EPSRC Centre for Doctoral Training in Agri-Food Robotics: AgriFoRwArdS - James Heselden

James Heselden

  • University of Lincoln

Research Interests

Mobile Autonomy; Fleet Management; Robot Task Planning; Robot Navigation; Swarm Robotics; Agri-Robots; Virtual/Mixed Reality



  • Spatially-Decentralised Coordination for Agricultural Fleets @ The Towards Autonomous Robots and Systems (TAROS) Conference 2023 / CDT Annual Conference / Joint Robotics CDT Conference (September 2023)

Other activities and outputs

  • Work placement with the Innovate UK Robot Highways project (November 2022 to February 2023).

About me

I am originally from South London and have been in Lincoln for 6 years now.

Before joining the CDT, I worked as a research assistant in the area of in-field logistics for soft-fruit picking, where I worked to upgrade and extend facilities for task management, route coordination, user interfacing, and robot communications. My main areas of interest are in fleet routing and task management, so this was a fun workspace to experiment with new ideas.

I also have interests in other fields such as virtual reality which I have been exploring in other projects. I hope to bring aspects of what I have learnt in these to my PHD.

MSc Project

James joined the CDT as a 4-year PhD student as he had already completed the MSc Robotics and Autonomous Systems at the University of Lincoln.

PhD Project

Robust Robotic Fleet Management for Warehouse Operations

The demand for click-and-deliver e-commerce services for food and grocery has increased. This, in turn, has increased the demand for deploying robotic-fleets in warehouses. There are very commercial players in the field with a feasible commercial solution. In literature, researchers address either the multi-robot task allocation, ignoring the path planning or the multi-robot path planning, ignoring the task planning approaches. However, simultaneously addressing these can ensure efficient collaborations between the robots in sharing the workload of different orders and efficient collision-free multi-robot navigation. There is also a high demand for performance guarantees if robots fail during their operation or if they end up in congested situations causing delays in fulfilling their assigned tasks. This project will explore the development of robust robotic fleet management algorithms with performance guarantees for robustness while deploying in a warehouse. 

The student will investigate (i) clustering of tasks based on the subtasks (items in each order), (ii) a hybrid centralised-decentralised approach for allocation of clusters of pick and delivery, and replenishment tasks preferably using market-based approaches and (iii) associated collision-free and time and energy efficient multi-robot path planning (MRPP) using prioritised multi-robot path planning with time-windowed reservation tables. The MRPP component may also be addressed in a hybrid manner to give robustness guarantees and to ensure scalability to a large number of robots operating in large environments. 

  1. Forwarding the thoughts and vision from a single robot’s perspective to those of a fleet of coordinated robots 
  2. Understanding of mathematical optimisation problem formulation 
  3. Working on physical robotic platforms 
  4. Developing and working on simulated environments 
  5. Programming and developing algorithms 
  6. Understanding and incorporating industry partner’s requirements into the research 
  7. Academic writing and publishing 
  8. Dissemination of research contributions to a wide range of audience 

James’s PhD project is being carried out under the primary supervision of Dr Gautham Das.