A major theme in the EPIC research portfolio with projects looking at decarbonisation of domestic heating, intelligent charging of electric vehicles, integration of renewable generation, self-optimising microgrids, demand side response / participation, control of low inertia / dynamic grids, efficient power electronic energy conversion, offshore wind, telecommunications and cyber security for smart grids, digital measurement and control technologies, cyber physical resiliency, protection of critical national infrastructure, energy storage, markets and gas systems. A particular focus of our activities is smart grid data analytics, where we seek to employ data analytics and machine learning techniques to address major challenges emerging in the energy industry, including wind/solar energy & load forecasting, event detection and diagnosis, oscillations source identification, and energy storage optimization to facilitate renewable energy integration.  

Autonomous vehicles will soon be on our streets, self-driving trucks will be transporting products and autonomous heavy equipment will be used in the construction industry. Autonomous ships will be navigating our oceans and UAVs will be present in our skies delivering goods, monitoring our landscape, collecting data and assisting in communications.  Such developments call for high levels of performance, reliability, safety and resilience.  Our research team works on the development of intelligent control, numerical optimisation, estimation and fault detection algorithms that will enable the deployment of safe and resilient driverless vehicles on land, at sea and in the air.  Robotics and automation offer speed, precision, and consistency, while humans offer versatility, adaptability and creativity. As we strive towards increased use of automation and robotics in manufacturing, to achieve optimal performance we aspire to have human-robot collaborative working environments where humans and robots can work seamlessly together as a team.  Through i-AMS, The Centre for Intelligent Autonomous Manufacturing Systems at Queen’s, EPIC staff are engaged in research on a range of topics in this area including, capturing, modelling and predicting human-robot interaction, developing methodologies to get robots to operate and move in a more natural human like manner, developing robot learning by observation capabilities (i.e. mimicking humans), and designing control and path planning methodologies to deliver flexible and safe multi-robot and human-robot collaborative working environments. 

The rapid advances in machine learning/AI, Internet of Things, cloud and edge computing, and the increasing levels of real-time monitoring and automation in manufacturing are at the heart of the transformation of manufacturing that is industry 4.0.  Research in EPIC in this domain is focused on the development of machine learning based approaches to process monitoring, soft sensing, predictive maintenance; simulation, optimization and control of manufacturing systems and processes; control and coordination of autonomous vehicles and robots; and VR/AR and haptics for enhanced digital manufacturing immersive experiences, with the goal of improving  productivity, flexibility, resilience, responsiveness and energy efficiency of manufacturing systems. 

Here our research is targeted towards the development of novel sensor technologies for applications in environment monitoring, biological sample analysis, food sciences, navigation, and measurement science, and the resource management and optimisation of wireless communication for sensor networks and IoT. Through, QAMEC, our Advanced MicroEngineering Centre we engage in the creation and characterisation of various types of sensors for greenhouse gas detection, remote atmospheric sensing and biomedical applications.  

Innovations in VR, AR, and MR are driving the advancement of human-computer interaction, in which audio is a crucial component for application in the creative industries. Collaborative research with SARC and the School of Psychology seeks to advance the aural modality in such next-generation interactive digital systems. A particular focus of our activities is virtual-acoustic instruments, which enhance immersion through rendering a sonic response to human inputs based on real-time simulation of acoustic behaviour. This involves research on numerical methods for simulation and synthesis of musical instruments and other sounding systems in conjunction with the design of related sensor interfaces, parameter estimation and signal processing methods, and experimental procedures for auditory-motor analysis.