| 1. |
- Ji, Qinglei, 1993-
(författare)
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Learning-based Control for 4D Printing and Soft Robotics
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Exploiting novel sensors and actuators made of flexible and smart materials becomes a new trend in robotics research. The studies on the design, production, and control of the new type of robots motivate the research fields of soft robots and 4D printed robots. 3D Printing (3DP) is an additive manufacturing technology that is widely used in printing flexible materials to fabricate soft robots. 4D Printing (4DP) combines 3DP technologies with smart materials to produce transformable devices. 4DP first prints structures with specifically designed responsive materials. When external stimuli such as temperature, voltage, or magnetic field are applied to the printed structure, it changes shape in a programmable way. The shape morphing property of 4DP makes it a novel approach to the actuators of robots.The employment of these special materials empowers these new robots with better compliance and adaptability to the working environment. However, compared with the rigid counterparts, they also have complex dynamic properties such as substantial non-linearity and time-variance. These factors make the precise modeling and robust control of these new robots challenging and thus hinder their potential applications. Focusing on soft robotic systems enabled by 3DP and 4DP approaches, this dissertation studies both traditional and Machine Learning (ML)-based approaches to the modeling, perception, and control of soft, non-linear, and time-variant robotic systems. The main contributions of this dissertation are:The scheme of Closed-Loop (CL) controlled 4DP (CL4DP) using temperature stimulated Shape Memory Polymer (SMP) is designed and validated numerically and experimentally. The feedback control system increases the precision and robustness of the shape morphing process of 4D printed SMP. Applications of CL4DP are explored.Data-driven model identification methods are applied to learn the dynamic model of the shape morphing process of CL4DP and the learned model has good quality to support model-based control design. Model-free and adaptive Reinforcement Learning (RL) controllers are developed to deal with the non-linearity and time variance of 4D printed actuators. To improve the stability and quick adaptability, a concise basis function set is selected instead of blindly using Deep Neural Networks (DNNs).A quadruped robot enabled by soft actuators and its simulation model are developed. The computation efficiency and model accuracy of the simulator are studied and optimized by comparing different simulation methods such as Finite Element Method (FEM) and lumped parameter method.The optimal walking gait pattern of a soft-legged quadruped robot is found by grid parameter search and RL with a physics based simulation model. To speed up the RL training process, modeling tricks are used to reduce the simulation time of the model and curriculum learning is used to reduce the learning time.A soft sensor made by printable conductive materials and 3DP is designed and optimally calibrated to estimate the shape of a pneumatically driven soft actuator. The geometry of the soft sensor is optimally designed for the best linearity, hysteresis and drift properties. The online estimation is based on a linear regression model learned from experimental data.A pneumatically driven soft gripper is developed by 3DP, the printable soft sensor, and pole-placement control methods. The operation of the gripper does not require an external image feedback system to measure its shape, which is estimated by the integrated soft sensor. The position feedback by the soft sensor and the controller by the pole-placement method enable the soft gripper to perform complex tasks with high precision.
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| 2. |
- Alhusin Alkhdur, Abdullah, 1980-
(författare)
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Toward a Sustainable Human-Robot Collaborative Production Environment
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This PhD study aimed to address the sustainability issues of the robotic systems from the environmental and social aspects. During the research, three approaches were developed: the first one an online programming-free model-driven system that utilises web-based distributed human-robot collaboration architecture to perform distant assembly operations. It uses a robot-mounted camera to capture the silhouettes of the components from different angles. Then the system analyses those silhouettes and constructs the corresponding 3D models.Using the 3D models together with the model of a robotic assembly cell, the system guides a distant human operator to assemble the real components in the actual robotic cell. To satisfy the safety aspect of the human-robot collaboration, a second approach has been developed for effective online collision avoidance in an augmented environment, where virtual three-dimensional (3D) models of robots and real images of human operators from depth cameras are used for monitoring and collision detection. A prototype system is developed and linked to industrial robot controllers for adaptive robot control, without the need of programming by the operators. The result of collision detection reveals four safety strategies: the system can alert an operator, stop a robot, move away the robot, or modify the robot’s trajectory away from an approaching operator. These strategies can be activated based on the operator’s location with respect to the robot. The case study of the research further discusses the possibility of implementing the developed method in realistic applications, for example, collaboration between robots and humans in an assembly line.To tackle the energy aspect of the sustainability for the human-robot production environment, a third approach has been developed which aims to minimise the robot energy consumption during assembly. Given a trajectory and based on the inverse kinematics and dynamics of a robot, a set of attainable configurations for the robot can be determined, perused by calculating the suitable forces and torques on the joints and links of the robot. The energy consumption is then calculated for each configuration and based on the assigned trajectory. The ones with the lowest energy consumption are selected.
