Robotic Workcell Research Articles

Smart hardware integration with advanced robot programming technologies for efficient reconfiguration of robot workcells

The manufacturing industry is seeing an increase in demand for more custom-made, low-volume production. This type of production is rarely automated and is to a large extent still performed manually. To keep up with the competition and market demands, manufacturers will have to undertake the effort to automate such manufacturing processes. However, automating low-volume production is no small feat as the solution should be adaptable and future proof to unexpected changes in customers’ demands. In this paper, we propose a reconfigurable robot workcell aimed at automating low-volume production. The developed workcell can adapt to the changes in manufacturing processes by employing a number of passive, reconfigurable hardware elements, supported by the ROS-based, modular control software. To further facilitate and expedite the setup process, we integrated intuitive, user-friendly robot programming methods with the available hardware. The system was evaluated by implementing five production processes from different manufacturing industries.

Investigation on spot welding and roller hemming by robots

This paper deals with automatic spot welding, hemming and material handling in automotive industries. Here, hood, doors and tail gate are processed by implementation of robots. Spot welding and hemming of the panels are the two operations which decide the quality of the car body. The quality of hemming and spot welding should be highly accurate, which consumes more time if done manually. There will be quality defects if it is done manually and high rate of production cannot be achieved. The robots were selected according to the payload required and space available. In this project, all the processes were simulated using the software and the entire robot operations were also simulated in the virtual environment to check for collision, cycle time, reach and range. Finally the simulated robot work cell is implemented on the floor and the robots were programmed to do their specific tasks without any interference of humans.

Search algorithm of the assembly sequence of products by using past learning results

In the future smart factory, the production system will further head in the direction of on-demand production. Products will be assembled one by one based on different specifications of customers. In these recognitions, this paper considers a method for raising productivity of the robot work cell. Under the assumption that the dual-arm robot assembles products in the work cell where one robot is in charge of all steps of assembling the product, we propose a computational algorithm for searching the efficient assembly sequence and work assignment to the robot hands utilizing reinforcement learning. Furthermore, we intend to use past learning results to determine work plans of robots more effectively. The proposed methods can eliminate or decrease the workload of the robot teaching. In addition, they can contribute to shorten the assembly time of products by giving the efficient work plan. In this research, the basic theory for automating the work planning of actual assembled products is considered using a building block model.

A novel force and motion control strategy for robotic chamfering of gears

Accurate positioning of the workpiece in the robotic work cell is currently required to perform machining operations such as chamfering in order to obtain high quality products. However, registration and workpiece fixturing errors are inevitable and lead to uncertainty in the desired robot end-effector trajectory which will lead to poor quality of the finished product. This paper proposes a method for robotic gear chamfering that can compensate for the registration error of the workpiece while avoiding use of expensive and time-consuming metrology devices for accurately registering the gear in the robot workspace. We highlight the problems in chamfering with workpiece uncertainty when traditional contour following methods are employed. A novel chamfering trajectory based on a part identification procedure is proposed that can account for the gear registration uncertainty. A force control strategy is employed in identifying the gear center and gear root positions. Based on this identification, we employ a novel force/motion strategy that can simultaneously chamfer two edges of the adjacent gear teeth. We have conducted a number of real-time experiments with a six degree-of-freedom robot to evaluate the proposed strategy, and representative chamfering experimental results are presented and discussed.

Transformation of robotic workcells to digital twins

There is a growing demand for adaptive, off-line programmed robotic cells that can eliminate the laborious and time-consuming on-line programming work. Motivated by industrial requests, the paper proposes a design approach that transforms the already existing physical workcells to parametric digital twins. By improving twin closeness through multi-level calibration methods, an accurate digital twin of the as-built cell can be established, in which the sufficient accuracy of the off-line planned robotic operations is ensured. The approach is presented through the design of a belt grinding and polishing robotic cell for a cast aluminium workpiece in a real industrial scenario.

Robots assembling machines: learning from the World Robot Summit 2018 Assembly Challenge

ABSTRACTThe Industrial Assembly Challenge at the World Robot Summit was held in 2018 to showcase the state-of-the-art of autonomous manufacturing systems. The challenge included various tasks, such as bin picking, kitting, and assembly of standard industrial parts into 2D and 3D assemblies. Some of the tasks were only revealed at the competition itself, representing the challenge of ‘level 5’ automation, i.e. programming and setting up an autonomous assembly system in less than one day. We conducted a survey among the teams that participated in the challenge and investigated aspects such as team composition, development costs, system setups as well as the teams' strategies and approaches. An analysis of the survey results reveals that the competitors have been in two camps: those constructing conventional robotic work cells with off-the-shelf tools, and teams who mostly relied on custom-made end effectors and novel software approaches in combination with collaborative robots. While both camps performed reasonably well, the winning team chose a middle ground in between, combining the efficiency of established play-back programming with the autonomy gained by CAD-based object detection and force control for assembly operations.

