Robotic Assembly Research Articles

An MRI-Conditional Flexible Endoscopic Robot with a Hydraulic Tendon-Driven Actuation System

This paper presents the conceptualization, mathematical modeling, and experimental validation of a magnetic resonance imaging (MRI)-conditional, flexible endoscopic robot for MRI-guided neurosurgery. The robotic assembly encompasses a straight rigid shaft, a tendon-driven steerable tip with two degrees of freedom, and a compact hybrid actuation system made of nonmagnetic modules. The robot’s small diameter, approximately 3 mm, allows for robot navigation through narrow cavities, such as nasal passages and the densely packed anatomy of the skull base. By filling the robot with deionized water, the MRI contrast of the robot can be improved, therefore enabling intra-operative tracking of the robot’s movement. In this paper, we present the design and fabrication process of the robotic system, kinematic analysis of the robot, and experimental studies to validate our model, characterize the robot, and test the feedback robot control. Furthermore, we demonstrate the MRI compatibility of the robot, along with the intra-operative tracking capability.

Prototyping of compliant grippers using FFF and TPU

PurposeThe purpose of this paper is to investigate the process of fused filament fabrication (FFF) of a compliant gripper (CG) using thermoplastic polyurethane (TPU) material. The paper studies the applicability of different CG designs and the efficiency of some design parameters.Design/methodology/approachAfter reviewing a number of different papers, two designs were selected for a number of exploratory experiments. Using design of experiments (DOE) techniques to identify important design parameters. Finally, the efficiency of the parts was investigated.FindingsThe research finds that a simpler design sacrifices some effectiveness in exchange for a remarkable decrease in production cost. Decreasing infill percentage of previous designs and 3D printing them, out of TPU, experimenting with different parameters yields functional products. Moreover, the paper identified some key parameters for further optimization attempts of such prototypes.Research limitations/implicationsThe cost of conducting FFF experiments for TPU increases dramatically with product size, number of parameters studied and the number of experiments. Therefore, all three of these factors had to be kept at a minimum. Further confirmatory experiments encouraged.Originality/valueThis paper addresses an identified need to investigate applications of FFF and TPU in manufacturing functional efficient flexible mechanisms, grippers specifically. While most research focused on designing for increased performance, some research lacks discussion on design philosophy, as well as manufacturing issues. As the needs for flexible grippers vary from high-performance grippers to lower performance grippers created for specific functions/conditions, some effectiveness can be sacrificed to reduce cost, reduce complexity and improve applicability in different robotic assemblies and environments.

Shape Control of Elastic Deformable Linear Objects for Robotic Cable Assembly

Currently, cables are manually installed in aircraft manufacturing scenarios. The utilization of robots in cable assemblies can enhance efficiency and ensure quality, presenting great promise for the future. However, the assembly process must control the shape of the cables, which is a significant challenge for robots. On the one hand, cables have high degrees of freedom in space, making accurate modeling of cable dynamics for robotic arm manipulation difficult; on the other hand, cable deformation has uncertainty, and the applied forces cause simultaneous deformation and motion, which is difficult to control. To address these problems, this study proposes a cable‐shape control method based on graph neural networks and online visual shape‐servoing. The method first approximates cable dynamics with a graph neural network. Then, in practice, the learnt model is used alongside visual‐based shape‐servoing to generate optimal robotic arm movements, controlling the cable to attain the desired shape. In the experiments, precise control of three different cable types is realized, and an example of a completed cable assembly is shown.

Sequence planning for on-orbit robotic assembly based on symbiotic organisms search with diversification strategy

The demands for efficiency and stability in space missions have necessitated the development of sequence planning schemes for on-orbit robotic assembly of space structures. A sequence planning method is proposed in this work based on robotic kinematic analysis and Symbiotic Organisms Search (SOS) algorithm with a diversification strategy. With feasibility as the constraint and assembly cost as the objective function, the assembly sequence planning is regarded as an optimization problem. The assembly relationship constraint is depicted as the dependency between assembly steps represented by a directed acyclic graph. An abstract modeling method based on robotic kinematics is proposed to construct geometric feasibility constraint, accommodating the interference stemming from the multi-degree-of-freedom motion of the robotic system. A diversification strategy is exploited to address the issue of local optima arising from solving the optimal sequence based on SOS. A cost prediction method using previously calculated populations as samples is proposed to reduce the reliance on robotic dynamic analysis for cost calculation. Finally, simulations are carried out to validate the practicability of the proposed method.

