Projects
Self-learning robotics for industrial contact-rich tasks (ATARI): enabling smart learning in automated disassembly
Yongjing Wang (Principal Investigator)
Value: £300k from EPSRC and £100k from the University of Birmingham
Duration: 10/2021-09/2023
Value: £300k from EPSRC and £100k from the University of Birmingham
Duration: 10/2021-09/2023
Abstract:
The existing procedure and state-of-the-art techniques for disassembly automation usually require a comprehensive analysis of a disassembly task, correct design of sensing and compliance facilities, efficient task plans, and reliable system integration. It is usually a complex, expensive and time-consuming process to implement a robotic disassembly system.
This project will develop a self-learning mechanism to allow robots to learn disassembly tasks and the respective control strategies autonomously, by combining multidimensional sensing and machine learning techniques. This capability will help build a more plug-and-play disassembly automation system, and reduce the technical difficulties and the implementation costs of disassembly automation.
It is expected the next generation industrial robotics can be adopted in more complex and uncertain tasks such as maintenance, cleaning, repair, remanufacturing and recycling, where many processes are contact-rich. Disassembly is a typical contact-rich task. The PI envisages that self-learning robotic disassembly will provide key understandings and technologies that can be adopted to the automation of other types of contact-rich tasks in the future to encourage a wider adoption of robots in the UK industry.
This project will develop a self-learning mechanism to allow robots to learn disassembly tasks and the respective control strategies autonomously, by combining multidimensional sensing and machine learning techniques. This capability will help build a more plug-and-play disassembly automation system, and reduce the technical difficulties and the implementation costs of disassembly automation.
It is expected the next generation industrial robotics can be adopted in more complex and uncertain tasks such as maintenance, cleaning, repair, remanufacturing and recycling, where many processes are contact-rich. Disassembly is a typical contact-rich task. The PI envisages that self-learning robotic disassembly will provide key understandings and technologies that can be adopted to the automation of other types of contact-rich tasks in the future to encourage a wider adoption of robots in the UK industry.
Automatic disassembly replanning for autonomous remanufacturing
Yongjing Wang (UK Principal Investigator)
Value: £12k from the Royal Society and £12k from NSFC
Duration: 03/2019-03/2022
Value: £12k from the Royal Society and £12k from NSFC
Duration: 03/2019-03/2022
Abstract:
A key step in remanufacturing is disassembly of the returned product to be remanufactured. Disassembly sequence planning is challenging due to uncertainties in the conditions of the returned products and complex recovery procedures involving human operators and machines. Rust, corrosion, deformation and parts missing may require disassembly plans to change
and adapt frequently. Current industrial robotic techniques, most of which designed for repetitive and structured motions in assembly, do not have the required flexibility for disassembly. The project will facilitate autonomous multi-agent model and optimal replanning to deal with unforeseen changes in
robotic disassembly processes. We expect to produce scientific results relevant to researchers in robotics, modelling & simulation, remanufacturing and recycling. The resultant techniques will promote the flexibility and robustness of robotic disassembly systems, and thus encourage a wider adoption of remanufacturing.
and adapt frequently. Current industrial robotic techniques, most of which designed for repetitive and structured motions in assembly, do not have the required flexibility for disassembly. The project will facilitate autonomous multi-agent model and optimal replanning to deal with unforeseen changes in
robotic disassembly processes. We expect to produce scientific results relevant to researchers in robotics, modelling & simulation, remanufacturing and recycling. The resultant techniques will promote the flexibility and robustness of robotic disassembly systems, and thus encourage a wider adoption of remanufacturing.
Hierarchical use of battery : Intelligent evaluation and collaborative robot disassembly
Yongjing Wang (UK Principal Investigator)
Value: £*** from JITRI
Duration: 10/2019-10/2022
Value: £*** from JITRI
Duration: 10/2019-10/2022
Abstract:
This project develops a robotic disassembly system for end-of-life electric vehicle battery packs.
AMTECAA (Advanced Manufacturing Technologies to Create, Activate & Automate) programme
Yongjing Wang (Co-investigator)
Value: £10m funded by the European Structural Investment Funds (ESIF)
Duration: 2019-2023
Value: £10m funded by the European Structural Investment Funds (ESIF)
Duration: 2019-2023
Abstract:
AMTECAA supports SME’s in Birmingham and Solihull. AMTECAA covers six high-impact interrelated technology areas, i.e. additive manufacturing, advanced machining, surface engineering, laser processing, and industrial automation while digital manufacturing (Industry 4.0) is an overreaching enabler for developing new products and processes.
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