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Details of Grant 

EPSRC Reference: EP/T027746/1
Title: Automatic Posture and Balance Support for Supernumerary Robotic Limbs
Principal Investigator: Farkhatdinov, Dr I I
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Adept Ergonomics Ocado Group Shadow Robot Company Ltd
Department: Sch of Electronic Eng & Computer Science
Organisation: Queen Mary University of London
Scheme: New Investigator Award
Starts: 01 July 2020 Ends: 30 June 2023 Value (£): 373,313
EPSRC Research Topic Classifications:
Control Engineering Robotics & Autonomy
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
07 Apr 2020 Engineering Prioritisation Panel Meeting 7 and 8 April 2020 Announced
Summary on Grant Application Form
It is well-known from the biomechanics and ergonomics research that material handling tasks in industry can often cause harmful working postures, potentially leading to musculoskeletal disorders and occupational injuries. Wearable robotic systems like supernumerary (additional) robotic limbs augment human bodies with extra mobility and manipulation capabilities, and they can increase the efficiency when conducting bulky material handling tasks and allow older workers to maintain their jobs. This project aims to create novel techniques to address ergonomics and safety of supernumerary robotic limbs. A novel posture and balance support wearable robotic system will be created and its control will be integrated with the supernumerary robotic limbs for material handling. The scope of the project is to study how the ergonomics of the supernumerary limbs for material handling can be improved through additional back and balance support. The implementation will be based on creating and using innovative mechatronic technologies (soft robotic actuation and sensing; light-weight cable-driven active mechanisms; haptic feedback; human-centred interactive control) and posture assessment and data processing methods (distributed wireless sensing; Cloud data storage; personalised machine-learning based data analysis and decision-making). The outcomes of the projects will have direct impacts on the UK manufacturing, logistics and agriculture industries (>15% of GDP, employing more than 10 million people), through development and evaluation of efficient and safe material handling robotic assistive technologies.
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