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Developing a multi-robot task planner for robotic disassembly of electrical-vehicle battery disassembly in industrial scale (UK students only)


   School of Metallurgy & Materials


About the Project

About the Project

This project aims to develop a hybrid (logical and geometric) task coordination planner for multi-robot where each robot has an specific task; and has to collaborate jointly with other robots. The developed method focuses on disassembly of electrical vehicle Lithium-ion battery (LIB) battery packs.

To efficiently generate feasible and optimized task execution plans for a group of robots to disassemble a lithium-ion battery, a hierarchical multi-robot temporal task planning framework is required, in which a central server allocates the collaborative tasks to the robots, and then individual robots can independently synthesize their task execution plans in a decentralized manner. The robots solely or jointly, autonomously or semi-autonomously, will carry out a variety of manipulation action primitives (e.g. unscrewing, cutting, grasping, unplugging, separating etc.) for both robotic disassembly and robotic testing of EV battery.

The work exploits the Behaviour Trees model for task execution and monitoring, which links different robot capabilities such as object tracking and motion planning in a modular fashion. A behaviour tree is a graphically represented mathematical model of plan execution that describes switching between a finite set of tasks in a modular fashion. Action primitives should be capable of being applied, via perception-based online planning, to a wide variety of object categories and object examples, which are present in arbitrary positions, with respect to arbitrary structures of obstacles and clutter. During action executions, robots will continuously update their status to complete the scheduled tasks without collision. Further complexities and extensions may involve bi-manual tasks (e.g. one arm holds an object while other unscrews or cuts it), or scheduling and synchronising simultaneous actions of multiple arms. Each such action primitive will exploit state-of-the-art methods in computer vision and sensory guided autonomous motion planning and control. Robots must adapt these actions to uncertainty and diversity in the objects being handled. The PhD research problem is how to automatically plan a sequence of such actions to disassemble a complex object.

Student will be working in a team of mutually supportive researchers, who together are creating a portfolio of robotic disassembly capabilities of various types, with research spanning: autonomous motion planning; dynamics and control of forceful actions; advanced human-robot teleoperative interfaces; machine learning, computer vision and multi-sensor fusion.

The overarching philosophy of our lab is to develop novel robotics technologies at the research cutting edge, while also demonstrating that these methods are practical and useful, in the context of societally important industrial applications.

Entry requirements

- The successful applicants should have a first-class degree in engineering and preferably an MSc in robotics or a closely related subject.

- a solid background in linear algebra, model parameterisation and optimisation, kinematics and dynamics.

- excellent programming skills (C++, Python, Matlab),

- good communication skills (oral and written).

Environment and support

Lab Tour: https://my.matterport.com/show/?m=du6jbWMzRk4

The University of Birmingham Extreme Robotics Lab, is one of the largest and best equipped robotics labs in UK. Facilities include a 1,000m2 lab on the main university campus, coupled with a full scale heavy duty industrial robot test-bed off campus at Birmingham Energy Innovation Centre. This comprises a pair of three tonne, 3.6metres reach, KUKA KR500 arms with 500kg payload, and a variety of other high-spec industrial robots. The academic campus lab includes a large number of high-spec cobots, in-lab super-computer, high-spec imaging devices and other sensors, hands and grippers, a VR/AR area, high-spec haptic devices, robot vehicles and much more. The UoB robotics team are collectively working on robotic grasping and manipulation, planning and reasoning for fixed and/or mobile manipulators, robotic vehicles and navigation, robot dynamics and control, Computer Vision, machine learning and AI. During your PhD, you will be part of a team of interdisciplinary researchers spanning mathematics, robotics, computer science, mechatronics and AI. As part of this project, you will also have access to an international collaborative network of world-leading experts in robotics.

This project is jointly funded by the University of Birmingham (UoB) and Manufacturing Technology Centre (MTC), and the PhD candidate will benefit from the combined expertise of the project supervisors from both UoB and MTC.

In addition, this PhD project benefits from the availability of the large-scale industrial robot facility at the Birmingham Energy Innovation Centre (BEIC), Tyseley Energy Park (TEP). We are particularly keen for this PhD project to be implemented and demonstrated on these large-scale robots, and for the student to learn the additional technical skills needed to work with such robots.

How to apply

1- Put your CV, 1-page cover letter on a safe cloud (e.g. Onedrive, Dropbox etc.) and get the link.

2- Send an email and share the link with Dr Alireza Rastegarpaah (Email: a.rastegarpanah[at]bham.ac.uk)

** Please do not attach your files to your email; the inbox is almost full**

Informal enquiries should be addressed to Dr Alireza Rastegarpanah via email: a.rastegarpanah[at]bham.ac.uk


Funding Notes

A fully funded (stipend+fees) PhD studentship is available only for UK students.

References

1- Rastegarpanah, A., Ahmeid, M., Marturi, N., Attidekou, P. S., Musbahu, M., Ner, R., ... & Stolkin, R. (2021). Towards robotizing the processes of testing lithium-ion batteries. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 235(8), 1309-1325.
2- Rastegarpanah, A., Gonzalez, H. C., & Stolkin, R. (2021). Semi-autonomous behaviour tree-based framework for sorting electric vehicle batteries components. Robotics, 10(2), 82.
3- Rastegarpanah, A., Hathaway, J., & Stolkin, R. (2021). Vision-guided MPC for robotic path following using learned memory-augmented model. Frontiers in Robotics and AI, 8.

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