EPSRC Centre for Doctoral Training in Agri-Food Robotics: AgriFoRwArdS - Samuel Carter

Samuel Carter

  • University of Lincoln

Research Interests

Indoor farming, agricultural automation, and mobile robotics.

Posters

  • AgriFoRwArdS CDT Annual Conference (2022): Learning robot navigation from demonstrations.

Activities and Outputs

  • Represented the CDT at the school outreach University of Lincoln British Science Week event 2022.

About me

My name is Samuel, aged 24 and am from Maidenhead. My background has been in mobile robotics. My MEng project involved 3D autonomous navigation and I have worked with mobile robots as an intern at Fox Robotics Ltd and Ross Robotics Limited. I believe that software is the core of robotics.

I have chosen to start the Agri-food CDT because I want to confront and overcome the demanding challenges of self sustainability. I was impressed by Lincoln University’s world leading involvement in agri-robotics research and am looking forward to being on the frontline of the cutting edge technology.

One of the areas of research I’m particularly interested in is automated indoor growing. This has been inspired by projects such as the Eden Project. I think there is a future in developing and converting non farmland into biodomes which have the capacity to grow exotic produce.

A fun fact about me is that I’ve had an 11 year career as a dancer doing tap and ballet. I’ve been an associate of the Royal Ballet School and performed in Sleeping Beauty at the Royal Opera House in Covent Garden. I hope to one day acquire my own farm which grows food using robots.

MSc Project

Learning robot navigation from demonstrations.

Mobile robot navigation is a complex task for human operators. Despite all the progress in autonomous navigation, the developed approaches are domain specific. Hence, a human operator is in charge of controlling the movements of a mobile robot. Autonomous navigation of mobile robots is a challenging and complex task. Developing a domain specific autonomous mobile system is effort demanding. This project will study the learning from demonstrations (LfD) for mobile robot navigation. This project studies deep Movement Primitives (an LfD method) for mobile robot (non-holonomic mobile robot) navigation. This project aims to map visual information into robot movement trajectories and generalise it. Simulated experiments on a mobile robot will be conducted to assess the feasibility of the LfD algorithms; and its performances are compared. Performance is measured on the adaptability to new goal positions and via points, obstacle avoidance and trajectory smoothness.

PhD Project

Title to be confirmed