Chris Mower

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

Remote control of manipulators in industry, such as concrete spraying, is generally repetitive and requires a high level of concentration and manual dexterity. Excessive cognitive loads can lead to fatigue that can become dangerous. The development of sensing devices (eg 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 workers, improved joy quality, and reduced costs.

The intention of this work is to address the scientific problem of developing computationally efficient approaches for shared autonomous control systems that blend kinesthetic operator input and sensory data that modulate the motion of a robotic device performing a manipulation task. We will develop methods on the KUKA LWR arm (fixed-base arm) and Dual UR5 Clearpath Husky (dual arm mobile-base) platforms to demonstrate the platform independence of this work.

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