Optimal automatic path planner and design for high redundancy robotic systems

PurposeIn the vast majority of the individual robot installations, the robot arm is just one piece of a complex puzzle of components, such as grippers, jigs or external axis, that together compose an industrial robotic cell. The success of such installations is very dependent not only on the selection of such components but also on the layout and design of the final robotic cell, which are the main tasks of the system integrators. Consequently, successful robot installations are often empirical tasks owing to the high number of experimental combinations that could lead to exhaustive and time-consuming testing approaches.Design/methodology/approachA newly developed optimized technique to deal with automatic planning and design of robotic systems is proposed and tested in this paper.FindingsThe application of a genetic-based algorithm achieved optimal results in short time frames and improved the design of robotic work cells. Here, the authors show that a multi-layer optimization approach, which can be validated using a robotic tool, is able to help with the design of robotic systems.Practical implicationsThe usage of the proposed approach can be valuable to industrial corporations, as it allows for improved workflows, maximization of available robotic operations and improvement of efficiency.Originality/valueTo date, robotic solutions lack flexibility to cope with the demanding industrial environments. The results presented here formalize a new flexible and modular approach, which can provide optimal solutions throughout the different stages of design and execution control of any work cell.

Specimen Temperature Detection on a Clinical Laboratory Pre-Analytic Automation Track: Implications for Direct-from-Track Total Laboratory Automation (TLA) Systems.

Clinical laboratory regulations require temperature monitoring of facilities, reagent and specimen storage, as well as temperature-dependent equipment. Real-time specimen temperature detection has not yet been integrated into total laboratory automation (TLA) solutions. An infrared (IR) pyrometer was paired with a complementary metal oxide semiconductor (CMOS) laser sensor and connected to an embedded networked personal computer (PC) to create a modular temperature detection unit for closed, moving clinical laboratory specimens. Accuracy of the detector was assessed by comparing temperature measurements to those obtained from thermocouples connected to battery-operated data loggers. The temperature detector was then installed on a pre-analytic laboratory automation system to assess specimen temperature before and after processing on an integrated thawing and mixing (T/M) robotic workcell. The IR temperature detector was able to accurately record temperature of closed, moving specimens on a pre-analytic automation system. The effectiveness of the T/M workcell was independently verified using the temperature detector. Specimen reroute on the pre-analytic automation track was identified as a potential risk for frozen specimens being inadvertently delivered to future, connected instrumentation. Automated IR temperature detection can be used to verify specimen temperature prior to instrument loading and/or sampling. Such systems could be used to prevent frozen specimens from being inadvertently loaded onto analytical instrumentation in TLA solutions.

A Data-driven Approach for General Visual Quality Control in a Robotic Workcell

Setting up computer vision quality control tasks in a robot workcell is expensive, time consuming, and often requires expert knowledge. In this work, a highly adaptable approach to mitigate this issue is introduced. First, an ontology of atomic building blocks is defined, where each block represents one computer vision algorithm. Second, these blocks are used to generate a complex graph structure. The generation follows a set of rules, which can be shown to the inexperienced worker as a list of simple questions. Afterwards, this graph can be refined. Finally, the graph is integrated into the robotic assembly sequence, and the visual quality control procedure can be executed. Two industrial use cases show the feasibility of this approach. Thus, this work leverages computer vision aided quality control tasks in robot workcells.

Process planning and scheduling for loop layout robotic workcell using virtual reality technology

The adoption of virtual reality in manufacturing system simulation had proved its effectiveness in bridging up to the gap between different areas of expertise, especially in product design and manufacturing. Virtual reality had enclosed human–machine interface by enabling the user to be immersed into the virtual environment and experience real-time interaction with the virtual objects. In this article, an implementation of virtual reality in cellular manufacturing system simulation is presented. By utilizing the features of visualization and real-time interaction of virtual reality technology, the manufacturing process of a product had been visualized while the real-time control on the product traveling path based on the user’s input was performed and the corresponding activities that related to the change of traveling path had been predicted in the virtual environment. Through the study, simulation of the manufacturing system in virtual reality showed its potential as a powerful decision support system in process planning and scheduling. Various process planning and schedules can be planned through the virtual environment, while the product traveling distance can be obtained from the developed system.

