ASAR has internationally recognised research excellence in Robotics & Artificial Intelligence (RAI). It was appointed the home of the National Advanced Robotics Research Centre in 1987 and was the founding member of the Northern Robotics Network, which became the National Robotics Network (NRN). ASAR is a spoke of the EPSRC Future AI & Robotics for Space Hub, a UK national centre of research excellence in space robots and AI funded through the Industrial Strategy Challenge Fund.
ASAR is at the forefront of strategic research initiatives funded by the EPSRC, the EU Framework Programmes for R&I, UK Government Departments (BEIS and Defra) and industry.
Our research has been applied in a range of sectors including automotive and aerospace manufacturing, nuclear decommissioning, space exploration, food production, agriculture and in health and social care.
In 1987, the University of Salford was the founding site for the UK’s National Advanced Robotics Research Centre (NARRC). Since then, robotics has formed the major strategic direction in Engineering within the University, thus, offering the Centre the opportunity of leading collaborations with other research units.
The Centre - currently led by Professor Samia Nefti-Meziani - has strong national and international connections with both other research institutes and the industry, with specific regard to manufacturing and partners, particularly in the Aerospace and Food sectors. Salford researchers have been at the forefront of strategic national developments initiated by several organizations in different areas of interest including: the Department of Trade and Industry (DTI), the Ministry of Agriculture, Fisheries and Food (MAFF), the Department of Business Innovation and Skills (BIS), the Department for Environment, Food and Rural Affairs (DEFRA), and the Engineering and Physical Sciences Research Council (EPSRC). Also, the Centre participated to multiple international initiatives from the European Union. Currently, the Centre is host for two government sponsored networks in Robotincs and in Automation, as part of the DEFRA-sponsored Food Manufacturing Engineering Group.
Moreover, the Centre for Robotics and Automation has a strong history in transferring its expertise to students. Taught course activities at undergraduate level in the area of robotics began in 1995 and an M.Sc. programme in Robotics and Automation started in 1996. The M.Sc. programme links closely with M.Sc. courses in Intelligent Machines and Mechatronics. The current annual intake onto the M.Sc programme in Robotics and Automation is 12-15 students per year.
In addition to taught courses, the Center has a unique offer of different Ph.D. programmes towards Master of Philosophy (M.Phil.), Doctor of Philosophy (Ph.D.), and Professional Doctorate (D.Prof.), with approximately 80-100 students involved in robotics research activities covering a wide range of topics and applications.
Over the years, the Centre developed a unique expertise in several areas in the field of Robotics and Automation, which, in turn, offer a unique advantage in terms of both basic and applied research. The Centre focuses its research on automation systems, robot and machine design, dexterous end effectors, legged robots and walking systems, soft robotics, biomimetics and biologically-inspired robots, haptics and telepresence, physical human-robot interaction, rehabilitation robotics, cognitive robotics and autonomous systems, uninhabited autonomous systems and unmanned air vehicles, and food automation. In addition, the Centre gained expertise in adjacent research topics and is extremely open to leveraging its know-how and facilities to explore other topics and to support multidisciplinary research in different areas.
The Centre for Robotics and automation focuses on the following areas:
Actuators – As robots are being increasingly used in domains other than manufacturing the traditional hydraulic, pneumatic and electric actuators are not always suitable. Salford has been developing new advanced actuators which provide improved performance, such as high power to weight ratio and variable stiffness.
Biomimetics/biologically inspired robotics – This theme involves looking to nature to see how it solves problems and then attempting to use this inspiration to develop novel robotic systems. We have developed a number of biologically inspired robots most notably walking robots based on both canine and primate anatomy.
Soft robotics – Traditional robots are typically metallic and as a result are heavy and rigid, soft robots, as the name suggests, are formed from much softer and more flexible materials. This means soft robots interact with the environment in a very different manner to traditional robots, they can deform when in contact with obstacles allowing them to perform tasks and work in environments previously unsuited to robots. The ability to deform and absorb impact also make soft robots inherently safe when operating near people.
