Active projects

Robotic skill transfer and augmentation for contact-rich tasks in manufacturing (STAMAN)

Yongjing Wang (Principal Investigator)
Value: £1.3M (FEC) including £1M from the EPSRC
Duration: 2024-2027


STAMAN’s vision is to create AI-based mechanisms to allow robots to share and recreate obtained digital skills (e.g. motion and force/torque control strategies) to allow easy automation scale-up for contact-rich tasks. This includes considering two research questions:

1) For skill transfer – how can a contact-rich skill be quickly transferred to a different robot (e.g. transferring a bolt-nut separation skill from a high-precision robot to a low-precision robot)?

2) For skill augmentation – how can existing contact-rich skills be used to create new contact-rich skills (e.g. augmentation of rigid-material skills to deal with soft materials)?

The project will develop a portfolio of research into the science of digital skills for contact-rich tasks, focusing on common manufacturing tasks such as bolt-nut assembly/disassembly, peg-hole insertion/separation, and shaft-ring assembly/disassembly. The ability to transfer and augment digital skills for contact-rich tasks will allow automation systems to be implemented on a larger scale, with minimal manual setting and fine-tuning required. STAMAN aims to create transferrable and augmentable digital skills that will underpin the development of mass machine skills for future manufacturing, similar to how industrial robots have contributed to modern mass production.

The proposed research encourages more use of robots in assembly (e.g. automotive, aerospace, electronics, etc.) and disassembly (e.g. repairs, remanufacturing and recycling), and thus directly contributes to the UK’s Made Smarter initiative and the circular economy goals.

Details: UKRI

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
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.

Past projects

Affordable and modular robotic disassembly systems (EPSRC IAA )

Yongjing Wang (Principal Investigator)
Value: EPSRC IAA Follow on rate
Duration: 4/2023-11/2023
Disassembly is a key step in remanufacturing and recycling. The status quo of disassembly for many consumer products is economically unjustifiable, mainly because of a lack of enabling technologies for the automation of disassembly. Also, some products, such as lithium-ion batteries from electric vehicles
and medical equipment, involve significant health and safety risks in disassembly (e.g. leakage and explosion), making disassembly expensive and difficult to scale up. 
In this project, we aim to significantly reduce the cost of robotic technologies by improving the design of the disassembly robot system to create a general-use low-cost robotic disassembly robot station (e.g. a single robot cell) that can be used for *** and *** – the two major disassembly operations to disassemble common consumer products. 
AMRD aims to develop a STANDARDISED robotic disassembly work cell that can be adopted in a variety of disassembly tasks with easy programming. It offers significantly more functionalities and a higher level of automation than the basic disassembly equipment, and significantly lower costs than the customised
solutions. Being standardised means the proposed robotic disassembly product can be mass-duplicated and mass-produced which will pave the way for scale-up.

Hierarchical use of battery : Intelligent evaluation and collaborative robot disassembly

Yongjing Wang (UK Principal Investigator)
Value: £*** from JITRI
Duration: 10/2019-10/2022
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

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.

If you’re looking to explore how automation technologies can help you be more productive or provide a competitive edge then please contact us. 

Official AMTECAA website 

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
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.