Simulation based draping of dry carbon fibre textiles with cooperating robots

Carbon fibre-reinforced plastic (CFRP) is a promising material for aircraft and other lightweight applications. To be competitive with low-cost metal based solutions highly effective and flexible production technologies are required. For this purpose production systems comprising automated fibre placement or automated tape laying technology are on the market for several years and widely spread. However, there is still a lack of automated systems capable of producing preforms efficiently and flexibly from textile semi-finished goods. Non-crimp fabrics (NCF) and weaves have to undergo considerable shear and reshaping during the layup of 3D-curved preforms in order to properly fit the 2D cut pieces to the moulds.At the Center for Lightweight Production Technology (ZLP) a digital and automated process for the easy draping of large NCF and weave cut pieces with several robots according to the previous draping simulation has been set up and tested in a robotic work cell. The details of converting the draping simulation into correct and easy to setup motions for cooperating robots and how to execute the entire process autonomously, i.e. without teaching the robots, are described. On the basis of preliminary tests the system’s capabilities on a large scale demonstrator part resembling an airplane’s rear pressure bulkhead are evaluated. An overview of the system’s architecture from simulation based planning to detecting, correct gripping, collision free autonomous transport and laydown of the cut pieces is also given.

Enabling the Electric Future of Mobility: Robotic Automation for Electric Vehicle Battery Assembly

Consumer demand for Electric Vehicles (EVs) is increasing due to improving performance and affordability. However, EV manufacturers are struggling to meet this rise in demand. A key bottleneck is supply from a nascent EV battery supply chain that is new and developing. In this paper, we propose robotic work cell design for fast and reliable assembly of EV battery modules, at scale, to reduce this demand-supply gap.

Robotic concrete surface finishing: a moldless approach to creating thermally tuned surface geometry for architectural building components using Profile-3D-Printing

This paper focuses on describing a novel hybrid concrete printing/casting process we term Profile-3D-Printing. Profile-3D-Printing is an additive/subtractive manufacturing process that combines deposition of concrete for rough layup with precision tooling for surface finishing of architectural building components commonly found in the architectural precast industry. Our research team from Architecture, the Robotics Institute, and Material Science invented this novel hybrid manufacturing process for robotically printing architectural facade panels with complex surface geometries. This effort was motivated by previously validated research focused on calibrating the thermal exchange rate of vertical surface geometries for the purpose of improving both the esthetic and thermodynamic performance of passive heating and cooling systems in buildings. Our hybrid approach to concrete 3D printing is unique because it combines high-volume material deposition, multi-resolution surface finishing, and the ability for high customization without significant increases in production time. Our project will significantly advance systems integration between energy-based building performance design and advance additive manufacturing, enabling precast concrete suppliers to design and manufacture innovative architectural products with added value for end users in energy savings. This project serves as a template for other industries to adopt hybrid additive manufacturing systems to transform traditionally mold-based approaches. The goal of our project is to develop and test robotic 3D-printing systems that enable customization for high-resolution parts. Hybrid additive manufacturing (Hybrid-AM) enables automated tooling of soft and phase-changing materials to achieve fine feature resolution and finish quality independent of print nozzle size. Currently, speed of printing, control of finish quality, and flexibility of application are three of the most significant barriers for industries seeking to adopt additive manufacturing for part production. This project highlights how Hybrid-AM techniques, built on robotic work cells, address these challenges to enable customization of value-added products.

Robotic abrasive wire cutting of polymerized styrene formwork systems for cost-effective realization of topology-optimized concrete structures

This paper presents a new method for industrial manufacturing of topology-optimized, ultrahigh performance concrete structures using robotic abrasive wire cutting of polymerized styrene formwork systems. Topology optimization is a well-established procedure within disciplines of aeronautic, automotive and naval engineering. However, while recent developments have highlighted a significant potential for structural innovation and reduction of material consumption in topology-optimizing concrete structures, an effective method for large-scale manufacturing of such structures remains to be found. We argue that the prohibitive factor for large-scale adoption is the non-availability of mechanical processes capable of performing high-speed, high-volume manufacturing of advanced non-standard formwork and, consequently that this inhibition can be overcome through integrating ruled surface rationalization and robotically controlled abrasive wire cutting of three-dimensional formwork systems in polymerized styrene foams. The viability of the proposed method is demonstrated through the production of a 21 m spatial concrete structure, using abrasive wire cutting of EPS formwork via a containerized robotic work cell with an ABB IRB 6700 industrial manipulator, extended with external rotary axis.