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| 3. |
- de Giorgio, Andrea, Dr Eng. 1987-
(författare)
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Introducing a procedural knowledge model for enhancing industrial process adaptiveness
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Industrial processes are mainly based on procedural knowledge that must be continually elicited from experienced operators and learned by novice operators. In the context of Industry 4.0, machines already play a key role in knowledge transfer; however, new models and methods based on the artificial intelligence advances of the past few years need to be developed and applied. The future of human-machine collaboration is not limited to physical applications, but it has the potential to harness both the strength of human skills, experience and the computational power provided by the surrounding machines for truly adaptive industrial processes. The winning recipe is a balance between letting humans exploit their inherent experience and letting machines integrate the missing skills to preserve production standards. This work introduces a procedural knowledge model to be used for the design of industrial and scientific adaptive processes and it paves the way to transforming human-machine collaboration into an efficient solution to make industrial and scientific processes resilient to a constantly changing world.
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| 4. |
- Holm, Magnus
(författare)
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Adaptive Decision Support for Shop-floor Operators using Function Blocks
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In manual and semi-automation production systems, flexibility and adaptability are affected by the shop-floor operators’ skills, abilities and knowledge. Such dependencies highlight the vital importance of developing and utilising the knowledge, achievements and abilities of the operators working with production on the shop-floor. Teamwork, including both novice and highly experienced shop-floor operators, in a production environment with a high level of automation, is essential already today and is predicted to increase, when the complexity and demands of future production systems intensify. This trend is confirmed in both the research literature and by specialists within industry.The key to future competitiveness and effectiveness of the manufacturing industry is the shop-floor operators who handle the production systems. In addition, the future information intensive working environment, with its increasing complexity and less time available for decision-making, demands adaptive decision support and adaptive control systems that facilitate collaborative work on the shop-floor. It is therefore important to emphasise how decisions are supported in the time-limited working environment of the shop-floor, because this has a large impact on production output and quality and is vital to the success of the company. Consequently, this dissertation presents a framework for an adaptive decision support system that concentrates on shop-floor operators, in order to enhance their development and future contribution to leading edge production systems.The overall aim of the research presented is to define a framework for an Adaptive Decision Support System, to address the scope and demands of the future shop-floor, as indicated in the research literature, and confirm its relevance, as well as further elaborate it on the basis of interviews with production managers and HR specialistsThe research presented uses the design science research process. In parallel, decision support systems and the industrial shop-floor have been studied in the research literature and the current state of industrial practice has been assessed. These areas together form the basis for the research on adaptive decision support for shop-floor operators. A framework enabling adaptive decision support and adaptive system control, based on event-driven function block technology and Augmented Reality technology, is formulated.The gap of research on decision support for shop-floor operators, indicated in the research literature is addressed by the research preformed. Adaptive and dynamic decision support and system control able to process vast amounts of information in real time demonstrates utility for shop-floor operators. The research presenting the Adaptive Decision Support System has demonstrated its utility for shop-floor systems and production operatives in two extensive studies using demonstrators based on real-life production environments.A methodology, the ‘User group’, has been formulated for research collaboration and bi-directional knowledge transfer between academia and the industrial partners. It provides tools that enable cooperation between the experienced research partner and the novices, despite their different levels of engagement in the same project, without dividing them into separate groups. The ‘user group’ case study presented describes how both the inexperienced and the research mature companies gain new knowledge and engage in ongoing research. By doing so, the industrial project partners have extensively supported the research presented and will subsequently be the expected beneficiaries.