Knowledge-based Clustering Federated Learning for fault diagnosis in robotic assembly

Fault diagnosis in industrial robots is a critical aspect of intelligent manufacturing. However, the accuracy of fault diagnosis models can be significantly affected by the few-shot problem, which refers to the limited availability of labeled data for training. Traditional methods often rely on combining data from different sources, which can raise privacy concerns due to the sensitive nature of the data involved. Federated Learning has emerged as a privacy-preserving approach, but it faces challenges in dealing with the Non-IID (Non-Independent and Identically Distributed) data distribution across different robot systems. In this study, we propose a novel approach called Knowledge-Based Clustering Federated Learning (KCFed) to address both the few-shot problem in robot fault diagnosis and the Non-IID problem in Federated Learning. KCFed incorporates Federated Learning principles to ensure privacy protection during the model training process. Additionally, by leveraging the Knowledge-based Clustering Mechanism (KCM) and Knowledge Accumulation Mechanism (KAM), KCFed aims to improve the performance of fault diagnosis models by clustering similar tasks and accumulating useful prior information. To evaluate the effectiveness of KCFed, we conducted experiments using data collected from industrial robots performing 12 different tasks, resulting in a diverse set of 48 states. The experimental results demonstrate the promising performance of KCFed in improving fault diagnosis accuracy while preserving privacy in a federated learning setting.

Task attention-based multimodal fusion and curriculum residual learning for context generalization in robotic assembly

Task attention-based multimodal fusion and curriculum residual learning for context generalization in robotic assembly

Investigating Collaborative Robotic Assembly: A Case Study of the FANUC CRX-10 iA/L in Industrial Automation at i-Labs

This study examines the practicality and limitations of using a FANUC CRX-10 iA/l collaborative robot to assemble a product component, highlighting the trade-offs between increased robotization and reduced manual intervention. Through a detailed case study in the i-Labs laboratory, critical factors affecting precision assembly such as station layout, tooling design and robot programming are discussed. The findings highlight the benefits of robots for nonstop operation, freeing up human operators for higher value tasks despite longer cycle times. In addition, the paper advocates further research into reliable gripping of small components, a current challenge for robotics. The work contributes to open science by sharing partial results and methods that could inform future problem solving in robotic assembly.

Multi-objective simulated annealing algorithm for robotic mixed-model two-sided assembly line balancing with setup times and multiple constraints

Robotic mixed-model two-sided assembly lines (RMTALs) have become increasingly prevalent in manufacturing industries for productivity enhancement. However, only limited attention has been paid to the RMTAL balancing problems that contain both setup times and multiple constraints such as positional constraints, zoning constraints, and synchronism constraints. Given the coexistence of these aspects in real-world scenarios, this study aims to address the RMTAL balancing problem with setup times and the mentioned constraints. To formulate the problem, a mixed integer programming model is proposed to optimize cycle time and total energy consumption. To handle multiple constraints and setup times, a new method based on four-vector encoding is introduced. This method addresses the three constraints during the encoding phase using four intervals and five rules, while setup times are processed during the decoding phase. To solve the problem, a Pareto entropy-based two-mode multi-objective simulated annealing algorithm is developed. The algorithm employs variable neighborhood descent algorithm with switchable objectives to generate the initial Pareto archive. Subsequently, it selects the initial solution with minimal potential in the Pareto archive, alternating between two search modes of exploration and exploitation based on the entropy difference of Pareto solutions. Comparative experiments with four state-of-the-art algorithms on benchmark instances of varying scales demonstrate that the proposed algorithm outperforms the others in over 93% of instances for the hypervolume ratio and inverted generational distance metrics.

A Robotics Experimental Design Method Based on PDCA: A Case Study of Wall-Following Robots.

There is a lack of research that proposes a complete and interoperable robotics experimental design method to improve students' learning outcomes. Therefore, this study proposes a student-oriented method based on the plan-do-check-act (PDCA) concept to design robotics experiments. The proposed method is based on our teaching experience and multiple practical experiences of allowing students to do hands-on experiments. It consists of eight steps, mainly including experimental goals, experimental activities, robot assembly, robot control, in-class evaluation criteria, and after-class report requirements. The after-class report requirements designed in the proposed method can help students improve their report-writing abilities. A wall-following robotics experiment designed using the PDCA method is proposed, and some students' learning outcomes and after-class reports in this experiment are presented to illustrate the effectiveness of the proposed method. This experiment also helps students to understand the fundamental application of multi-sensor fusion technology in designing an autonomous mobile robot. We can see that the proposed reference examples allow students to quickly assemble two-wheeled mobile robots with four different sensors and to design programs to control these assembled robots. In addition, the proposed in-class evaluation criteria stimulate students' creativity in assembling different wall-following robots or designing different programs to achieve this experiment. We present the learning outcomes of three stages of the wall-following robotics experiment. Three groups of 42, 37, and 44 students participated in the experiment in these three stages, respectively. The ratios of the time required for the robots designed by students to complete the wall-following experiment, less than that of the teaching example, are 3/42 = 7.14%, 26/37 = 70.27%, and 44/44 = 100%, respectively. From the comparison of learning outcomes in the three stages, it can be seen that the proposed PDCA-based design method can indeed improve students' learning outcomes and stimulate their active learning and creativity.

