ability to respond and manage some dynamic events that may occur in the cells such as unexpected robot breakdown or dynamic products arrivals. Computational results on benchmarks show the effectiveness and the robustness of the proposed system.

Cognition-enhanced assembly sequence planning for ergonomic and productive human–robot collaboration in self-optimizing assembly cells

Self-optimizing robotized assembly systems are able to compensate the restricted operation purpose of traditional robotized automation, in order to adapt dynamically to changed production conditions. As the human operator is directly involved in the assembly process, the interaction between the human and the robot has to be designed carefully to avoid exposing the human to excessive physical and cognitive strain. For controlling a robotized assembly cell, a Cognitive Control Unit (CCU) was developed that uses the cognitive software Soar and human-like assembly strategies to achieve a transparent and understandable assembly process. To minimize the cognitive and ergonomic risks during assembly, the CCU was extended by a graph-based assembly sequence planner (GASP). The GASP is able to find the optimal assembly sequence by using a complete assembly graph of the final product as well as generic production rules for assessing the ergonomic conditions of the individual assembly steps. The presented simulation study validates the functionality of the GASP with respect to the number of workflow switches between the human and the robot, the number of switches between the robotic tools, as well as the number of assembly group switches required to collaboratively assemble a model of a Stromberg carburetor. The results show a significant reduction of all three measures. The number of parts and the type of assessment of the assembly steps have a significant impact here.

Multi-objective optimisation of dynamic scheduling in robotic flexible assembly cells via fuzzy-based Taguchi approach

This paper presents Taguchi method coupled with fuzzy logic for dealing with multi-objective optimisation problems for dynamic scheduling in robotic flexible assembly cells (RFACs). This is the first study to address these particular problems. In this study, Taguchi optimisation method has been applied to reduce the number of experiments required for scheduling RFACs. The experiments are implemented with four different scheduling factors, namely sequencing rule, dispatching rule, cell utilisation and due date tightness. These factors are difficult to optimise considering the objectives of multiple functions instead of a single objective. Therefore, a multiple performance characteristics index (MPCI) based fuzzy logic approach has been developed to derive the optimal solution. The predicted results of MPCIs have been verified via a confirmation test. Results of the confirmation test show significant improvement in MPCI using the optimal levels of the scheduling factors.

Anthropomorphic design of robotic arm trajectories in assembly cells

BACKGROUND: Anthropomorphism is attribution of human form or behavior to non-human agents. Its application in a robot increases occupational safety and user acceptance and reduces the mental effort needed to anticipate robot behavior. OBJECTIVE: The research question focuses on how the anthropomorphic trajectory and velocity profile of a virtual gantry robot affects the predictability of its behavior in a placement task. METHODS: To investigate the research question, we developed a virtual environment consisting of a robotized assembly cell. The robot was given human movements, acquired through the use of an infrared based motion capture system. The experiment compared anthropomorphic and constant velocity profiles. The trajectories were based on human movements of the hand-arm system. The task of the participants was to predict the target position of the placing movement as accurately and quickly as possible. RESULTS: Results show that the anthropomorphic velocity profile leads to a significantly shorter prediction time (α = 0.05). Moreover, the error rate and the mental effort were significantly less for the anthropomorphic velocity profile. Based on these findings, a speed-accuracy trade-off can be excluded. CONCLUSIONS: Participants were able to estimate and predict the target position of the presented movement significantly faster and more accurately when the robot was controlled by the human-like velocity profile.

Intention management within a BDI framework

The Belief-Desire-Intention (BDI) model of agency provides a powerful technique for describing goal-based behavior for both individual agents and, more recently, agent teams. Numerous frameworks have been developed since the model was first proposed in the early 1980's. However, none of these frameworks have explicitly represented intention, which has meant that intention-based reasoning has had no direct framework support. Given the importance of this in the development of practical agent applications, we consider this to be a major shortcoming of existing frameworks. This paper explores how explicitly represented goals can be used as both a unifying modeling concept for the management of intention, and as the basis for a BDI framework. The exploration is grounded both in terms of software – a recently developed BDI framework called GORITE and in application – an execution system for a robotic assembly cell. Both are discussed in detail.

Multi-robot spot-welding cells: An integrated approach to cell design and motion planning

The necessity to manage several vehicle models on the same robotized assembly cell has made the cell design and the robot off-line motion planning two fundamental activities. Industrial practice and state-of-the-art methods focus on the technical issues of each activity, but no integrated approach has been yet proposed, resulting in a lack of optimality for the final cell configuration. The paper introduces a formalization of the whole process and proposes a heuristic multi-stage method for the identification of the optimal combination of cell design choices and motion planning. The proposed architecture is depicted through a real case for welding application.

Simulation modelling and analysis of scheduling in robotic flexible assembly cells using Taguchi method

Due to increasing competition in the developing global economy, today’s companies are facing greater challenges than ever to employ flexible manufacturing systems (FMS) capable of dealing with unexpected events and meeting customers’ requirements. One such system is robotic flexible assembly cells (RFACs). There has been relatively little work on the scheduling of RFACs, even though overall scheduling problems of FMS have attracted significant attention. This paper presents Taguchi optimisation method in conjunction with simulation modelling in a new application for dynamic scheduling problems in RFACs, in order to minimise total tardiness and number of tardy jobs (NT). This is the first study to address these particular problems. In this study, Taguchi method has been used to reduce the minimum number of experiments required for scheduling RFACs. These experiments are based on an L9 orthogonal array with each trial implemented under different levels of scheduling factors. Four factors are considered simultaneously: sequencing rule, dispatching rule, cell utilisation and due date tightness. The experimental results are analysed using an analysis of mean to find the best combination of scheduling factors and an analysis of variance to determine the most significant factors that influence the system’s performance. The resulting analysis shows that this proposed methodology enhances the system’s scheduling policy.

APPLICATION OF A FUZZY-SIMULATION MODEL OF SCHEDULING ROBOTIC FLEXIBLE ASSEMBLY CELLS

This study describes the application of the develop ed model to the scheduling of the Robotic Flexible Assembly Cells (RFACs). The present study is a continuation of the paper titled “Development of a Fuzz ySimulation Model of Scheduling Robotic Flexible Assembly Cells”. This new model for multi-objective scheduling problems in RFACs was based on combining a fuzzy-based mathematical model with simulation software tools. In this study, a hypothetical case study of RFACs is presented to demonstrate the effectiveness of the developed model and then compare the results that are obtained by the new methodology with the common scheduling rules. The simulation results show that the performance of the proposed methodology outperforms the most popular scheduling rules from previous research.