<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/10C5DF82-3677-45AB-BDED-0BEF24FCEEB3" ns1:id="10C5DF82-3677-45AB-BDED-0BEF24FCEEB3"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/282CF797-6A1F-4C84-B039-CAD2E112DE73" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/4183F004-3650-4DC9-A021-145961C06133" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/4183F004-3650-4DC9-A021-145961C06133" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2014-11-30T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/DD8B2EFC-2120-470B-A2A5-5A8A8EE1A501" ns1:rel="FUND" ns1:start="2014-07-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">131639</ns2:identifier></ns2:identifiers><ns2:title>A Virtual Simulation system for Autonomous Environmental Exploration</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Feasibility Studies</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>There is a very real need for robotics that can explore environments; there are a number of hazardous locations within which one would like to deploy robots there are practical applications of field robots in the Nuclear, Military and Agricultural sectors. Even when a human controls a robot it is best practice is to have the robot as aware of its environment as possible. 
Our objective is to create a virtual environment that allows for the development and testing of autonomous agents. Having good simulators allows for quicker and cheaper iterations in the testing of machine learning and robotic artificial intelligence. This opens up a range of training options including training deep learning systems through continual interation.</ns2:abstractText></ns2:project>