Dynamic Affect-Based Motion Planning of a Humanoid Robot for Human-Robot Collaborative Assembly in Manufacturing

The objective was to investigate the impacts of the robot’s dynamic affective expressions in task-related scenarios on human–robot collaboration (HRC) and performance in human–robot collaborative assembly tasks in flexible manufacturing. A human–robot hybrid cell was developed to facilitate a human co-worker and a robot to collaborate to assemble a few parts into a final product. The collaborative robot was a humanoid manufacturing robot with the ability to display its affective states due to changes in task scenarios on its face. The assembly task was divided into several subtasks, and based on an optimization strategy, the subtasks were optimally allocated to the human and the robot. A computational model of the robot’s affective states was derived inspired by that of humans following the biomimetic approach, and an affect-based motion planning strategy for the robot was proposed to enable the robot to adjust its motions and behaviors with task situations and communicate (inform) the situations to the human co-worker through affective expressions. The HRC and the assembly performance for the affect-based motion planning were experimentally evaluated based on a comprehensive evaluation scheme and were compared with two alternative conditions: (i) motion planning that did not display affective states, and (ii) motion planning that displayed text messages instead of displaying affective states to communicate the situations to the human co-worker. The results clearly showed that the dynamic affect-based motion planning produced significantly better HRC and assembly performance than that produced by motion planning associated with the display of no affective states or text messages. The results encouraged employing manufacturing robots with dynamic affective expressions to collaborate with humans in flexible assembly in manufacturing to improve HRC and assembly performance.

Investigation of Multi-Stage Visual Servoing in the context of autonomous assembly

This paper introduces a novel strategy, Multi-Stage Based Visual Servoing (MSBVS), to tackle the significant challenge of planning complex trajectories in robotic systems without hand–eye calibration, particularly in some assembly tasks. The MSBVS strategy decomposes a complete visual servoing process into three distinct phases, each characterized by unique functionalities. By assigning specific image features and control objectives to each phase, the strategy indirectly facilitates the planning and tracking complex trajectories. Furthermore, a novel image feature, the vanishing angle, is introduced, enhancing the precision in representing an object’s spatial position. Subsequently, a robotic vision system is established, and real-time simulations and experiments are performed to verify the efficacy of the MSBVS strategy. The results show that MSBVS efficiently plans and executes complex trajectories in robotic assembly tasks.

Robotic assembly line balancing considering the carbon footprint objective with cross-station design

Robotic assembly lines are widely applied in the manufacturing sector to produce a wide range of products because of their efficiency and multifunctionality. The robotic assembly line balancing problem (RALBP) is a combinatorial optimization problem where the decision variables are task assignment and robot allocation. However, RALBP considering carbon footprint, which is a very significant environmental concern, has scarcely been studied in the literature and a practical “cross-station” design is never mathematically formulated. In this paper, a mixed-integer programming model is proposed to optimize the two objectives according to the Pareto principle. A particle swarm algorithm (PSO) with some improvement rules is designed to solve the problem. To examine the efficiency of the algorithm, computational experiments including five medium-sized and five large-sized datasets are conducted. The results show that the efficiency of PSO is better than that of four other classic algorithms in terms of three evaluation metrics. Further, the production manager and assembly line designer can choose the appropriate production plan and upgrade the line configuration.

Robotic compliant assembly for complex-shaped composite aircraft frame based on Gaussian process considering uncertainties

Robots are finding increasing application in aircraft composite structure assembly due to their flexibility and the growing demand of aircraft manufacturers for high production rates. The contact force of the composite frame in a robotic assembly of the aircraft composite fuselage panel can hardly be controlled due to the multi-surface variable contact stiffness caused by compliance and complex shape with multiple mating surfaces. The paper proposes a robotic assembly system for the aircraft composite fuselage frame with a compliant contact force control strategy using the Gaussian process surrogate model. First, a robotic assembly system is introduced, and the global coordinate system transformation model is built. Then, a compliant force control architecture is designed to generate the desired output force. Subsequently, a Gaussian process surrogate model with uncertainties is utilized to model the complicated relationship between the robot’s output force and the normal contact force acting on the mating surface of the composite frame. Furthermore, an optimal contact force control strategy is implemented to improve the contact quality. Finally, an experiment demonstrates that the proposed methodology can ensure that the contact force on each surface is within the limit of the engineering specification and uniformly distributed, improving the quality compared to the traditional assembly process.

