SwePub - sökning: WFRF:(Liu Sichao)

Numrering	Referens	Omslagsbild	Hitta
1.	Liu, Sichao, et al. (författare) An ‘Internet of Things’ enabled dynamic optimization method for smart vehicles and logistics tasks 2019 Ingår i: Journal of Cleaner Production. - : Elsevier. - 0959-6526 .- 1879-1786. ; 215, s. 806-820 Tidskriftsartikel (refereegranskat)abstract Centralized and one-way logistics services and the lack of real-time information of logistics resources are common in the logistics industry. This has resulted in the increased logistics cost, energy consumption, logistics resources consumption, and the decreased loading rate. Therefore, it is difficult to achieve efficient, sustainable, and green logistics services with dramatically increasing logistics demands. To deal with such challenges, a real-time information-driven dynamic optimization strategy for smart vehicles and logistics tasks towards green logistics is proposed. Firstly, an ‘Internet of Things’-enabled real-time status sensing model of logistics vehicles is developed. It enables the vehicles to obtain and transmit real-time information to the dynamic distribution center, which manages value-added logistics information. Then, such information can be shared among logistics companies. A dynamic optimization method for smart vehicles and logistics tasks is developed to optimize logistics resources, and achieve a sustainable balance between economic, environmental, and social objectives. Finally, a case study is carried out to demonstrate the effectiveness of the proposed optimization method. The results show that it contributes to reducing logistics cost and fuel consumption, improving vehicles’ utilization rate, and achieving real-time logistics services with high efficiency.
2.	Wang, Lihui, et al. (författare) Overview of Human-Robot Collaboration in Manufacturing 2020 Ingår i: Proceedings of 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing. - Cham : Springer. ; , s. 15-58 Konferensbidrag (refereegranskat)abstract Human-robot collaboration (HRC) in the manufacturing context aims to realise a shared workspace where humans can work side by side with robots in close proximity. In human-robot collaborative manufacturing, robots are required to adapt to human behaviours by dynamically changing their pre-planned tasks. However, the robots used today controlled by rigid native codes can no longer support effective human-robot collaboration. To address such challenges, programming-free and multimodal communication and control methods have been actively explored to facilitate the robust human-robot collaborative manufacturing. They can be applied as the solutions to the needs of the increased flexibility and adaptability, as well as higher effort on the conventional (re)programing of robots. These high-level multimodal commands include gesture and posture recognition, voice processing and sensorless haptic interaction for intuitive HRC in local and remote collaboration. Within the context, this paper presents an overview of HRC in manufacturing. Future research directions are also highlighted.
3.	Zhang, Yingfeng, et al. (författare) Smart box-enabled product–service system for cloud logistics 2016 Ingår i: International Journal of Production Research. - : Taylor & Francis. - 0020-7543 .- 1366-588X. ; 54:22, s. 6693-6706 Tidskriftsartikel (refereegranskat)abstract Modern logistics takes significant progress and rapid developments with the prosperity of E-commerce, particularly in China. Typical challenges that logistics industry is facing now are composed by a lack of sharing, standard, cost-effective and environmental package and efficient optimisation method for logistics tasks distribution. As a result, it is difficult to implement green, sustainable logistics services. Three important technologies, Physical Internet (PI), product–service system (PSS) and cloud computing (CC), are adopted and developed to address the above issues. PI is extended to design a world-standard green recyclable smart box that is used to encapsulate goods. Smart box-enabled PSS is constructed to provide an innovative sustainable green logistics service, and high-quality packaging, as well as reduce logistics cost and environmental pollution. A real-time information-driven logistics tasks optimisation method is constructed by designing a cloud logistics platform based on CC. On this platform, a hierarchical tree-structure network for customer orders (COs) is built up to achieve the order-box matching of function. Then, a distance clustering analysis algorithm is presented to group and form the optimal clustering results for all COs, and a real-time information-driven optimisation method for logistics orders is proposed to minimise the unused volume of containers. Finally, a case study is simulated to demonstrate the efficiency and feasibility of proposed cloud logistics optimisation method. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
4.	Zhang, Y., et al. (författare) The ‘Internet of Things’ enabled real-time scheduling for remanufacturing of automobile engines 2018 Ingår i: Journal of Cleaner Production. - : Elsevier. - 0959-6526 .- 1879-1786. ; 185, s. 562-575 Tidskriftsartikel (refereegranskat)abstract Typical challenges that managers of remanufacturing face are composed of the lack of timely, accurate, and consistent information of remanufacturing resources. Therefore, it is difficult to implement real-time production scheduling for the shop floor. To address this problem, the authors applied the concept of the ‘Internet of Things’ to the remanufacturing of automobile engines to form an Internet of Manufacturing Things environment. Under the Internet of Manufacturing Things, an identification technology for disassembled engine parts was designed, and the real-time status of the remanufacturable resources can be monitored. Based on the captured remanufacturing information, a real-time production scheduling method was developed, and a mathematical model was developed to achieve cost reduction, dynamic management of remanufacturable resources, and energy consumption decrease. To obtain an optimal solution, a Pareto-based optimization method was used. Finally, a case study was performed to analyze the effectivity of the proposed method. The results showed that the remanufacturing cost and energy consumption were reduced by 34% and 34% respectively, and the worker load rate was more balanced. These improvements can contribute to more sustainable development and greener production within the remanufacturing industry, especially for remanufacturing of automobile engines.
