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Details of Grant 

EPSRC Reference: EP/W00206X/1
Title: Self-learning robotics for industrial contact-rich tasks (ATARI): enabling smart learning in automated disassembly
Principal Investigator: Wang, Dr Y
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Beihang University KEYENCE (UK) Ltd KUKA Robotics UK Limited
The Manufacturing Technology Centre Ltd Wuhan University of Technology
Department: Mechanical Engineering
Organisation: University of Birmingham
Scheme: New Investigator Award
Starts: 01 May 2022 Ends: 31 October 2024 Value (£): 298,263
EPSRC Research Topic Classifications:
Artificial Intelligence Control Engineering
Robotics & Autonomy
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
04 Aug 2021 Engineering Prioritisation Panel Meeting 4 and 5 August 2021 Announced
Summary on Grant Application Form
Disassembly is an essential operation in many industrial activities including repair, remanufacturing and recycling. Disassembly tends to be manually carried out - it is labour intensive and usually inefficient.

Disassembly requires high-level dexterity in manipulations and thereby can be more difficult to robotise in comparison to the tasks that have no physical contacts (e.g. computer visual inspection) or simple contacts (e.g. cutting, welding, pick-and-place). Robotic disassembly has the potential to improve the productivity of repair, remanufacturing, recycling, all of which have been recognised as key components of a more circular economy.

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 a 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 Principal Investigator 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.

Key Findings
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Organisation Website: http://www.bham.ac.uk