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Iain Gould

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Agricultural Robotics: The Future of Robotic Agriculture

Aug 02, 2018
Tom Duckett, Simon Pearson, Simon Blackmore, Bruce Grieve, Wen-Hua Chen, Grzegorz Cielniak, Jason Cleaversmith, Jian Dai, Steve Davis, Charles Fox, Pål From, Ioannis Georgilas, Richie Gill, Iain Gould, Marc Hanheide, Alan Hunter, Fumiya Iida, Lyudmila Mihalyova, Samia Nefti-Meziani, Gerhard Neumann, Paolo Paoletti, Tony Pridmore, Dave Ross, Melvyn Smith, Martin Stoelen, Mark Swainson, Sam Wane, Peter Wilson, Isobel Wright, Guang-Zhong Yang

Agri-Food is the largest manufacturing sector in the UK. It supports a food chain that generates over {\pounds}108bn p.a., with 3.9m employees in a truly international industry and exports {\pounds}20bn of UK manufactured goods. However, the global food chain is under pressure from population growth, climate change, political pressures affecting migration, population drift from rural to urban regions and the demographics of an aging global population. These challenges are recognised in the UK Industrial Strategy white paper and backed by significant investment via a Wave 2 Industrial Challenge Fund Investment ("Transforming Food Production: from Farm to Fork"). Robotics and Autonomous Systems (RAS) and associated digital technologies are now seen as enablers of this critical food chain transformation. To meet these challenges, this white paper reviews the state of the art in the application of RAS in Agri-Food production and explores research and innovation needs to ensure these technologies reach their full potential and deliver the necessary impacts in the Agri-Food sector.

* UK-RAS Network White Papers, ISSN 2398-4414 
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3D Soil Compaction Mapping through Kriging-based Exploration with a Mobile Robot

Mar 21, 2018
Jaime Pulido Fentanes, Iain Gould, Tom Duckett, Simon Pearson, Grzegorz Cielniak

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This paper presents an automated method for creating spatial maps of soil condition with an outdoor mobile robot. Effective soil mapping on farms can enhance yields, reduce inputs and help protect the environment. Traditionally, data are collected manually at an arbitrary set of locations, then soil maps are constructed offline using Kriging, a form of Gaussian process regression. This process is laborious and costly, limiting the quality and resolution of the resulting information. Instead, we propose to use an outdoor mobile robot for automatic collection of soil condition data, building soil maps online and also adapting the robot's exploration strategy on-the-fly based on the current quality of the map. We show how using Kriging variance as a reward function for robotic exploration allows for both more efficient data collection and better soil models. This work presents the theoretical foundations for our proposal and an experimental comparison of exploration strategies using soil compaction data from a field generated with a mobile robot.

* Submitted paper, to IEEE Robotics and Automation Letters (RA-L) special issue on Precision Agricultural Robotics and Autonomous Farming Technologies. Not reviewed 
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