5.	Abbott, Andrew, et al. (författare) Ionic liquids at interfaces : general discussion 2018 Ingår i: Faraday discussions. - : Royal Society of Chemistry (RSC). - 1359-6640 .- 1364-5498. ; 206, s. 549-586 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
6.	Flores-García, Erik, et al. (författare) Digital Twin-Based Services for Smart Production Logistics 2021 Konferensbidrag (refereegranskat)abstract Digital Twin (DT)-based services including Industrial Internet of Things (IIoT) are essential for achieving the vision of Smart Production Logistics and enhancing manufacturing competitiveness. DT-based services combining IIoT provide real-time location of materials and optimization of resources for addressing mass customization and fluctuating market demand. However, literature applying IIoT and achieving DT-based services in Smart Production Logistics (SPL) is scarce. Accordingly, the purpose of this study is to analyze the combined use of DT-based services and IIoT in SPL. We propose a framework combining DT-based services and IIoT for the real-time location and optimization of material handling. The study draws results from an SPL demonstrator based on a case in the automotive industry applying the proposed framework. The results show improvement in the delivery, makespan, and distance travelled during material handling. The study provides critical insight for managers responsible for improving the delivery of materials and information inside a factory.
7.	Guo, Zhengang, et al. (författare) Exploring self-organization and self-adaption for smart manufacturing complex networks 2023 Ingår i: Frontiers of Engineering Management. - : Springer Nature. - 2095-7513 .- 2096-0255. ; 10:2, s. 206-222 Tidskriftsartikel (refereegranskat)abstract Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch, short-cycle, and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments, which poses great challenges to manufacturing enterprises. Fortunately, recent advances in the Industrial Internet of Things (IIoT) and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber—physical systems for smart, flexible, and resilient manufacturing systems. In this context, this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes. Specifically, a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels. Moreover, the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology, which can be added to or removed from the networks in a plug-and-play manner. Materials, information, and financial assets are passed through interactive links across the networks. Subsequently, analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices. Consequently, an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions. The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method, reducing manufacturing cost, manufacturing time, waiting time, and energy consumption, with reasonable computational time. This work potentially enables managers and practitioners to implement active perception, active response, self-organization, and self-adaption solutions in discrete manufacturing enterprises.
8.	Jeong, Yongkuk, et al. (författare) Digital Twin-Based Services and Data Visualization of Material Handling Equipment in Smart Production Logistics Environment 2022 Ingår i: Advances in Production Management Systems. Smart Manufacturing and Logistics Systems. - Cham : Springer Nature. ; , s. 556-564 Konferensbidrag (refereegranskat)abstract Smart production logistics has introduced in manufacturing industries with emerging technologies such as digital twin, industrial internet of things, and cyber-physical system. This technological innovation initiates the new way of working, working environment, and decision-making process. Especially the decision-making process has changed from experience and intuition to knowledge and data driven. In this paper, digital twin-based services, and data visualization of material handling equipment in smart production logistics environment are presented. There are several applications of digital twin in manufacturing industries already, however feedback from the virtual environment to physical environment and interactions between them which are the essential features of digital twin are very weak in many applications. Therefore, we have developed digital twin-based services in the laboratory scale including feedback and interaction. In addition, data visualization application of material handling equipment in automotive industry is presented to provide insights to the users. Both applications have developed based on the same framework including database and middleware, so it has possibilities to develop further in the future.