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| 5. |
- Liu, Sichao, 1991-
(författare)
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Multimodal Human-Robot Collaboration in Assembly
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Human-robot collaboration (HRC) envisioned for factories of the future would require close physical collaboration between humans and robots in safe and shared working environments with enhanced efficiency and flexibility. The PhD study aims for multimodal human-robot collaboration in assembly. For this purpose, various modalities controlled by high-level human commands are adopted to facilitate multimodal robot control in assembly and to support efficient HRC. Voice commands, as a commonly used communication channel, are firstly considered and adopted to control robots. Also, hand gestures work as nonverbal commands that often accompany voice instructions, and are used for robot control, specifically for gripper control in robotic assembly. Algorithms are developed to train and identify the commands so that the voice and hand gesture instructions are associated with valid robot control commands at the controller level. A sensorless haptics modality is developed to allow human operators to haptically control robots without using any external sensors. Within such context, an accurate dynamic model of the robot (within both the pre-sliding and sliding regimes) and an adaptive admittance observer are combined for reliable haptic robot control. In parallel, brainwaves work as an emerging communication modality and are used for adaptive robot control during seamless assembly, especially in noisy environments with unreliable voice recognition or when an operator is occupied with other tasks and unable to make gestures. Deep learning is explored to develop a robust brainwave classification system for high-accuracy robot control, and the brainwaves act as macro commands to trigger pre-defined function blocks that in turn provide micro control for robots in collaborative assembly. Brainwaves offer multimodal support to HRC assembly, as an alternative to haptics, auditory and gesture commands. Next, a multimodal data-driven control approach to HRC assembly assisted by event-driven function blocks is explored to facilitate collaborative assembly and adaptive robot control. The proposed approaches and system design are analysed and validated through experiments of a partial car engine assembly. Finally, conclusions and future directions are given.
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| 6. |
- Wang, Qiuchen, et al.
(författare)
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Multi-actor perspectives on human robotic collaboration implementation in the heavy automotive manufacturing industry : A Swedish case study
- 2023
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Ingår i: Technology in society. - : Elsevier Ltd. - 0160-791X .- 1879-3274. ; 72
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Tidskriftsartikel (refereegranskat)abstract
- Implementing an industrial collaborative robot for Human-Robot Collaboration (HRC) in the automotive manufacturing industry is an emerging technology-driven solution aiming to increase production efficiency and reduce the human operator's ergonomic load. Successful implementation of innovative technology depends on technical feasibility and on the acceptance by the affected actors. Many studies exist that focus on the technical aspects of HRC, however, research that focuses on understanding the multi-actor concerns of HRC adoption is rare. In an effort to support the successful adoption of industrial collaborative robots, this study aims to understand the concerns of the various actors who work at the operational and management levels influencing future HRC adoption in the heavy automotive manufacturing industry. A literature review was conducted to understand the HRC implementation challenges and the methods used to investigate multi-actor involvement in advance of, and during, the implementation stage. After reviewing existing studies, the actor analysis method was selected to present the actors' perceptions using the action, factor, and goal (AFG) list to understand different actors’ opinions of HRC adoption, using a Swedish heavy vehicle manufacturing company case study. The case study results showed that the actors from the same organization had different concerns but mostly positive expectations for future HRC adoption. The actors’ perception map shows the details pertaining to Actions, Concerns, and Goals as well as the logical flow between these elements in regards to HRC future adoption. The involvement of different actor groups prior to new solution implementation contributes to a holistic view of potential implementation influences and challenges in the organization. Actor analysis can provide a set of analysis processes that comply with multi-actor perceptions to understand future adoption challenges from different perspectives. In the next step, safety-related issues and under-development standardization are the key challenges of HRC implementation.