End effectors – Many products, particularly items of food cannot be grasped with traditional end effectors. We have extensive experience in developing novel end effectors for grasping difficult to hand products.
Dexterous hands – Robots are multipurpose tools, however, traditional robot grippers tend to only be able to handle a single or small range of products. If the robot is retasked the end effector often needs to be changed. This is costly and time consuming and as a result there is a drive to develop multipurpose grippers. We have extensive experience develop dexterous grippers based on the human hand.
Automation for the food industry – The food industry uses less automation than other manufacturing sectors. The main reasons for this are the fact that food products are typically deformable and they have high levels of natural variation. This means traditional automation is not suitable. We have worked with more than 50 food companies to explore the use of automation and have developed many novel automated systems.
Physical human robot interaction (pHRI) – In the future robots will operate close to and in collaboration with people. Existing robots are unable to achieve this in safe manner and we are developing both hardware and software systems to allow safe physical human robot interaction.
Rehabilitation robotics – With an aging population and pressures on health budgets robotics is becoming increasingly important in healthcare. We are developing exoskeletons and other robotics devices to assist with rehabilitation.
The Marie Curie Initial Training Network, SMART-E (Sustainable Manufacturing through Advanced Robotics Training in Europe), coordinated by the University of Salford, has launched a new European research and training programme on Advanced Robotics under the European Union programme FP7-PEOPLE-2013-ITN with a total budget of approximately €4 million. SMART-E will develop a leading European Doctoral Training programme, training 15 high calibre Researchers in the areas of Dexterous, Soft and Compliant Robotics in Manufacturing; Reconfigurable and Logistics Robotics; and Safety & Human Robot Interaction.
Coordinated by Prof Samia Nefti-Meziani, SMART-E brings together a team of world-renowned experts in robotics from leading universities in the UK, Germany, Italy and Switzerland ((University of Salford, University of Sheffield, Technical University of Munich, Scuola Superiore Sant'Anna, Italian Institute of Technology,University of Zurich) and a world leading manufacturers and end users of Automation in the Aerospace and Food sectors including FESTO, AIRBUS and the Advanced Manufacturing Research Centre (AMRC). The team is supported by a number of additional leading Universities, research laboratories and industries as associated partners.
The Autonomous Systems & Robotics Research Centre holds the leading role in Autonomous mission planning and management, task allocation, hybrid optimisation, and intelligent decision making in the GAMMA Programme (Growing Autonomous Mission Management Applications).
Lead Partners in this programme include North West Aerospace Alliance (NWAA) and BAE Systems, together with the Universities of Salford, Manchester, Lancaster, Liverpool, UCLAN, Liverpool (including the Virtual Engineering Centre), and National Nuclear Laboratories.
GAMMA is a three year £9.1 million Autonomous Systems programme aimed at driving SME engagement and developing technology within the emerging autonomous systems markets. GAMMA technology areas of interest include data management, image processing, sensing and communication and mission planning and management.
Development of a novel Soft, variable stiffness continuum manipulator.
The future of manufacturing will likely require robots working close to, and in collaboration with humans. This has led to the development of ‘soft’ and compliant robotic systems which mimic the soft interaction between people. This study is developing a physically soft manipulator able to vary its stiffness depending upon its task requirements. This will enable it to operate in a safe low stiffness mode when moving large distances but switch to a high stiffness mode to allow for precise/accurate position control. This will result in a robot manipulator which unlike most ‘soft’ systems is able to achieve high levels of accuracy.
One of the main challenges of robotic grasping is preventing slippage while manipulating objects. Slippage can cause the grasped object to fall and break, which might lead to a grievous situation especially if the handled object is expensive or contains hazardous substances. Also, grasping an object with sufficient force to prevent slippage, whilst not damaging or deforming the shape of the object proves to be an intricate challenge despite the existence of a huge body of literature on robotic grasping. Saber’s research focuses at designing a nonlinear impedance-slip control for robotic hands, grippers and end effectors. When grasping objects, the controller should robustly overcome external nonlinear disturbances, along with inaccuracies in the system model, preventing slippage and minimizing deformation of objects.
