Chris Mower

Research project title: 
Shared Autonomy For Kinesthetic Tools
Research project: 

 

Many industrial tasks are usually repetitive and require prolonged use of machinery and high levels of concentration that subsequently leads to operator fatigue. The development of sensing devices, such as LIDAR and RGB-D cameras, and compliant feedback will allow shared control robotic systems to improve workflow and reduce operator burden leading to a safer working environment for civil and construction engineers.

The main aims of this research can be summarised in the following question: How do we efficiently process and blend kinesthetic operator input and perception input in the form of LIDAR and RGB-D data to produce safe motion plans for robotic end-effectors that can adapt on-line in a cluttered and dynamic environment for a semi-autonomous robotic system?

The project is funded by Costain.

 

About me: 

 

Education:

MSc Computing (Visual Information Processing) from Imperial College London
MSc Applied Mathematics with Numerical Analysis from The University of Manchester
BSc Mathematics from The University of Sheffield

 

Notable Experience:

During my time at The University of Manchester (UoM) I contributed code to the Numerical Algorithms Group (NAG) Library in collaboration with Dr Craig Lucas (NAG), Prof. Nicholas J. Higham (UoM), and Dr Natasa Strabic (UoM). I authored the function named G02ANF in the Fortran programming language, see also here. For an estimated and subsequently invalid correlation matrix, G02ANF returns a valid correlation matrix subject to fixing a leading principle submatrix and applying the smallest uniform perturbation to the remainder of the input while preserving the unit diagonal. The method implements a shrinking algorithm developed by Higham, Strabic, and Sego; see paper. My dissertation, entitled Shrinking For Restoring Definiteness, was based on this work and sponsored by NAG.