9.	Lee, Christina, 1992, et al. (författare) Production Across the Nordics 2022 Rapport (övrigt vetenskapligt/konstnärligt)abstract In the uncertain and volatile market that companies are currently facing worldwide, researchers and engineers become a key link to strengthen the industry and universities in order to understand, communicate, and tackle current challenges. In the PhD course, International Production, the goal is to investigate what makes Sweden and Iceland booming industrial hubs driven by technology. Through the visits to different types of industries, such as fintech, medical, or automotive industry, we as researchers have gained a better understanding of the challenges they are currently facing. This report is a summary of our findings and observations. The participants have focused on the six challenge areas highlighted within the Produktion2030 graduate school and summarize their findings as: •Resource-efficient production: Data as a resource is becoming increasingly important for the majority of companies in the Nordics and the application of traditional resource management tools on data is a suggested area for future research. •Flexible production: To strengthen organizations by enabling production systems to be flexible to address market variations is a key challenge to consider in the manufacturing industry •Virtual production development: Digitalization level is distinct in each Nodic country with the reason that each country has its own digitalization transformation policy and different measures on digitalization level. •Humans in the production system: Humans are central in the production systems of the visited companies. Use of automation technology and AI to support humans in their work may become more common in the future. •Circular production systems and maintenance: Circular production systems require a complex approach through the whole value chain. Industry in the Nordics has started the adoption of a circularity approach. •Integrated product and production development: Integration of product and production development is a key business factor for the Nordic countries, and geographical proximity between the two departments can have a beneficial effect. We hope that this report provides more details regarding the success and current challenges of the Swedish and Icelandic enterprises.
10.	Li, Shufei, et al. (författare) Proactive human-robot collaboration : Mutual-cognitive, predictable, and self-organising perspectives 2023 Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 81, s. 102510- Forskningsöversikt (refereegranskat)abstract Human-Robot Collaboration (HRC) has a pivotal role in smart manufacturing for strict requirements of human -centricity, sustainability, and resilience. However, existing HRC development mainly undertakes either a human-dominant or robot-dominant manner, where human and robotic agents reactively perform operations by following pre-defined instructions, thus far from an efficient integration of robotic automation and human cognition. The stiff human-robot relations fail to be qualified for complex manufacturing tasks and cannot ease the physical and psychological load of human operators. In response to these realistic needs, this paper presents our arguments on the obvious trend, concept, systematic architecture, and enabling technologies of Proactive HRC, serving as a prospective vision and research topic for future work in the human-centric smart manufacturing era. Human-robot symbiotic relation is evolving with a 5C intelligence - from Connection, Coordination, Cyber, Cognition to Coevolution, and finally embracing mutual-cognitive, predictable, and self -organising intelligent capabilities, i.e., the Proactive HRC. With proactive robot control, multiple human and robotic agents collaboratively operate manufacturing tasks, considering each others' operation needs, desired resources, and qualified complementary capabilities. This paper also highlights current challenges and future research directions, which deserve more research efforts for real-world applications of Proactive HRC. It is hoped that this work can attract more open discussions and provide useful insights to both academic and industrial practitioners in their exploration of human-robot flexible production.
11.	Liu, Sichao, et al. (författare) A Framework of Data-Driven Dynamic Optimisation for Smart Production Logistics 2020 Ingår i: APMS 2020: Advances in Production Management Systems. Towards Smart and Digital Manufacturing. - Cham : Springer. ; , s. 213-221 Konferensbidrag (refereegranskat)abstract Production logistics systems in the context of manufacturing, especially in automotive sectors today, are challenged by the lack of real-time data of logistics resources, optimal configuration and management strategies of materials, and optimisation approaches of logistics operations. This turns out to be the bottleneck in achieving flexible and adaptive logistics operations. To address these challenges, this paper presents a framework of real-time data-driven dynamic optimisation schemes for production logistics systems using the combined strength of advanced technologies and decision-making algorithms. Within the context, a real-time data sensing model is developed for the timely acquisition, storage, distribution, and utilisation of equipment and process data in which sensing devices are deployed on physical shop floors. The value-added data enable production logistics processes to be digitally visible and are shared among logistics resources. A multi-agent-based optimisation scheme for production logistics systems based on real-time data is developed to obtain the optimal configuration of logistics resources. Finally, a prototype-based simulation within an automotive manufacturing shop floor is used to demonstrate the proposed conceptual framework.
12.	Liu, Sichao, et al. (författare) Cognitive neuroscience and robotics : Advancements and future research directions 2024 Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 85 Forskningsöversikt (refereegranskat)abstract In recent years, brain-based technologies that capitalise on human abilities to facilitate human–system/robot interactions have been actively explored, especially in brain robotics. Brain–computer interfaces, as applications of this conception, have set a path to convert neural activities recorded by sensors from the human scalp via electroencephalography into valid commands for robot control and task execution. Thanks to the advancement of sensor technologies, non-invasive and invasive sensor headsets have been designed and developed to achieve stable recording of brainwave signals. However, robust and accurate extraction and interpretation of brain signals in brain robotics are critical to reliable task-oriented and opportunistic applications such as brainwave-controlled robotic interactions. In response to this need, pervasive technologies and advanced analytical approaches to translating and merging critical brain functions, behaviours, tasks, and environmental information have been a focus in brain-controlled robotic applications. These methods are composed of signal processing, feature extraction, representation of neural activities, command conversion and robot control. Artificial intelligence algorithms, especially deep learning, are used for the classification, recognition, and identification of patterns and intent underlying brainwaves as a form of electroencephalography. Within the context, this paper provides a comprehensive review of the past and the current status at the intersection of robotics, neuroscience, and artificial intelligence and highlights future research directions.