Current sensing technologies are very challenging to implement over 3D surfaces, sometimes expensive and difficult to replace, while a soft and low-cost solution able to reproduce some of the properties of our skin is needed, especially on high-deformable areas as the robotic-joints. In this way it is possible to enable and maximise the quality of the robot interactions’ with the surrounding environment.
This work describes an initial step towards the realisation of a stretchable and deformation-responsive “sensitive skin” for reproducing the human sensing capabilities in robotic applications. We are developing a sensor as a pressure sensitive fabric material which responds to external stimuli by changing its electrical conductivity. The stretchable sensor is surrounded by electrodes for the electrical circuit and in this way, since it does not present internal wires, it is extremely soft and stretchable. When an external stimulus is applied, the variations in the internal conductivity of the sensitive skin will change the distribution of the injected electrical current inside it, resulting in a variation of the measured voltages at the boundary. The collected potentials are then sent to a software for reconstructing the image of the internal conductivity distribution.
So far a second prototype of the experimental set-up with PCB has been developed. We have been working on the voltage data and study the performance parameters for the optimisation of the drive patterns. We have demonstrated that, depending on the present stimuli position over the conductive domain, the selection of electrodes on which current injection and voltage reading are performed, can be chosen dynamically resulting in an improved quality of the reconstructed image and system performance. At the moment we are focusing on developing reconstruction algorithms to improve the time and spatial resolution of the sensor and to achieve touch interpretation.
My current research is on developing a novel self-healing framework for Robots and Complex machinery. The key behind this framework is the mathematical modelling of the degrading components within a mechanical system, where the wear interactions between components are taken into account, leading to a more accurate indication of system health, and thus maintenance actions can be scheduled accordingly minimising human intervention, increasing the autonomous aspect of the system and giving the appearance of self-healing. The self-healing framework will start from the diagnostics and prognostics of a system; it uses a data-driven approach, using statistical modelling and machine learning. It works by classifying normal response of a system under a specific operation scheme, which then makes it easier to identify incipient faults; this will also lead to more accurate remaining useful lifetime calculations, and so prevent downtime that leads to economic losses and in some cases human casualties.
Recently, UAVs are promising to be a cost-effective and safe approach to improve awareness in any given environment. Although UAV technologies are reliable in gathering and sharing information with base stations, current technologies are limited to short flights. Hence available multicopters are not permissive. Murad is currently working on a self-regulated, fail safe hybrid propulsion system which will improve the flight endurance increasing the effectiveness in battle scenarios.
Designing a multi-robot system provides numerous advantages for many applications, such as low cost, multi-tasking and more efficient group work. While the rigidity of the robots used in industrial and medical application increase the probability of risk of injury. Therefore, many researches are done to increase the safety factor for robot-human interaction, as a result, either the separated between the human and robot is suggested or the force shutdown to robot system is applied. These solutions might be useful for industrial application but it is not for medical and the application require the direct interaction between the human the machine. To overcome the rigidity problem, a soft robot arm is presented. Ala aims to design and build multi soft robot system and design a suitable cooperative control system.
Mohd Nadhir Ab Wahab
Swarm Intelligence (SI) is one of the prominent techniques employed to solve optimisation problems. It has been applied to problems pertaining to engineering, schedule, planning, networking and design. However, this technique has two main limitations. First, the SI technique may not be suitable for real-time application, as it does not have the same aspects of limitations as a real-time platform. Second, setting the parameter for SI techniques to produce the most promising outcome is challenging. Therefore, this study has been conducted to overcome these two limitations. Based on the literature, Particle Swarm Optimization (PSO) was selected as the main SI for this study, due to its proven performances, abilities and simplicity. Five new techniques were created based on the PSO technique in order to address the two limitations. The first two techniques focused on the first limitation, while the other three techniques focused on the latter. Three main experiments (benchmark problems, engineering problems, path planning problems) were designed to assess the capabilities and performances of these five new techniques. These new techniques were also compared against several other well-established SI techniques such as the Genetic Algorithm (GA), Differential Equation (DE) and Cuckoo Search Algorithm (CSA). Potential Field (PF), Probabilistic Road Map (PRM), Rapidly-explore Random Tree (RRT) and Dijkstra’s Algorithm (DA) were also included in the path planning problem in order to compare these new techniques’ performances against Classical methods of path planning. Results showed all five introduced techniques managed to outperform or at least perform as good as well-established techniques in all three experiments.
