Congratulations (Dr.) Christopher Mower for his successful PhD Defence

Title: An Optimization-based Formalism for Shared Autonomy in Dynamic Environments

Congratulations to Christopher Mower for successfully passing his PhD viva on 29th September 2021. His thesis is entitled 'An Optimization-based Formalism for Shared Autonomy in Dynamic Environments'. 


Short lay summary: 

Christopher Mower
Christopher Mower

Construction tasks, such as concrete spraying, demand high levels of concentration and manual dexterity from an operator. Often, these tasks are prolonged and can lead to fatigue and low levels of situational awareness. This can have a damaging impact on safety and has been linked as a contributing factor to the construction sector having one of the highest levels of workplace fatalities. As such, operators require extensive and costly training to perform these tasks at a high quality whilst ensuring minimum safety standards. In the ideal case, regulators would replace this system with an autonomous robot maximizing human safety. However, current AI methods are not able to match, let alone surpass, the contextual awareness and responsive capabilities of skilled human operators.

In order to create a safer environment, improve quality, and reduce costs, this thesis explores shared autonomy. This approach aims to combine the natural aptitudes of humans with the latest advancements in multi-modal sensing, the processing capacity of computers, and systematic reasoning abilities of autonomous methods to generate optimal robot motions. Shared autonomous methods apply not only to construction, but the likes of healthcare, remote inspection, maintenance, disaster recovery, and even space and maritime exploration. The recent developments within this branch of work has generated a number of difficult research questions, that form the focus of this thesis, such as: What kind of assistance would improve task performance without impediment of operator experience? How do we model assistance that would enable a computationally efficient system, responsive enough for online teleoperation in complex environments? And, how can we leverage intention estimation and prediction, and environment sensing, to make predictions of degenerate future events and adapt control strategies early enough to prevent these from becoming a problem?

The work presented in this thesis, develops methods to address these questions in specific cases inspired by the requirements and restrictions of the construction sector. The direction taken in this work has been to leverage numerical optimization as a framework for modeling shared autonomy that maintains the features of an operators skill and experience, whilst ensuring (changing) physical and operational constraints. This thesis demonstrates the capabilities of the proposed work in realistic lab mock-ups, and evaluates the methods through rigorous, and repeatable analysis.

This PhD was supported by the Costain Group PLC.


Chris's examiners were:

Dr Nick Hawes (University of Oxford, External)

Dr Michael Herrmann (University of Edinburgh, Internal)