13.	Liu, Sichao, et al. (författare) Complex-Network-Based Cyber-Physical Production Systems Subject to Cascading Failures 2020 Ingår i: Proceedings of 8th Swedish Production Symposium. ; , s. 395-406 Konferensbidrag (refereegranskat)
14.	Liu, Sichao, et al. (författare) Digital twin-enabled advance execution for human-robot collaborative assembly 2022 Ingår i: CIRP annals. - : Elsevier BV. - 0007-8506 .- 1726-0604. ; 71:1, s. 25-28 Tidskriftsartikel (refereegranskat)abstract A reliable human-robot workcell relies on accurate and nearly real-time updated models, especially in a constrained yet dynamic environment. This paper investigates digital twin-driven human-robot collaborative assembly enabled by function blocks. Leveraging sensor data, digital models are developed to precisely mimic physical human-robot collaborative settings supported by a digital-twin architecture. An advance-execution twin system based on the current status through real-time condition monitoring performs assembly planning and adaptive robot control using a network of function blocks. An augmented reality-based interaction method using HoloLens further facilitates human-centric assembly. An engine-assembly case study is performed to validate the effectiveness of the system.
15.	Liu, Sichao, et al. (författare) Energy-efficient trajectory planning for an industrial robot using a multi-objective optimisation approach 2018 Ingår i: Procedia Manufacturing. - : Elsevier BV. - 2351-9789. ; , s. 517-525 Konferensbidrag (refereegranskat)abstract This paper presents an approach for energy-efficient trajectory planning of an industrial robot. A model that can be used to formulate the energy consumption of the robot with the kinematics constraints is developed. Given the trajectory in the Cartesian space, the septuple B-spline is applied in joint space trajectory planning to make the velocities, accelerations, and jerks bounded and continuous, with constraints on the initial and ending values. Then, energy-efficient optimisation problem with nonlinear constraints is discussed. Simulation results show that, the proposed approach is effective solution to trajectory planning, with ensuring a good energy improvement and fluent movement for the robot manipulators.
16.	Liu, Sichao, et al. (författare) Function block-based multimodal control for symbiotic human-robot collaborative assembly 2021 Ingår i: Journal of manufacturing science and engineering. - : ASME International. - 1087-1357 .- 1528-8935. ; 143:9, s. 1-10 Tidskriftsartikel (refereegranskat)abstract In human–robot collaborative assembly, robots are often required to dynamically changetheir preplanned tasks to collaborate with human operators in close proximity. One essential requirement of such an environment is enhanced flexibility and adaptability, as well asreduced effort on the conventional (re)programming of robots, especially for complexassembly tasks. However, the robots used today are controlled by rigid native codes thatcannot support efficient human–robot collaboration. To solve such challenges, thisarticle presents a novel function block-enabled multimodal control approach for symbiotichuman–robot collaborative assembly. Within the context, event-driven function blocks asreusable functional modules embedded with smart algorithms are used for the encapsulation of assembly feature-based tasks/processes and control commands that are transferredto the controller of robots for execution. Then, multimodal control commands in the form ofsensorless haptics, gestures, and voices serve as the inputs of the function blocks to triggertask execution and human-centered robot control within a safe human–robot collaborativeenvironment. Finally, the performed processes of the method are experimentally validatedby a case study in an assembly work cell on assisting the operator during the collaborativeassembly. This unique combination facilitates programming-free robot control and theimplementation of the multimodal symbiotic human–robot collaborative assembly withthe enhanced adaptability and flexibility.
17.	Liu, Sichao, et al. (författare) IoT-enabled Dynamic Optimisation for Sustainable Reverse Logistics 2018 Ingår i: 25th CIRP Life Cycle Engineering (LCE) Conference, 30 April – 2 May 2018, Copenhagen, Denmark. - : Elsevier. ; , s. 662-667 Konferensbidrag (refereegranskat)abstract Currently, typical challenges that logistics industry faces include the exploding logistics (including reverse logistics) tasks, the lack of real-time and accurate logistics information, and demands towards sustainable logistics. Therefore, it is difficult for logistic companies to achieve highly-efficient and sustainable reverse logistics. This paper adopts a bottom-up logistics strategy that aims to achieve the real-time information-driven dynamic optimisation distribution for logistics tasks. Under this strategic framework, an IoT-enabled real-time information sensing model is designed to sense and capture the real-time data of logistics resources, which are shared among companies after the value-added processes. Real-time information-driven dynamic optimisation for logistics tasks is proposed to optimise the configuration of logistics resources, reduce logistics cost, energy consumption and the distribution distance, and alleviate the environmental pollution. The objective of this research is to develop an innovative logistics distribution model for sustainable logistics.