Wajid Rasheed Ismaeel Al-Rikabi
Fuzzy type-2 controllers can easily deal with systems nonlinearity and utilise human expertise to solve many complex control problems; they are also very good at processing uncertainty, which exists in many robotic systems, such as autonomous vehicles. However, their computational cost is high, especially at the type reduction stage. In this research, we aimed to reduce the computation cost of the type reduction stage, thus to facilitate faster performance speed and increase the number of actions able to be operated in one microprocessor. Proposed here are adaptive integration principles with a binary successive search technique to locate any straight sections in fuzzy sets, then using them to cut the cost of the weighted average computation, which runs in many type reductions. A variable adaptation rate is suggested during the type reduction iterations to reduce the computation cost further. The influence of the proposed approaches on the fuzzy type-2 controller’s error has been mathematically analysed and then experimentally measured using a wall following behaviour, which is the most important action for many autonomous vehicles. Results show a performance time-gain exceeding 200%.
This study develops a new accelerated version of the enhanced Karnik-Mendel type reducer by using better initialisations and better indexing scheme. The resulting performance time-gain reached 170%, with respect to the original version. A further cut in the type reduction time was achieved by proposing a One-Go type reduction procedure. This technique can reduce multiple sets altogether in one pass, thus eliminating much of the redundant calculations needed to carry out the reduction individually. All the proposed type reduction enhancements were evaluated in terms of their execution time-gain and performance error using every possible fuzzy firing level combination. Tests were then performed using a real autonomous vehicle navigating in a relatively complex arena field with acute, right, obtuse, and reflex angled corners, to assure a wide variety of operation conditions. A simplified state hold technique using Schmitt-trigger principles and dynamic sense pattern control was suggested and implemented to assure small rule base size and to obtain a more accurate evaluation of the type reduction stages.
This study focuses directly on analysis and evaluation of human gait features such as spatial gait data, temporal gait data, spatiotemporal gait data, and kinematic gait data. The changes in gait features can be used for assisting in clinical diagnoses especially in cognitive diseases such as; multiple sclerosis. In this study, Microsoft Kinect v2 has been chosen to collect data form participants who are instructed to walk about 3 meters in the front of the camera which can provide data as 3D skeletal numerical data for 25 joints.
Loai Al Abeach
This research presents the design of a variable stiffness, soft, three fingered dexterous gripper. The gripper uses two designs of McKibben muscles. Extensor muscles which increase in length when pressurised are used to form the fingers of the gripper. Contractor muscles which decrease in length when pressurised are then used to apply forces to the fingers via tendons which cause flexion and extension of the fingers. The two types of muscles are arranged to act antagonistically and this means that by raising the pressure in all of the pneumatic muscles the stiffness of the system can be increased without a resulting change in finger position. The research presents the design of the gripper, some basic kinematics to describe its function and then experimental results demonstrating the ability to adjust the bending stiffness of the gripper’s fingers. It has been demonstrated that the finger’s bending stiffness can be increased by over 150%. The research concludes by demonstrating that the fingers can be closed loop position controlled and are able to track step and sinusoidal inputs.
In addition, three more end effectors are developed in this research as well. Two of them are modified variable stiffness, pneumatic, soft robot gripper designed to enhance the performance of the previous one. The third once which is variable stiffness too, however, its constructed using granular jamming criteria and its work depends on the negative pressure in contrast with the previous three soft grippers.
Soft wearable robots are efficient alternatives to rigid-frame exoskeletons because they are compact and lightweight. We are manufactured a wearable robot for human upper-limp power assist and rehabilitation because a physically handicapped elderly and disabled people can expect to live more independent life by using this kind of devices. We are developing a suitable intelligent control to control this device efficiently.
