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 intention of this work is to address the problem by developing computationally efficient approaches for shared autonomous control systems that blend kinesthetic operator input and sensory data to produce adaptive motion plans for robotic devices set in a cluttered and dynamic environment. Additionally, the system must incorporate some reasonable feedback method to the operator.

The project is funded by Costain and EPSRC.


  • Wolfgang Merkt, Yiming Yang, Theodoros Stouraitis, Christopher Mower, Maurice Fallon and Sethu Vijayakumar, Robust Shared Autonomy for Mobile Manipulation with Continuous Scene Monitoring, Proc. 13th IEEE Conference on Automation Science and Engineering, Xian, China (2017).


About me: 


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 potentially invalid correlation matrix, G02ANF returns a valid correlation matrix subject to fixing a leading principle submatrix by 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.

Student type: 
Aligned student