18.	Liu, Sichao, et al. (författare) Leveraging multimodal data for intuitive robot control towards human-robot collaborative assembly 2021 Ingår i: <em>Procedia CIRP of the 54th Conference on Manufacturing Systems</em>. - : Elsevier BV. ; , s. 206-211 Konferensbidrag (refereegranskat)abstract In human-robot collaborative assembly, robots are often required to assist human operators to execute the assembly of complex tasks. However, the robots cannot be intuitively controlled to execute accurate task assembly and motion control in close proximity. In response to this need, a novel approach using multimodal data is developed for human-centred robot control in human-robot collaborative assembly. An interface design is developed to fuse multimodal communication channels for robust and adaptive robot control, and then multimodal data are defined as control input for assembly task execution. This control scheme offers human operators symbiotic multimodal tools for proactive HRC with enhanced flexibility and adaptability.
19.	Liu, Sichao, et al. (författare) Multimodal Data-Driven Robot Control for Human-Robot Collaborative Assembly 2022 Ingår i: Journal of manufacturing science and engineering. - : ASME International. - 1087-1357 .- 1528-8935. ; 144:5 Tidskriftsartikel (refereegranskat)abstract In human-robot collaborative assembly, leveraging multimodal commands for intuitive robot control remains a challenge from command translation to efficient collaborative operations. This article investigates multimodal data-driven robot control for human-robot collaborative assembly. Leveraging function blocks, a programming-free human-robot interface is designed to fuse multimodal human commands that accurately trigger defined robot control modalities. Deep learning is explored to develop a command classification system for low-latency and high-accuracy robot control, in which a spatial-temporal graph convolutional network is developed for a reliable and accurate translation of brainwave command phrases into robot commands. Then, multimodal data-driven high-level robot control during assembly is facilitated by the use of event-driven function blocks. The high-level commands serve as triggering events to algorithms execution of fine robot manipulation and assembly feature-based collaborative assembly. Finally, a partial car engine assembly deployed to a robot team is chosen as a case study to demonstrate the effectiveness of the developed system.
20.	Liu, Sichao, 1991- (författare) Multimodal Human-Robot Collaboration in Assembly 2022 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.
21.	Liu, Sichao, et al. (författare) Sensorless force estimation for industrial robots using disturbance observer and neural learning of friction approximation 2021 Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier. - 0736-5845 .- 1879-2537. ; 71, s. 1-11 Tidskriftsartikel (refereegranskat)abstract Contact force estimation enables robots to physically interact with unknown environments and to work with human operators in a shared workspace. Most heavy-duty industrial robots without built-in force/torque sensors rely on the inverse dynamics for the sensorless force estimation. However, this scheme suffers from the serious model uncertainty induced by the nonnegligible noise in the estimation process. This paper proposes a sensorless scheme to estimate the unknown contact force induced by the physical interaction with robots. The model-based identification scheme is initially used to obtain dynamic parameters. Then, neural learning of friction approximation is designed to enhance estimation performance for robotic systems subject with the model uncertainty. The external force exerted on the robot is estimated by a disturbance observer which models the external disturbance. A momentum observer is modified to develop a disturbance Kalman filter-based approach for estimating the contact force. The neural network-based model uncertainty and measurement noise level are analysed to guarantee the robustness of the Kalman filter-based force observer. The proposed scheme is verified by the measurement data from a heavy-duty industrial robot with 6 degrees of freedom (KUKA AUGLIS six). The experimental results are used to demonstrate the estimation performance of the proposed approach by the comparison with the existing schemes.
22.	Liu, Sichao, et al. (författare) Sensorless haptic control for human-robot collaborative assembly 2021 Ingår i: CIRP - Journal of Manufacturing Science and Technology. - : Elsevier BV. - 1755-5817 .- 1878-0016. ; 32, s. 132-144 Tidskriftsartikel (refereegranskat)abstract This paper presents an approach to haptically controlling an industrial robot without using any external sensors for human-robot collaborative assembly. The sensorless haptic control approach is enabled by the dynamic models of the robot where only joint angles and joint torques are measurable. Accurate dynamic models of the robot in the presliding and sliding regimes are developed to estimate the external forces/torques, where the friction model is also explored. The estimated external force applied to the robot by an operator is converted to the reference position and speed of the robot by an admittance controller. In this research, adaptive admittance control is adopted to support human-robot collaborative assembly, naturally and easily, with accurate positioning and control for smooth movement. Moreover, torque-based commands are used to control the robot’s assembly operations. Finally, the proposed approach is validated by a case study on assisting an operator during the collaborative assembly of a car engine.