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| 7. |
- Adamson, Göran, 1958-
(författare)
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A Novel Method for Adaptive Control of Manufacturing Equipment in Cloud Environments
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The ability to adaptively control manufacturing equipment, both in local and distributed environments, is becoming increasingly more important for many manufacturing companies.One important reason for this is that manufacturing companies are facing increasing levels of changes, variations and uncertainty, caused by both internal and external factors, which can negatively impact their performance. Frequently changing consumer requirements and market demands usually lead to variations in manufacturing quantities, product design and shorter product life-cycles. Variations in manufacturing capability and functionality, such as equipment breakdowns, missing/worn/broken tools and delays, also contribute to a high level of uncertainty. The result is unpredictable manufacturing system performance, with an increased number of unforeseen events occurring in these systems. Events which are difficult for traditional planning and control systems to satisfactorily manage.For manufacturing scenarios such as these, the use of real-time manufacturing information and intelligence is necessary to enable manufacturing activities to be performed according to actual manufacturing conditions and requirements, and not according to a pre-determined process plan. Therefore, there is a need for an event-driven control approach to facilitate adaptive decision-making and dynamic control capabilities.Another reason driving the move for adaptive control of manufacturing equipment is the trend of increasing globalization, which forces manufacturing industry to focus on more cost-effective manufacturing systems and collaboration within global supply chains and manufacturing networks. Cloud Manufacturing is evolving as a new manufacturing paradigm to match this trend, enabling the mutually advantageous sharing of resources, knowledge and information between distributed companies and manufacturing units. One of the crucial objectives for Cloud Manufacturing is the coordinated planning, control and execution of discrete manufacturing operations in collaborative and networked environments. Therefore, there is also a need that such an event-driven control approach supports the control of distributed manufacturing equipment.The aim of this research study is to define and verify a novel and comprehensive method for adaptive control of manufacturing equipment in cloud environments.The presented research follows the Design Science Research methodology. From a review of research literature, problems regarding adaptive manufacturing equipment control have been identified. A control approach, building on a structure of event-driven Manufacturing Feature Function Blocks, supported by an Information Framework, has been formulated. The Function Block structure is constructed to generate real-time control instructions, triggered by events from the manufacturing environment. The Information Framework uses the concept of Ontologies and The Semantic Web to enable description and matching of manufacturing resource capabilities and manufacturing task requests in distributed environments, e.g. within Cloud Manufacturing. The suggested control approach has been designed and instantiated, implemented as prototype systems for both local and distributed manufacturing scenarios, in both real and virtual applications. In these systems, event-driven Assembly Feature Function Blocks for adaptive control of robotic assembly tasks have been used to demonstrate the applicability of the control approach. The utility and performance of these prototype systems have been tested, verified and evaluated for different assembly scenarios.The proposed control approach has many promising characteristics for use within both local and distributed environments, such as cloud environments. The biggest advantage compared to traditional control is that the required control is created at run-time according to actual manufacturing conditions.The biggest obstacle for being applicable to its full extent is manufacturing equipment controlled by proprietary control systems, with native control languages. To take the full advantage of the IEC Function Block control approach, controllers which can interface, interpret and execute these Function Blocks directly, are necessary.