The Center collectively attracted over 30 research grants including:
Prof. Nefti-Meziani holds Doctorat D’etat in robotics and artificial intelligence and is Director of the Centre for Autonomous Systems & Advanced Robotics, and Chair of Robotics at the University of Salford. In this role, she leads a multidisciplinary team of 6 academics and 12 researchers in automation, robot/machine design, dexterous end effectors, legged robots, soft robotics, biologically-inspired robots, haptics/telepresence, physical human-robot interaction, rehabilitation robotics, cognitive robotics, uninhabited autonomous systems. She is an internationally leading researcher in embodied intelligence and cognition. She has 25 years’ experience in advanced theoretical research in the areas of embodied intelligence, advanced robotics where the focus of her contribution is in the development of concepts, mechanisms and algorithms. She has pioneered the first application of Soft Robotics in manufacturing.
She has published extensively in the above areas and the rigorous quality of her publications in AI and Robotics is evidenced by their high impact factors in Journals. The application value of her research has attracted significant national media coverage on Sky, the BBC, ITV, Granada, in addition to print and on-line media. She has wider practical cross sectorial technologies including Nuclear Food, Agriculture, Nuclear, Aerospace & Defence, and Healthcare through several projects.
She has also extensive leadership experience as a former Director of the Doctoral School of the 6* IRIS Research Institute (2005-08). She has been responsible for 150 doctoral students and all postgraduate research provision across various departments, and has nurtured a strong research culture and environment, which received the highest award for research environment (4*) in the 2008 (RAE). She has successfully supervised and graduated more than 30 PhD students and has extensive experience running very successful industrial sponsored robotics PG programmes at national and international level which attract more than 100 post-graduate students every year.
Prof. Nefti-Meziani is a proven strategic leader of multi-national, multi-sector, multi-faceted and complex Robotics & Artificial Intelligence research programmes. She is renowned for her extensive experience of leading and managing large scale multi-disciplinary research projects, funded by EU, involving multiple academic and industrial partners. Examples include the research and training programme SMART-E (€4m), for which she is the PI and which included 20 partners, comprising 7 academics and 13 companies, RobotCub (€8.5m), NovelQ (€11.3m) and ASTRAEA(£32m) and also the GAMMA (£9m) project that engaged 50 SMEs with 46 research proposals. She was also heavily involved in the management and delivery of CENFRA (£5m) and ASCEII(£16m. Through these programmes, and her role as an expert in robotics, she has worked very closely with food, aerospace and nuclear supply chain to deliver proof of concept and innovative low-cost robotics solutions for these programmes. She has also managed and delivered other projects funded by innovateUK and EPSRC as PI and Co-I.
She is co-founder of the Northern Robotics Network (NRN), which is an associate partner of the RAS-SIG. Its membership includes a range of leading nuclear sector organizations. She has worked seamlessly with the partners of the NRN’s industrially led Extreme Environment Cluster across a wide cross-section of industry sectors. This work has been part of the consultation discussion with EPSRC and innovate UK, which has helped inform the area of focus of the Industrial Strategy Challenge Fund. She is Vice Chairman of IEEE Robotics and Automation UK & RI, Associate Editor of IEEE Transactions on Fuzzy Systems, served as Advisory board member for Chamber of commerce in France, the Asian council and the EPSRC centre for innovative manufacturing and Member of the Engineering and Physical Sciences Research Council (EPSRC) Peer Review College.
Project name: KinectING FRAILTY
Medical Research Council Confidence in Concept (CiC) ROUND 4 2016: Full application, £ 91,021
Start date: 01/01/2017
Project name: MIHome
Salford Royal Trust and SallixHome
Capital funding scheme, £ 352,586
Start date: 01/10/2016
Project name: Marie Curie-Fellow
EC (Framework), £70.000
Principal Investigator: S Nefti-Meziani
Start date: 06/2017
Project name: Robotics and Autonomous systems “In Touch”
In Touch Ltd, £60,000.00.