23.	Liu, Sichao, et al. (författare) Sensorless haptic control for physical human-robot interaction 2021 Ingår i: Advanced Human-Robot Collaboration in Manufacturing. - Cham : Springer Nature. ; , s. 319-350 Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract Industrial robots can offer fast speed and high accuracy for task execution but are far behind humans in terms of flexibility, adaptability, controllability, and even predictability. Therefore, combining the strength, accuracy, and speed of robots with flexibility, adaptability and controllability can achieve high-quality performance in human-robot interaction. In addition, humans in the manufacturing sector often need to adapt to the pre-planned/pre-defined tasks, dynamically. However, the robots used nowadays are controlled by pre-generated codes that cannot support effective human-robot interaction. Most industrial robots do not have built-in torque/force sensors in the joints and have difficulty in installing external force/torque sensors on the end-effectors. Additionally, the available signals from a robotic system are limited. To address such challenges, this chapter presents an approach to haptically controlling an industrial robot without using any external sensors for effortless and natural human-robot interaction. This sensorless haptic control approach is enabled by the dynamic models of the robot where only joint angles and joint torques are measurable. To have better interaction performance, accurate modelling of the dynamic model of the robot both in the presliding and sliding regimes is developed to estimate the joint torques where the friction model is thoroughly explored. The estimated external force applied to the robot by an operator is transferred into reference position and speed of the robot by an admittance controller, and adaptive admittance parameters are adopted to make human-robot interaction more natural and easier with accurate positioning and control and smooth movement. Finally, the proposed approach is validated by a case study in which a human operator interacts with an industrial robot.
24.	Liu, Sichao, et al. (författare) Symbiotic human-robot collaboration : Multimodal control using function blocks 2020 Ingår i: Procedia CIRP. - : Elsevier B.V.. - 2212-8271. ; , s. 1188-1193 Konferensbidrag (refereegranskat)abstract Complex assembly tasks require increased flexibility and adaptability, as well as higher effort on the conventional (re)programming of robots. To solve such challenges, this paper presents a function block-enabled multimodal control scheme for symbiotic human-robot collaborative assembly. Data/event-driven function blocks with smart decision algorithms are used for human-centred robot control with multimodal fusion. Then, multimodal control commands in the form of haptics, gesture and voice are defined as the inputs of the function blocks to trigger task execution. This novel scheme facilitates the implementation of the multimodal symbiotic human-robot collaborative assembly with enhanced stability and flexibility.
25.	Wang, Lihui, et al. (författare) Function block-based human-robot collaborative assembly driven by brainwaves 2021 Ingår i: CIRP annals. - : Elsevier BV. - 0007-8506 .- 1726-0604. ; 70:1, s. 5-8 Tidskriftsartikel (refereegranskat)abstract As an emerging communication modality, brainwaves can be used to control robots for seamless assembly, especially in noisy environments where voice recognition is not reliable or when an operator is occupied with other tasks and unable to make gestures. This paper investigates human-robot collaborative assembly based on function blocks and driven by brainwaves. Using wavelet transform, brainwaves measured by EEG sensors are converted to time-frequency images and subsequently classified by a convolutional neural network (CNN) as commands to trigger a network of function blocks for assembly actions. The effectiveness of the system is experimentally validated through an engine-assembly case study.
26.	Yi, Shuming, et al. (författare) A vision-based human-robot collaborative system for digital twin 2022 Ingår i: Procedia CIRP. - : Elsevier BV. - 2212-8271. ; , s. 552-557 Konferensbidrag (refereegranskat)abstract Flexible and safe human-robot collaboration depends on accurately capturing the three-dimensional motion of humans and robots in the field of smart manufacturing. In this paper, a novel approach to developing a human-robot collaborative assembly system is proposed and applied to the field of digital twins. Within the context, a deep learning-based model is explored to develop a depth camera-based human recognition system for accurate prediction of key points for human skeletons model and high-precision human localisation in a human-robot collaborative setting. After the functional mapping of robot calibration, a collision warning module leverages coordinates of key human-robot points to facilitate efficient and safe human-robot collaborative assembly.