Design of a robot system for reorienting and assembling irregular parts

The design of a robot system for assembling numerous parts, including screws, washers, nuts, and other irregular parts, is important in manufacturing. Although a robot can flexibly manipulate various parts with the aid of vision and force sensors, some challenges remain, for example, precise orientation and precise insertion when uncertainty exists in the positioning and manipulation of irregular objects. In this work, we present a method to design the orienting strategy for positioning the irregular parts based on the constraints formed by the fixture, thus the irregular parts such as the motor can be precisely localized only with the robot squeezing actions. We then develop a peg-in-hole insertion strategy using the force constraints formed by the environment, where fewer robot actions are required to insert the peg, thus enabling efficient and precise peg-in-hole insertions. Finally, we developed a robot assembly system with three robots from Universal Robots to perform all the assembly tasks, including picking, holding, placing, and inserting. Experiments on the assembly of a belt drive unit reveal the efficiency of the proposed method.

Dynamics Simulation of a Small Satellite with a Robotic Manipulator for Additive Manufacturing

In-space manufacturing is an emerging concept that offers several benefits when compared with traditional space structures that are built on the ground and launched fully assembled. Structures manufactured in space are not limited by launch vehicle fairing volumes and do not require reinforcement to survive the ascent to orbit. Additive manufacturing is uniquely suited to this paradigm and has been proposed as a key piece of several in-space manufacturing architectures. However, the complex dynamics involved with 3D printing a structure on-orbit require additional study. Controlling a spacecraft with an attached multijointed, independently articulated robot arm is a complicated problem, and additive manufacturing is especially difficult due to the large tool paths required to complete a structure. Here, a numerical model of a free-flying small satellite with an attached robotic arm assembly is presented. Simulations for multiple 3D structures printed on-orbit and analysis of the small satellite controls required to stabilize the articulated arm are described. Multiple spacecraft control strategies are implemented, with resource utilization tracked throughout the simulation. The results demonstrate the feasibility of a small satellite platform for hosting a 3D printing robotic arm in space, and the simulation can be used to inform the design of future small satellite systems attempting complicated on-orbit manufacturing missions.

Design, Assembly and Control of a Differential/Omnidirectional Mobile Robot through Additive Manufacturing

Although additive manufacturing is a relatively new technology, it has been widely accepted by industry and academia due to the wide variety of prototypes that can be built. Furthermore, using mobile robots to carry out different tasks allows greater flexibility than using manipulator robots. In that sense, and based on those above, this article focuses on the design and assembly of a multi-configurable mobile robot that is capable of changing from a differential to an omnidirectional configuration. For this purpose, a sequential mechatronic design/control methodology was implemented to obtain an affordable platform via additive manufacturing which is easily scalable and allows the user to change from one configuration to another. As a proof of concept, this change is made manually. Fabrication, construction, and assembly processes for both structures are presented. Then, a hierarchical control law is designed. In this sense and based on Lyapunov’s method, a low-level controller is developed to control the angular speed of the wheels to a desired angular speed, and a medium-level controller controls the robot’s attitude to follow a desired Cartesian trajectory. Finally, the control strategies are implemented in both prototype configurations, and through experimental results, the theoretical analysis and the construction of the mobile robot are validated.

Spatial Visual Feedback for Robotic Arc-Welding Enforced by Inductive Machine Learning

Abstract An intelligent system for spatial visual feedback is presented, which enables the robot's autonomy for a range of robotic assembly tasks, in particular for arc welding, in an unstructured and “fixtureless” environment. The robot's autonomy is empowered by an embedded inductive inference-based machine learning module which learns a welded object's structural properties in the form of geometrical properties. In particular, the system tries to recognize line segments, using a spatial (three-dimensional) visual sensor in order to autonomously execute the objective task. The innovative result is that the recognition of the geometric primitives is done without a predefined Computer-Aided Design (CAD) model, significantly improving the system's autonomy and robustness. The system is validated on real-world welding tasks.

Balancing and sequencing of mixed-model parallel robotic assembly lines considering energy consumption

Balancing and sequencing of mixed-model parallel robotic assembly lines considering energy consumption

Recognition and Localization Method for Automotive Axle Holes in Assembly Robots

Recognition and Localization Method for Automotive Axle Holes in Assembly Robots

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.