The goal of this project is look at multi-agent reinforcement learning (MARL) for the optimisation of process parameters for predictive manufacturing. This project is in collaboration with Prof Moataz Attallah.

Role: Beneficiary, Principal Investigator.

Co-Investigator: Prof Moataz Attallah.

PhD Students: Ms Ilaria Lagalante, Mr Francesco Careri.

Research Assistants: Mr Ahmed Faizan, Ms Nicoletta Lambrou, Mr Sam Robbins.

Reinforcement Learning (RL) has achieved exceptional success in recent years. Recently, a prominent area of research involves the extension to multi-agent reinforcement learning (MARL). The focus of this project is on building reliable and robust human-agent teams. In particular, the aims are: i) explainability – of the agents to the human, and of the human to the agents; ii) team behaviour in multi-human and multi-agent teams.

The goal of this project is look at multi-agent reinforcement learning (MARL) to improve and automate human-agent teaming. This project is in collaboration with the Alan Turing Institute and ARL.

Role: Co-Investigator.

Principal Investigator: Prof Christopher Baber and Prof Andrew Howes.

Research Associate: Dr Aditya Acharya.

PhD Student: Mr Aju Ani Justus.

The aim of this project is to investigate multi-agent reinforcement learning (MARL) approaches in the context of swarm robotics under constrained data sharing. Resources and communication between the agents are designed to be kept to a minimum (e.g., sending only state/action pairs and rewards), taking inspiration by the minimal communication in honeybees. In particular, this projects will constitute a testbed for the theoretical development of model-free MARL and will underpin blue skies research through the use of the proposed robots.

Role: Beneficiary, Principal Investigator.

PhD Student: Mr Ziyue Chu.

The main goal of this project is to create a digital twin replica of the first floor of the University of Derby as a general university setting for COVID-19 propagation.

The project extends the previous application built for the 2020 ECR, making use of Unity3D to generate the digital twin (virtual reality).

Role: Beneficiary, Principal Investigator.

Research Supervisor: Roisin Hunt (Computer Games Modelling and Animation).

Research Assistants: Petar Cacik, Will Kitchen, Ryan Skull.

The main goal of this project is to analyse the official data and the current research on the COVID-19 pandemic in order to assess the impact of asymptomatic individuals.

We use the official data to estimate the parameters of an epidemiological model that has been formulated to predict the trends of this virus and and assess different containment strategies. 

Finally, virtual reality technology is used to provide a visual immersive environment, building on previous research on crowd dynamics and safety compliance with the government policies.

Role: Beneficiary, Principal Investigator.

Research Assistants: Matej Kapinaj, Diego Marti Mason, Alejandro Pinel Martínez.

The main goal of this project is to automate the creation of information models to support virtual reality training solutions. The KTP research associate worked in partnership with Bloc Digital, a leading company in XR solutions located in Derby.

Role: Innovate UK KTP Supervisor.

KTP Research Associate: Dr. Claire Palmer.

The ProSFeT project (http://www.prosfet.eu/PROSFET/) aims at improving logistics operations and local authority planning  within the urban freight framework. The main objectives include a review of the urban freight transport in Europe, the utilisation of stakeholders' engagement methods and the use of decision support tools.

My role in the project is to develop a decision tool (.Net/C#) which provides additional information to stakeholders in order to support the feasibility of urban consolidation centres (UCCs) in urban context. Specifically, the tool extends a traditional planner to include costs in terms of personnel, vehicles, orders, distances, etc. A key aspect captured by the tool is the calculation of the advantages of using a UCC in terms of CO2 consumption vs a direct shipping approach.

Decision makers can use the tool to test a set of different scenarios, by placing the UCC according to different sets of rules. By running the planner, the planning results can be used to estimate costs of the UCC in the specified scenario and to obtain some metrics on how to optimise logistics in the considered urban context.

The SUPERFLUIDITY project (http://superfluidity.eu/)  aims at deploying 5G in Europe. As in physics a matter is in the superfluidity state  when it behaves like a fluid with zero viscosity, so the main objectives of the project are to achieve the same in the Internet. The key points include fast instantiation of services, easy use of these services independently of where the user is in the network (whether it is in the core, aggregation or edge), and to shift them to new locations.

My role in the project was to study virtualisation through a set of container deployer softwares, like Kubernetes (https://kubernetes.io/) and nomad (https://www.nomadproject.io/). The aim was to instantiate and destroy virtual machines through both orchestrators after designing a method to interact with them on a linux machine. The performances of the proposed methods were therefore tested and compared for results.

SUPERFLUIDITY is aimed at providing "a converged cloud-based 5G concept that will enable innovative use cases in the mobile edge, empower new business models, and reduce investment and operational costs".