27.	Yi, Shuming, et al. (författare) Safety-aware human-centric collaborative assembly 2024 Ingår i: Advanced Engineering Informatics. - : Elsevier BV. - 1474-0346 .- 1873-5320. ; 60 Tidskriftsartikel (refereegranskat)abstract Manufacturing systems envisioned for factories of the future will promote human-centricity for close collaboration in a shared working environment towards better overall productivity within the context of Industry 5.0. Robust and accurate recognition and prediction of human intentions are crucial to reliable and safe collaborative operations between humans and robots. For this purpose, this paper proposed a safety-aware human-centric collaborative assembly approach driven by function blocks, human action recognition for intention detection, and collision avoidance for safe robot control. Within the context, a deep learning-based recognition system is developed for high-accuracy human intention recognition and prediction, and an assembly feature-based approach driven by function blocks is presented for assembly execution and control. Thus, assembly features and human behaviours during assembly are formulated to support safe assembly actions. Skeleton-based human behaviours are defined as control inputs to an adaptive safety-aware scheme. The scheme includes collaborative and parallel mode-based pre-warning and obstacle avoidance approaches for a human-centric collaborative assembly system. The former is to monitor and regulate robot control modes when working in parallel with humans, and the latter uses a position-based approach to control robot actions by adaptively adjusting obstacle avoidance trajectories in a dynamic collaborative environment. The findings of this paper reveal the effectiveness of the developed system, as experimentally validated through an engine-assembly case study.
28.	Zhang, Jianjing, et al. (författare) Neural rendering-enabled 3D modeling for rapid digitization of in-service products 2023 Ingår i: CIRP annals. - : Elsevier BV. - 0007-8506 .- 1726-0604. ; 72:1, s. 93-96 Tidskriftsartikel (refereegranskat)abstract Rapid digitization of physical objects enables monitoring, analysis, and maintenance of in-service products, of which an up-to-date CAD model is not available. It provides designers with the products' actual response to the real-world usage, which provides a reference base for design optimization. This paper presents neural rendering as a novel method for rapid digital model building. It learns a radiance field from RGB images to determine the characteristics of the physical object. Textured mesh can be generated from the learned radi-ance field for efficient 3D modeling. The effectiveness of the method is demonstrated by an engine component.
29.	Zhang, Yingfeng, et al. (författare) Agent-based intelligent medical diagnosis system for patients 2015 Ingår i: Technology and Health Care. - : IOS Press. - 0928-7329 .- 1878-7401. ; 23, s. S397-S410 Tidskriftsartikel (refereegranskat)abstract BACKGROUND: According to the analysis of the challenges faced by the current public health circumstances such as the sharp increase in elderly patients, limited medical personnel, resources and technology, the agent-based intelligent medical diagnosis system for patients (AIMDS) is proposed in this research. OBJECTIVE: Based on advanced sensing technology and professional medical knowledge, the AIMDS can output the appropriate medical prescriptions and food prohibition when the physical signs and symptoms of the patient are inputted. METHODS: Three core modules are designed include sensing module, intuition-based fuzzy set theory/medical diagnosis module, and medical knowledge module. RESULTS: The result shows that the optimized prescription can reach the desired level, with great curative effect for patient disease, through a case study simulation. CONCLUSION: The presented AIMDS can integrate sensor technique and intelligent medical diagnosis methods to make an accurate diagnosis, resulting in three-type of optimized descriptions for patient selection.
30.	Zhang, Yingfeng, et al. (författare) Clustering and Genetic Algorithm Based Hybrid Flowshop Scheduling with Multiple Operations 2014 Ingår i: Mathematical problems in engineering (Print). - : Hindawi Publishing Corporation. - 1024-123X .- 1563-5147. ; 2014 Tidskriftsartikel (refereegranskat)abstract This research is motivated by a flowshop scheduling problem of our collaborative manufacturing company for aeronautic products. The heat-treatment stage (HTS) and precision forging stage (PFS) of the case are selected as a two-stage hybrid flowshop system. In HTS, there are four parallel machines and each machine can process a batch of jobs simultaneously. In PFS, there are two machines. Each machine can install any module of the four modules for processing the workpeices with different sizes. The problem is characterized by many constraints, such as batching operation, blocking environment, and setup time and working time limitations of modules, and so forth. In order to deal with the above special characteristics, the clustering and genetic algorithm is used to calculate the good solution for the two-stage hybrid flowshop problem. The clustering is used to group the jobs according to the processing ranges of the different modules of PFS. The genetic algorithm is used to schedule the optimal sequence of the grouped jobs for the HTS and PFS. Finally, a case study is used to demonstrate the efficiency and effectiveness of the designed genetic algorithm.
