Since the early 1990’s, robots have been used to aid the treatment of people with neuromuscular disabilities and soft robotics offers a unique platform due their inherent conformability to the body and enables safe human-device interaction. Previous studies showed that soft pneumatic actuators (SPAs) have great potential to build wearable devices for rehabilitative purpose. A general approach to manufacture SPAs is based on using elastomeric materials such as silicone and rubber; and then pneumatic pressure is employed to power actuators. Although elastic materials offer some superior properties, some properties of elastomeric materials – material density, stiffness, and strength - present challenges in wearable applications. As the soft robotics domain ventures into more comprehensive and demanding applications, sensor information becomes key to achieve high task performance, and thus the seamless integration of soft actuating and sensing parts is needed to achieve a continuum of sensing and actuation. Sensors also need to have similar material properties (modulus, extensibility) to be used for the actuator themselves in order to not to hinder the actuator’s performance. To address the challenges mentioned above, I will employ textile materials to achieve both sensing and actuation and computerized flatbed knitting technology will be primarily utilized for the fabrication of such structures and soft robotic glove as a hand rehabilitative device will be constructed. The combination of the acquired new technical skills, the advanced training received, the research management experience and the international and inter-sectoral mobility of this fellowship will significantly diversify my competences. This will enhance my capacity to pursue an independent academic career, i.e., to build my own lab after the fellowship and to apply for international grants both in soft robotics and wearable technologies.
Sewage sludge management is one of the most challenging waste issues in Environmental Engineering field due to their highly polluted nature and high volume. On the other hand, sewage sludge, which is also called as biosolids, can be a nutrient resource for the agri-foods which leads to their usage in agriculture as fertilizers. Usage of biosolids as fertilizer is the most desirable management strategy since it provides resource recovery and prevents the usage of synthetic chemicals which can be harmful for human and environment health. Therefore, after the treatment of biosolids to meet the required quality criteria, their ultimate disposal as fertilizer has to be sustained. Up to date, organic, metallic or hazardous pollutants were addressed to be removed from biosolids before land spreading and standards were defined in regulations. However, there are new concerning pollutants for biosolids application to agricultural lands. One of the most recent and biggest concerns is the presence of silver nanoparticles (AgNPs), which are widely used as biocides in the consumer products and are shown to occur in wastewater treatment plants, mostly, in biosolids. They are, as a source of ionic and nano Ag, among the emerging pollutants which are known to have potential to pose threat to human and environment health and have not been included in monitoring lists, yet. Therefore, the aim of this research is to advance the state of the art in land spread of biosolids through the investigation of the toxic effects and bioacculumation of AgNPs and its transformation products in soil organisms. For excellence in research, the knowledge about the toxic effects is integrated with environmental risk assessment which will enable necessary actions such as proposing guidelines for biosolids applications.
Thousands of hectares of forest lands are lost to wildfires every year. Utilization of Unmanned Aerial Vehicles (UAVs) is an efficient tool for fighting fires, however the state-of-the-art techniques lack in ability to predict fire spread direction and coordinate multiple UAVs to suppress the fire under limited communication. DUF project aims to apply powerful tools from artificial intelligence domain to UAV firefighting problem, creating an innovative solution for autonomous firefighting, which will reduce the amount of lands lost to fires. DUF will use the deep learning techniques for estimating the fire spread direction from infrared camera streams obtained from UAVs. Deep learning is a mature technology for classical image recognition, but the use of deep learning to learn predictive models for fire spread is a novel approach. After the model is learned, a decentralized approximate dynamic planning algorithm will be utilized to coordinate UAV actions for suppressing the fire. The algorithm development, simulations and first phase of the flight experiments will be conducted at Istanbul Technical University (ITU) Aerospace Research Center (ARC). The project will conclude with flight tests conducted on natural forest fires, with operational support from Forest of Ministry of Turkey. Prof. Ure earned his Ph.D. degree from Massachusetts Institute of Technology, working on advanced UAV projects and collaborating with leading researchers in the world. He has extensive experience on autonomous systems and published more than 30 critically acclaimed journal and conference papers in this subject. Prof. Ure is currently working as assistant professor in ITU and through this innovative multidisciplinary research and with the help of experimental infrastructure provided by the ITU, Prof. Ure is expected to gain maturity in managing research projects and advance his career toward being an esteemed professor in the field of aeronautics and artificial intelligence in Europe.