Examining Workcell Kinematic Chains to Identify Sources of Positioning Degradation

Automated industrial workcells are becoming increasingly complex and varied due to greater accessibility of advanced robotic and sensing technologies. Degradation monitoring and diagnostics must advance to reduce the impact of increased system complexity on troubleshooting faults and failures and to optimize system operations. A new methodology is being developed for the design and implementation of monitoring kinematic chains commonly found in robot workcells. This method will enable the identification of degraded components which contribute to relative positioning accuracy error between moving objects, tools, devices, and other components. The proposed methodology is being developed and tested on a six degree of freedom industrial robot arm workcell use case developed at the National Institute of Standards and Technology (NIST). Industrial robot users and integrators can use this method to examine the kinematic chains within their workcells and design a key position monitoring implementation. With the added key position monitoring, degradations can be identified at a designed resolution allowing for enhanced maintenance planning and production control. The methodology will be extended to other manufacturing workcells in the future.

Simultaneous path placement and trajectory planning optimization for a redundant coordinated robotic workcell

An optimization method using Particle Swarm Optimization (PSO) is introduced in order to simultaneously optimize the trajectory planning and placement of a given path in a redundant coordinated robotic workcell. The workcell consists of a 6 Degree Of Freedom (DOF) serial manipulator, a 6 DOF parallel manipulator and a rotary table mounted on the parallel manipulator. Since the workcell has 13 DOF, one has to solve the kinematic redundancy of the workcell. The solution will be obtained considering the singularities of the serial manipulator and the workspace boundaries of all manipulators. The algorithm to obtain the optimum path placement is explained through a simple example and the result for a helix path around the workpiece is represented.

Human-Aware Robotic Assistant for Collaborative Assembly: Integrating Human Motion Prediction With Planning in Time

Introducing mobile robots into the collaborative assembly process poses unique challenges for ensuring efficient and safe human–robot interaction. Current human–robot work cells require the robot to cease operating completely whenever a human enters a shared region of the given cell, and the robots do not explicitly model or adapt to the behavior of the human. In this work, we present a human-aware robotic system with single-axis mobility that incorporates both predictions of human motion and planning in time to execute efficient and safe motions during automotive final assembly. We evaluate our system in simulation against three alternative methods, including a baseline approach emulating the behavior of standard safety systems in factories today. We also assess the system within a factory test environment. Through both live demonstration and results from simulated experiments, we show that our approach produces statistically significant improvements in quantitative measures of safety and fluency of interaction.

A Robotic Lift Assister: A Smart Companion for Heavy Payload Transport and Manipulation in Automotive Assembly

Although as robot involvement in manufacturing for the transportation industry has increased dramatically in recent years [1], over 60% of automotive assembly tasks are still accomplished manually by human workers. The automotive assembly line presents numerous challenges, such as significant variability in tools and parts, flexible tasks, and unstructured and dynamic environments that preclude direct automation via traditional fenced robotic work cells. While human-robot collaboration offers a technically viable means to integrate robotic systems into automotive assembly [2]-[4], the lack of suitable robotic systems remains a challenge.

A model-based scooping grasp for the autonomous picking of unknown objects with a two-fingered gripper

Grasping objects used in daily activities is not an easy task for a robot: the diversity of shapes and volumes of objects renders specific grasping methods inefficient. In this paper, we propose a novel model-based scooping grasp for the picking of thin objects lying on a flat surface, which are typically elusive to common grippers and grasping strategies. A robotic work cell composed of a serial arm, a commercially available gripper and a 3D camera overlooking the workspace is used to demonstrate and test the algorithm. Since a commercial gripper is used, the robot is capable of grasping a large variety of objects, in addition to the targeted thin objects. An experiment based on a test set of 80 objects results in an overall grasp success rate of 84%, which demonstrates the potential of the novel scooping grasp to extend the capabilities of existing grippers.

A Collaborative Framework for Robotic Task Specification

Implementation of automated robotic solutions for complex tasks currently faces a few major hurdles. For instance, lack of effective sensing and task variability – especially in high-mix, low-volume processes – creates too much uncertainty to reliably hard-code a robotic work cell. Current collaborative frameworks generally focus on integrating the sensing required for a physically collaborative implementation. While this paradigm has proven effective for mitigating uncertainty by mixing human cognitive function and fine motor skills with robotic strength and repeatability, there are many instances where physical interaction is impractical, as human reasoning and task knowledge are still needed. The proposed framework consists of key modules such as a path planner, path simulator, and result simulator. An integrated user interface facilitates the operator to interact with these modules and edit the path plan before ultimately approving the task for automatic execution by a manipulator that need not be collaborative. The software modules are integrated using the Robotic Operating System. Application of the collaborative framework is illustrated for a pressure washing task in a remanufacturing environment that requires one-off path planning for each part. The framework can also be applied to various other tasks, such as spray-painting, sandblasting, deburring, grinding, and shot peening.