Principal Investigator: S Nefti-Meziani (100%).
Start date: 08/2016
Project name: Autonomous Swarm-Based Mission Planning and Management System
Minister of Defence, £36,916.00.
Principal Investigator: S Nefti-Meziani (100%).
Start date: 02/2016
Project name: EPSRC Centre for Innovative Manufacturing in Intelligent Automation - Feasibility Study
Principal Investigator: S Nefti-Meziani (50%). Co-Investigator: S Davis (50%).
Start date: 05/2015
Project name: KTP with HellermannTyton Ltd
Technology Strategy Board, £152,176.00.
Principal Investigator: S Nefti-Meziani (50%). Co-Investigator: S Davis (50%).
Start date: 05/2014
Project name: Sustainable Manufacturing through Robotics Training in Europe (SMART-E)
EC (Framework), €4M.
Principal Investigator: S Nefti-Meziani (83%). Co-Investigators: S Davis (10%), P Scarf (7%).
Start date: 10/2013
Project name: GAMMA: Growing Autonomous System Mission Management Applications (£9M)
Regional Growth Fund, £312,137.00.
Principal Investigator: S Nefti-Meziani
Start date: 01/2012
Project name: The Aerospace Supply Chain Excellence (ASCE) 2 Programme ( £16M)
Principal Investigator: S Nefti-Meziani
Start date: 06/2012
Project name: Challenging established rules for train control through a fault tolerance approach
Principal Investigator: T Mei (66%). Co-Investigator: S Nefti-Meziani (34%).
Start date: 02/2010
Project name: ASTRAEA T7
BAE Systems, £20,000.00.
Investigator: S Nefti-Meziani (100%).
Start date: 08/2008
Project name: NovelQ - Novel processing methods for the production and distribution of high quality and safe foods
Investigators: S Nefti-Meziani (35%), J Gray (50%), D Caldwell (15%).
Start date: 03/2008
Project name: Academic Fellowship 2004 (Virtual Environments and Future Workspaces Research Centres)
Investigator: S Nefti-Meziani (100%).
Start date: 06/2007
Project name: The portal for projects and communities in the virtual organisation domain (VE-Forum)
EC (Framework), £136,106.00.
Investigators: S Nefti-Meziani (30%), G Cooper (30%), Y Rezgui (40%).
Start date: 01/2005
Dr Steve Davis graduated from the University of Salford with a degree in Robotic and Electronic Engineering in 1998 and an MSc in Advanced Robotics in 2000. He then became a Research Fellow gaining his PhD in 2005 before moving to the Italian Institute of Technology in 2008 as a Team Leader. He returned to Salford in 2012 as a Lecturer in Manufacturing Automation and Robotics. His research interests include soft robotics, actuators, biologically inspired systems, biomimetics, humanoid robots, end-effectors and dexterous hands, industrial automation and automation for the food industry. He has published extensively with more than 63 publications, many in high impact factor journals such as SoRo. He has also co-authored chapters in two books on automation and has two patent. In addition he contributed to the UK-RAS White Paper “Agricultural Robotics: the Future of Robotic Agriculture” and has produced 40+ confidential reports for food, and other, companies highlighting to them potential areas of automation. These companies have ranged from small family businesses through SMEs to large multinational organisations. Dr Davis has been associate editor at several IEEE conferences and was Guest Editor of special editions of the journal Actuators. He has experience of attracting research funding, most notably the €3,948,470 Marie Curie Initial Training Networks (ITN) SMART-E (Sustainable Manufacturing through Advanced Robotics Training in Europe).
