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Details of Grant 

EPSRC Reference: EP/I005021/1
Title: Life-Long Infrastructure Free Robot Navigation
Principal Investigator: Newman, Professor PM
Other Investigators:
Researcher Co-Investigators:
Project Partners:
BAE Systems Department for Transport Guidance Navigation Ltd.
Massachusetts Institute of Technology Navtech Radar Limited Nissan
Department: Engineering Science
Organisation: University of Oxford
Scheme: Leadership Fellowships
Starts: 01 October 2010 Ends: 31 March 2016 Value (£): 1,655,490
EPSRC Research Topic Classifications:
Artificial Intelligence Robotics & Autonomy
Transport Ops & Management
EPSRC Industrial Sector Classifications:
Transport Systems and Vehicles
Related Grants:
Panel History:
Panel DatePanel NameOutcome
09 Jun 2010 EPSRC Fellowships 2010 Interview Panel A Announced
Summary on Grant Application Form
In the future, autonomous vehicles will play an important part in our lives. They will come in a variety of shapes and sizes and undertake a diverse set of tasks on our behalf. We want smart vehicles to carry, transport, labour for and defend us. We want them to be flexible, reliable and safe. Already robots carry goods around factories and manage our ports, but these are constrained, controlled and highly managed workspaces. Here the navigation task is made simple by installing reflective beacons or guide wires. This project is about extending the reach of robot navigation to truly vast scales without the need for such expensive, awkward and inconvenient modification of the environment. It is about enabling machines to operate for, with and beside us in the multitude of spaces we inhabit, live and work. Even when GPS is available, it does not offer the accuracy required for robots to make decisions about how and when to move safely. Even if it did, it would say nothing about what is around the robot and that has a massive impact on autonomous decision-making.Perhaps the ultimate application is civilian transport systems. We are not condemned to a future of congestion and accidents. We will eventually have cars that can drive themselves, interacting safely with other road users and using roads efficiently, thus freeing up our precious time. But to do this the machines need life-long infrastructure-free navigation, and that is the focus of this work.We will use the mathematics of probability and estimation to allow computers in robots to interpret data from sensors like cameras, radars and lasers, aerial photos and on-the-fly internet queries. We will use machine learning techniques to build and calibrate mathematical models which can explain the robot's view of the world in terms of prior experience (training), prior knowledge (aerial images, road plans and semantics) and automatically generated Web queries. The goal is to produce technology which allows robots always to know precisely where they are and what is around them. Robots have a big role to play in our future economy, but underpinning this role will be life-long infrastructure-free navigation.
Key Findings
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Organisation Website: http://www.ox.ac.uk