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| 8. |
- Danielsson, Oscar, 1982-
(författare)
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Augmented reality smart glasses as assembly operator support : A framework for enabling industrial integration
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Manufacturing industry is seeing vast improvements in productivity and flexibility as the fourth industrial revolution continues to unfold. However, despite improved computation and automation capacity, there is still a role for operators to play in Industry 4.0, mirrored in the concept of Operator 4.0. Improved productivity and a more competitive global market have contributed to increasing manufacturing complexity, putting greater cognitive demands on operators. A technology that can support operators in this new manufacturing landscape is augmented reality (AR), specifically, headworn AR smart glasses (ARSG). With ARSG, operators can receive information interactively in real time, hands free and overlying their natural environment. ARSG are an emerging technology that is becoming more mature; there are early examples of their use in manufacturing industry, but ARSG are not yet widespread.Because ARSG are an emerging technology, there is still uncertainty as to how ARSG can be integrated, like other production equipment, in assembly lines. When current literature was analyzed, it was found that there is a need for more knowledge particularly from the manufacturing engineering perspective of practically integrating ARSG on the industrial shop floor in the long term. This thesis therefore aims to create a framework that supports industry in making strategic and practical decisions about integrating ARSG in production as an assembly operator support tool. The framework is designed to guide industrial decision makers in evaluating the suitability of ARSG as support in an assembly station and, further to offer specific recommendations and rationales for actions to take. It has two main perspectives: the operators using the ARSG and the manufacturing engineers conducting the integration into the production systems. The framework was iteratively developed, using design science combining qualitative and quantitative methods into mixed methods. Three research questions were developed and answered as steps toward creating and evaluating the framework.The results of the thesis show that ARSG integration should be considered in relation to the investment cost and efficiency gains. For instance, ARSG requires the digitalization of assembly instructions before it can be feasible. If operators are mostly stationary when working and have little need for spatial guidance, there might be cheaper alternatives to ARSG, such as monitors or pick-by-light, that merit prior consideration. The framework has been developed and tested iteratively with industrial experts from different fields, with the initial strawman design based on three literature reviews and previous research.
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| 9. |
- Gustavsson, Patrik, 1988-
(författare)
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Virtual Reality Platform for Design and Evaluation of the Interaction in Human-Robot Collaborative Tasks in Assembly Manufacturing
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Industry is on the threshold of the fourth industrial revolution where smart factories area necessity to meet customer demands for increasing volumes of individualized products. Within the smart factory, cyber-physical production systems are becoming important to deal with changing production. Human-robot collaboration is an example of a cyber-physical system in which humans and robots share a workspace. By introducing robots and humans into the same working cell, the two can collaborate by allowing the robot to deal with heavy lifting, repetitive, and high accuracy tasks, while the human focuses on tasks that need intelligence, flexibility, and adaptability. There are few such collaborative applications in industry today. In the implementations that actually exist, the robots are mainly working side-by-side with humans rather than truly collaborating. Three main factors that limit the widespread application of human-robot collaboration can be identified: lack of knowledge regarding suitable human-robot collaboration tasks, lack of knowledge regarding efficient communication technologies for enabling interaction between humans and robots when carrying out tasks, and lack of efficient ways to safely analyze and evaluate collaborative tasks.The overall aim of this thesis is to address these problems and facilitate and improve interaction between humans and robots, with a special focus on assembly manufacturing tasks. To fulfill this aim, an assembly workstation for human-robot collaboration has been developed and implemented both physically and virtually. A virtual reality platform called ViCoR has been developed that can be used to investigate, evaluate, and analyze the interaction between humans and robots and thereby facilitate the implementation of new human-robot collaboration cells. The workstation developed has also been used for data collection and experiments during the thesis work, and used to extract knowledge of how the interaction between human and robot can be improved.
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| 10. |
- Liu, Hongyi, 1990-
(författare)
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Context-aware human-robot collaboration in assembly
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The PhD study is aiming to increase the accuracy and efficiency of human-robot collaborative (HRC) assembly systems. To achieve this goal, four main directions are investigated in this research. The first direction is HRC assembly context recognition, which focuses on the identification and recognition of relevant assembly context in the assembly environment. Valuable knowledge can be captured through the assembly context to increase assembly efficiency. The definition of assembly context is given, and recognition algorithms are designed. The second direction is multimodal robot control. Instead of coding, the possibility to control robots with multiple modalities is explored. The algorithm to increase the recognition accuracy of multimodal robot control is developed. The third direction is human motion prediction. Robots can be supported to anticipate and prepare for the human operator' next move with an accurate and timely prediction of the human operator's motion. Two different approaches are explored to predict human motions during the assembly operation. The efficiency of HRC assembly systems can be further boosted. The last direction of the study is remote HRC. A special scenario of HRC is explored where a human operator collaborates with a robot remotely. The scenario is investigated, a possible solution is also provided. Along with the four directions, key algorithms, system designs, and experiments are analysed. Furthermore, the advantages, drawbacks, and future directions of the approaches are given.
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