The group will be represented by Prof. Bruce MacDonald and Dr Pau Medrano-Gràcia from The University of Auckland and Prof. Mike Duke from the University of Waikato. The seminar will summarize their research partnership to create robotics technology for kiwifruit and apple orchards, with the Universities of Auckland and Waikato, New Zealand’s Plant and Food Research Institute, and New Zealand company Robotics Plus. This includes an autonomous mobile robot, a targeted pollination system for kiwifruit flowers, a four-arm harvester for kiwifruit, and a prototype apple harvester.
The performance of autonomous robots, i.e. robots that can make their own decisions and choose their own actions, is becoming increasingly impressive, but most of them are still constrained to labs, or controlled environments. In addition to this, these robots are typically only able to do intelligent things for a short period of time, before either crashing (physically or digitally) or running out of things to do. In order to go beyond these limitations, and to deliver the kind of autonomous service robots required by society, we must conquer the challenge of combining artificial intelligence and robotics to develop systems capable of long-term autonomy in everyday environments. This talk will present recent progress in this direction, focussing on the mobile robots for security and care domains developed by the EU-funded STRANDS project (http://strands-project.eu) which have so far completed over 106 days of autonomy in real service environments. In particular the presentation will cover our approach which combines probabilistic verification and machine learning to produce a planning system which controls how the robots select and execute their tasks over these extended periods of autonomy.
Nick Hawes is an Associate Professor of Engineering Science in the Oxford Robotics Institute at the University of Oxford. His research applies techniques from artificial intelligence to allow robots to perform useful tasks for, or with, humans in everyday environments (from moving goods in warehouses to supporting nursing staff in a care home). He is particularly interested in how robots can understand the world around them and how it changes over time (e.g. where objects usually appear, how people move through buildings etc.), and how robots can exploit this knowledge to perform tasks more efficiently and intelligently.
The annual conference provides a fantastic opportunity for research and addresses the design, development and use of interactive technologies for children.
Dr Rubegni is a well established member of this community and was paper chair of the conference. She presented her research on children as authors of digital storytelling and how to increase their awareness on gender issues.
Grace also presented a work in progress paper on how to maintain long-distance relationships with children by exploring autobiographical designs and life-logging.
Principal Investigator, Dr Bashir Al-Diri and colleagues at the School of Computer Science are conducting a research project to collect 2D fundus images and 3D Optical Coherence Tomography (OCT) images using a fully automated user-friendly retinal-imaging camera (3D OCT-1 Maestro).
This study, titled ‘Automated Retinal Imaging Lab (ARIAL), will look towards finding and analysing new signs in the retinal vascular system photographed at the back of the eye, which might be changed due to disease. These signs can then be monitored and measured over time to detect signify disease progression.
OCT images are the most common techniques used for detecting eye diseases affecting the macula; OCT images are using in routine clinical practice and for diagnosis and monitoring diseases such as diabetes and high blood pressure as well as other systemic diseases.
All images will be reviewed and stratified by a Consultant Ophthalmologist Surgeon. Any abnormality will be reported directly to you and your registered GP with clear advice on further action if needed.
For this study, we welcome everyone with or without any known eye disease or diagnosed with any chronic systemic diseases. OCT images and lifestyle data will be captured and collected every 6 months for the duration that you are available; each visit will take no longer than 30 minutes.
There has been no such dataset available for the research community in the past, so this project will be of great scientific interest.
Further information can be found here. To participate, please contact Dr Bashir Al-Diri: