Self-learning robotics for industrial contact-rich tasks (ATARI): enabling smart learning in automated disassembly
Lead Research Organisation:
University of Birmingham
Department Name: Mechanical Engineering
Abstract
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.
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.
Organisations
- University of Birmingham (Lead Research Organisation)
- Caterpillar Inc. (Collaboration)
- University of Castile-La Mancha (Collaboration)
- University of Sheffield (Collaboration)
- Ecobat Technologies (Collaboration)
- Airbus Group (Collaboration)
- Dyson (Collaboration)
- Beihang University (Project Partner)
- KEYENCE (UK) Ltd (Project Partner)
- Wuhan University of Technology (Project Partner)
- KUKA (United Kingdom) (Project Partner)
- Manufacturing Technology Centre (United Kingdom) (Project Partner)
Publications
Laili Y
(2022)
Optimisation of Robotic Disassembly for Remanufacturing
Laili Y
(2022)
Optimisation of Robotic Disassembly for Remanufacturing
Laili Y
(2022)
Robotic Disassembly Sequence Planning With Backup Actions
in IEEE Transactions on Automation Science and Engineering
Laili Y
(2022)
Optimisation of Robotic Disassembly for Remanufacturing
Laili Y
(2022)
Optimisation of Robotic Disassembly for Remanufacturing
Lan F
(2023)
On the correctness of using two-dimensional representations in the analysis of cylindrical peg-hole insertion and withdrawal.
in Royal Society open science
Liu Q
(2023)
A Two-Stage Screw Detection Framework for Automatic Disassembly Using a Reflection Feature Regression Model.
in Micromachines
Qu M
(2023)
Robotic Disassembly Task Training and Skill Transfer Using Reinforcement Learning
in IEEE Transactions on Industrial Informatics
Su S
(2022)
Design of a compliant device for peg-hole separation in robotic disassembly
in The International Journal of Advanced Manufacturing Technology
Wang Y
(2022)
Robotic disassembly and remanufacturing automation
Description | Guidelines on the implementation of eco-friendly criteria for AI and other emerging technologies |
Geographic Reach | Multiple continents/international |
Policy Influence Type | Implementation circular/rapid advice/letter to e.g. Ministry of Health |
Impact | This report is ITU's advice to all UN members |
URL | https://www.itu.int/en/ITU-T/focusgroups/ai4ee/Documents/T-FG-AI4EE-2021-D.WG3.01-Word-E.docx |
Description | EPSRC IAA (2022-25): Affordable And Modular Robotic Disassembly Systems |
Amount | £50,000 (GBP) |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 04/2023 |
End | 12/2023 |
Description | New partnership with Caterpillar |
Organisation | Caterpillar Inc. |
Country | United States |
Sector | Academic/University |
PI Contribution | The ATARI team is collaborating with Caterpillar on robotic disassembly techniques for battery and engine remanufacturing for the defense sector. |
Collaborator Contribution | The ATARI team is collaborating with Caterpillar on robotic disassembly techniques for battery and engine remanufacturing for the defense sector. Suppor letter sent in Jan 2023. |
Impact | Exchange of information and joint new research proposal submitted. |
Start Year | 2023 |
Description | Research collabration with Airbus |
Organisation | Airbus Group |
Country | France |
Sector | Academic/University |
PI Contribution | Regular talks with the collaborator to share research activity information and ideas |
Collaborator Contribution | Members of Airbus visit the UoB team to contribute to the project |
Impact | We are joining forces in research activities and the preparation of HORIZON proposals. |
Start Year | 2022 |
Description | Research collabration with Dyson |
Organisation | Dyson |
Country | United Kingdom |
Sector | Private |
PI Contribution | Regular talks with the collaborator to share research activity information and ideas |
Collaborator Contribution | Members of Dyson visit the UoB team to contribute to the project |
Impact | We are joining forces in research activities and the preparation of new research proposals. |
Start Year | 2022 |
Description | Research collabration with ECOBAT |
Organisation | Ecobat Technologies |
Country | Germany |
Sector | Private |
PI Contribution | Regular talks with the collaborator to share research activity information and ideas |
Collaborator Contribution | ECOBAT has agreed to host onsite tests for ATARI developments |
Impact | We are preparing for an onsite test of ATARI technologies |
Start Year | 2022 |
Description | Research collabration with Sheffield University |
Organisation | University of Sheffield |
Department | Sheffield Biorepository |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Regular talks with the collaborator to share research activity information and ideas |
Collaborator Contribution | Members of Sheffield University visit the UoB team to contribute to the project |
Impact | We are joining forces in research activities and the preparation of new research proposals. |
Start Year | 2022 |
Description | Research collabration with University of Castilla-La Mancha - UCLM |
Organisation | University of Castile-La Mancha |
Country | Spain |
Sector | Academic/University |
PI Contribution | Regular talks with the collaborator to share research activity information and ideas |
Collaborator Contribution | Members of Universidad de Castilla-La Mancha visit the UoB team to contribute to the project |
Impact | We are joining forces in research activities and the preparation of HORIZON proposals. |
Start Year | 2022 |
Description | A conference presentation at ICAC2022 - Mr Yue Zang |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Schools |
Results and Impact | A conference presentation at ICAC2022 - Mr Yue Zang |
Year(s) Of Engagement Activity | 2022 |
Description | A conference presentation at ICAC2022 - Ms Farzaneh Goli |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Schools |
Results and Impact | A conference presentation at ICAC2022 - Ms Farzaneh Goli |
Year(s) Of Engagement Activity | 2022 |
Description | An invited talk at ICAC2022 - Dr Yongjing Wang |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Schools |
Results and Impact | Dr Yongjing Wang has been invited to give a talk at ICAC2022 |
Year(s) Of Engagement Activity | 2022 |
Description | An invited talk at IEEE International Conference on Universal Village (UV) |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Schools |
Results and Impact | Dr Yongjing Wang has been invited to give a talk at IEEE International Conference on Universal Village (UV), Boston, US. |
Year(s) Of Engagement Activity | 2022 |
Description | Special session at 2023 IEEE International Conference on Automation Science and Engineering (CASE) |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | We are hosting a special session about smart remanufacturing at the 2023 IEEE International Conference on Automation Science and Engineering (CASE). CASE is the flagship automation conference of the IEEE Robotics and Automation Society and constitutes the primary forum for cross-industry and multidisciplinary research in automation. "Smart Remanufacturing Technologies" (Code: 8wf47). |
Year(s) Of Engagement Activity | 2023 |
URL | https://case2023.org/special-session-proposals/ |