Dr Theo Theodoridis holds a PhD degree on crime recognition surveillance robots from the University of Essex, UK. He worked as a postdoctoral research officer (EPSRC grand) on multimodal human-robot interfaces, visual guidance as well as pattern recognition control methods at NASA Jet Propulsion Laboratory (JPL). Currently he is working as a lecturer in robotics, mechatronics and advanced embedded systems in the school of CSE at Salford University in Manchester, UK, and he is an active researcher and a supervisor of several PhD students. He has been a principal and co-investigator of several research grants and attracted funding at a national level (Regional Growth Fund: CO-I, £312K, Medical Research Council: CO-I, £91K, Innovate UK: CO-I, £50K, BBC RDF-TV: PI-I, £10K, and CST RDF Fund: PI-I, £6K). He is also a honorary visiting professor in Mechatronics at Manipal University – Dubai, and the inventor of the home service robot Carebot that has participated in a BBC documentary attracting significant national media coverage. Also, Dr Theodoridis is a reviewer and author of several leading journals of the field such as the IEEE transactions on Systems Man and Cybernetics, Human-Machine Systems, and Evolutionary Computation.
Dr Saber Mahboubi received the bachelor’s degree in power engineering from the University of Rajaee, Tehran, Iran, and the M.Sc. degree in mechatronics from the University
of Tabriz, Iran. Before his Ph.D., he worked as a researcher fellow with the Max Planck Institute for Biological Cybernetics, Germany. He then became a Marie Curie research fellow gaining his PhD in Robotics from the University of Salford, UK. He is currently
a post-doctoral research fellow at the centre of Autonomous System and Robotics, University of Salford.Saber’s research interests include biologically inspired systems, variable impedance mechanisms, robust control, Robotic manipulation and artificial intelligence.
Dr Majeed Soufian (MIEEE) has a history of successful research collaboration and funding alongside a commitment to teaching and development of young researchers and PhD students. His research collaborations include both academic, industrial and government agency partners. He obtained his degrees from UMIT (University of Manchester), MMU and University of Oxford. Dr Soufian has published with others over 70 research articles in international conferences and journals with a research interest in eSystem engineering, robotics, machine learning and AI, computer science and software with applications in modelling, control, autonomous vehicle navigation, IoT, data science, pharmaceutical, medical microbiology, health, nuclear, Cybersecurity of industrial automation and autonomous systems especially CAV (Connected and Autonomous Vehicle) and so on. He has reviewed articles for many scientific publications for Chapman & Hall, SAGE, Elsevier, ACS publication, Sciedu press and sits on the International Editorial Review Board of Artificial Intelligence Research (AIR), and International Programme and Steering Committees of "Developments in eSystems Engineering (DeSE)". He has had and been involved in a grant portfolio of over £1.3M funded by major research councils, DTI, industrial partners and universities.
His industrial experience also includes establishing and managing an industrial R&D group for design and development of an electro-optic sensor and semi-automatic navigation system of an autonomous mobile object, and over four years in Numerical Algorithms Group where his expertise were utilised in the development and porting of numerical algorithms (the .Net optimisation routines that can be used for machine learning) with some HPC (High Performance Computing) practices. He has also worked as a visiting scientist in STFC (Science and Technology Facility Council), Daresbury Laboratories toward a feasibility study of multiscale modelling and developing a distributed system software for it.
S. Davis, D.G. Caldwell, “Handling Device” WO 2007052018, 10 May 2007.
We attract students from all walks of life and this helps to create a vibrant and dynamic postgraduate research environment.
Research degrees offer you the flexibility to carry out your own research project under direct supervision of an academic member of staff. You can study towards a Master of Philosophy (MPhil), Doctor of Philosophy (PhD), Professional Doctorate (DProf) or MSc by Research.
Our students have access to a wide range of supervisory expertise, training and excellent facilities. The research training you receive will be tailored to your particular needs, which your supervisory team will discuss with you as soon as possible after you arrive.
Our current students undertake research across a variety of topic areas from Actuators, Biomimetics/biologically inspired robotics, Soft robotics, End effectors and dexterous hands, Automation for the food industry, Physical human robot interaction (pHRI), and Rehabilitation robotics.
If you are interested in applying for the doctoral programme, first of all contact the relevant Research Group Lead to discuss your proposed research topic and to identify a Supervisor. You would also need to prepare a Research Proposal (approximately 1500 words) before you apply online.
For more information, please contact:
Catriona Nardone (Research Support Officer)
0161 295 3482