A robot arm digital twin utilising reinforcement learning

Matulis, Marius; Harvey, Carlo

doi:10.1016/j.cag.2021.01.011

Cited by 114 publications

(37 citation statements)

References 18 publications

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“…Matulis et al present a method for creating and training a digital twin robot for a robotic arm. The project demonstrates that a trained robot can perform a given task even if it is currently in a state it has never been in before ( Matulis and Harvey, 2021 ). Liu et al proposed a digital-driven machining quality tracking and dynamic control method, which effectively solved the problems of low efficiency of quality problem traceability, poor timeliness and unpredictability of quality control in machining process ( Liu Y et al, 2021b ).…”

Section: Related Workmentioning

confidence: 99%

Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

Liu

Tao

et al. 2022

Front. Bioeng. Biotechnol.

102

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Mobile robots have an important role in material handling in manufacturing and can be used for a variety of automated tasks. The accuracy of the robot’s moving trajectory has become a key issue affecting its work efficiency. This paper presents a method for optimizing the trajectory of the mobile robot based on the digital twin of the robot. The digital twin of the mobile robot is created by Unity, and the trajectory of the mobile robot is trained in the virtual environment and applied to the physical space. The simulation training in the virtual environment provides schemes for the actual movement of the robot. Based on the actual movement data returned by the physical robot, the preset trajectory of the virtual robot is dynamically adjusted, which in turn enables the correction of the movement trajectory of the physical robot. The contribution of this work is the use of genetic algorithms for path planning of robots, which enables trajectory optimization of mobile robots by reducing the error in the movement trajectory of physical robots through the interaction of virtual and real data. It provides a method to map learning in the virtual domain to the physical robot.

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Section: Related Workmentioning

confidence: 99%

Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

Liu

Tao

et al. 2022

Front. Bioeng. Biotechnol.

102

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“…The curse of dimensionality may also disable the system from learning something useful [ 287 ]. As a robotic system’s digital twin grows up and provides reliable data, the combination “DT + RL” seems to be a promising approach ( SG -factor): in [ 265 ], Matulis et al integrated digital twin and reinforcement learning for a 6DOF robotic manipulator to plan pick-and-place motions; in [ 266 ], Liu et al proposed a multitasking-oriented robot arm motion planning scheme based on deep reinforcement learning and twin synchro-control; in [ 267 ], Xia et al proposed a DT to train deep reinforcement learning agent for automating smart manufacturing systems; and in [ 268 ], Zhao et al demonstrated collision avoidance for a number of UAVs in a confined airspace, using LSTM-MACG. While RL has become popular in recent years, it is not the only AI strategy that is integrated with DTs.…”

Section: Advanced Roboticsmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

189

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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“…project management [112], robotics [113,114], sustainable development [115,116]. However, in current study will focus on Energy systems.…”

Section: Definitionmentioning

confidence: 99%

Digital Twin (DT) in Smart Energy Systems - Systematic Literature Review of DT as a growing solution for Energy Internet of the Things (EIoT)

2021

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In the industry 4.0 era, the Smart Energy System (SES) should be able to address the emerging challenges of digitization and socioeconomic/ecologic transition along with other critical entities of the society. However, because of the complexity of this system, both researchers and practitioners are seeking an agile and smart solution. The main motive of this review is to investigate the applications and implementation of Digital Twin (DT) in the provision of energy services. Research Questions (RQ) of this study include: RQ1: What are the applications of DT in SES and how effective is DT in that use case of EIoT? RQ2: Which issues of an SES can be addressed efficiently by using DT? Through answering the mentioned questions, the current study is heading to following objectives (O), O1: Describe the state of the art of DT in SES. O2: Develop a direction for energy 4.0 management through listing the applications, challenges and important factors of implementing DTs. O3: Provide a list of various approaches in employing DT in the scope of SES. The current study is a systematic literature review (SLR), based on SCOPUS, WOS and IEEE digital libraries. Two keywords (namely “Digital Twin” and “Energy Systems”) have been first used. To achieve the final list of articles, 2 levels of screening have been conducted. The first Screening was based on the relevance of the results concerning research objectives. The second screening was an abstract study. The exclusion/inclusion criteria in the abstract study were based on the research questions. The papers that have the potential of answering one of the research questions have been included. Since the implementation of DT is a rather new topic, both backward snowballing and forward snowballing strategies are implemented to finalize the article selection phase. 60 articles identified by searching through scientific databases and 11 articles have been appended to the list during the snowballing process. The results of the current review provide a managerial guideline for practitioners that are heading to utilize DT, along with an anthology of DT within SES scope to feed possible future studies.

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A robot arm digital twin utilising reinforcement learning

Cited by 114 publications

References 18 publications

Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Digital Twin (DT) in Smart Energy Systems - Systematic Literature Review of DT as a growing solution for Energy Internet of the Things (EIoT)

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