31.	Zhang, Yingfeng, et al. (författare) Game theory based real-time shop floor scheduling strategy and method for cloud manufacturing 2017 Ingår i: International Journal of Intelligent Systems. - : John Wiley & Sons. - 0884-8173 .- 1098-111X. ; 32:4, s. 437-463 Tidskriftsartikel (refereegranskat)abstract With the rapid advancement and widespread application of information and sensor technologies in manufacturing shop floor, the typical challenges that cloud manufacturing is facing are the lack of real‐time, accurate, and value‐added manufacturing information, the efficient shop floor scheduling strategy, and the method based on the real‐time data. To achieve the real‐time data‐driven optimization decision, a dynamic optimization model for flexible job shop scheduling based on game theory is put forward to provide a new real‐time scheduling strategy and method. Contrast to the traditional scheduling strategy, each machine is an active entity that will request the processing tasks. Then, the processing tasks will be assigned to the optimal machines according to their real‐time status by using game theory. The key technologies such as game theory mathematical model construction, Nash equilibrium solution, and optimization strategy for process tasks are designed and developed to implement the dynamic optimization model. A case study is presented to demonstrate the efficiency of the proposed strategy and method, and real‐time scheduling for four kinds of exceptions is also discussed.
32.	Zhang, Yingfeng, et al. (författare) Production System Performance Prediction Model Based on Manufacturing Big Data 2015 Ingår i: ICNSC 2015 - 2015 IEEE 12th International Conference on Networking, Sensing and Control. - : IEEE. - 9781479980697 ; , s. 277-280 Konferensbidrag (refereegranskat)abstract Existing production systems are short of real-time performance status of production process active perception, resulting in the production abnormal conditions processed lag, leading to the frequency problems of deviations in production tasks execution and planning. To address this problem, in this research, an advanced identification technology is extended to the manufacturing field to acquire the real-time performance data. Based on the sensed real-time manufacturing data, this paper presents a prediction method of production system performance by applying the Dynamic Bayesian Networks (DBN) theory and methods. It aims to achieve the prediction of the performance status of production system and potential anomalies, and to provide the important and abundant prediction information for real-time production control.
33.	Zhang, Yingfeng, et al. (författare) Real-time shop-floor production performance analysis method for the internet of manufacturing things 2014 Ingår i: Advances in Mechanical Engineering. - : Hindawi Publishing Corporation. - 1687-8132 .- 1687-8140. ; 6 Tidskriftsartikel (refereegranskat)abstract Typical challenges that manufacturing enterprises are facing now are compounded by lack of timely, accurate, and consistent information of manufacturing resources. As a result, it is difficult to analyze the real-time production performance for the shopfloor. In this paper, the definition and overall architecture of the internet of manufacturing things is presented to provide a new paradigm by extending the techniques of internet of things (IoT) to manufacturing field. Under this architecture, the real-time primitive events which occurred at different manufacturing things such as operators, machines, pallets, key materials, and so forth can be easily sensed. Based on these distributed primitive events, a critical event model is established to automatically analyze the real-time production performance. Here, the up-level production performance analysis is regarded as a series of critical events, and the real-time value of each critical event can be easily calculated according to the logical and sequence relationships among these multilevel events. Finally, a case study is used to illustrate how to apply the designed methods to analyze the real-time production performance.
34.	Zhang, Yaqian, et al. (författare) Skeleton-RGB integrated highly similar human action prediction in human–robot collaborative assembly 2024 Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 86 Tidskriftsartikel (refereegranskat)abstract Human–robot collaborative assembly (HRCA) combines the flexibility and adaptability of humans with the efficiency and reliability of robots during collaborative assembly operations, which facilitates complex product assembly in the mass personalisation paradigm. The cognitive ability of robots to recognise and predict human actions and make responses accordingly is essential but currently still limited, especially when facing highly similar human actions. To improve the cognitive ability of robots in HRCA, firstly, a two-stage skeleton-RGB integrated model focusing on human-parts interaction is proposed to recognise highly similar human actions. Specifically, it consists of a feature guidance module and a feature fusion module, which can balance the accuracy and efficiency of human action recognition. Secondly, an online prediction approach is developed to predict human actions ahead of schedule, which includes a pre-trained skeleton-RGB integrated model and a preprocessing module. Thirdly, considering the positioning accuracy of the parts to be assembled and the continuous update of human actions, a dynamic response scheme of the robot is designed. Finally, the feasibility and effectiveness of the proposed model and approach are verified by a case study of a worm-gear decelerator assembly. The experimental results demonstrate that the proposed model achieves precise human action recognition with a high accuracy of 93.75% and a lower computational cost. Specifically, only 15 frames from a skeleton stream and 5 frames (less than 16 frames in general) from an RGB video stream are adopted. Moreover, it only takes 1.026 s to achieve online human action prediction based on the proposed prediction method. The dynamic response scheme of the robot is also proven to be feasible. It is expected that the efficiency of human–robot interaction in HRCA can be improved from a closed-loop view of perception, prediction, and response.

Skapa referenser, mejla, bekava och länka

Länka till träfflistan

Träfflista för sökning "WFRF:(Liu Sichao) "

Avgränsa träffmängd

År