EPSRC Centre for Doctoral Training in Agri-Food Robotics: AgriFoRwArdS - Calvin John

Calvin John

  • University of East Anglia

Research Interests

Computer vision, artificial intelligence, robotics, machine learning, agricultural automation


  • Robotic Waypoint Coverage Under Soft Parallel Path Constraints @ The Towards Autonomous Robots and Systems (TAROS) Conference 2023 / CDT Annual Conference / Joint Robotics CDT Conference (September 2023)

About me

My name is Calvin, I am originally from Manchester. I am interested in researching the use of Robotics and AI in agriculture in order to create more environmentally sustainable farming practices.  I joined the Agri-forwards CDT in October 2022. Before joining the CDT, I studied an MSc in Computer Science with Data Analytics at the University of York and a BA in Philosophy, Politics, and Economics from the University of Essex. I am excited to be joining the University of East Anglia for my Ph.D. project in Agricultural Automation. I chose UEA because of its world-leading research in computer vision and its strong industrial links. 

MSc Project

Comparative Study of Graph-Based Methods for Coverage Path Planning: An Analysis of the Minimum Spanning Tree vs. the Nearest Neighbours Algorithm

Coverage path planning (CPP) is a crucial field of research in autonomous mobile robotics. The fundamental objective of CPP is to define a path that allows an autonomous agent, such as a robot, to pass over all points in a defined space while considering a specific set of constraints. This is of paramount importance in a multitude of applications including automated floor cleaning, aerial photography, agricultural monitoring, mine sweeping, search and rescue, and many more. Effective CPP significantly reduces the cost of operations in these fields by optimizing the robot’s movement, thus minimizing the time taken and energy expended for task completion.

This project seeks to contribute to the field by undertaking a comparative analysis of two graph-based methods for CPP; the Minimum Spanning Tree (MST) and the Nearest Neighbours (NN) algorithm. Both algorithms have been widely utilized in CPP but have yet to be compared under a uniquely specified set of constraints.

The hard constraints imposed in this study prevent diagonal movement between nodes, therefore obliging the robot to move in four directions: up, down, left, and right. This condition closely simulates real-world scenarios where certain movement patterns may not be viable due to physical restrictions or safety considerations. The soft constraints, on the other hand, specify that the robot must strive to travel in parallel horizontal lines. While it is not mandatory, these constraints reflect operational efficiency in many real-world applications like agricultural field monitoring or automated vacuum cleaning where following a systematic, parallel line pattern optimizes coverage and conserves energy.

The effectiveness of both the Minimum Spanning Tree (MST) and Nearest Neighbours (NN) algorithms will be gauged using two specific performance metrics: computation time and the frequency of node revisitation during path traversal. By comparing MST and NN in this context, the study aims to provide insights and potential recommendations for researchers and practitioners in the field, ultimately driving the advancement of efficient, cost-saving autonomous robotics applications.

PhD Project

A GNSS/RTK Localization-based Agri-Robot Digital Twin for Precision Agri-tasks in Crop Fields

The UK agricultural sector is struggling to attract skilled workers to carryout essential agricultural tasks that were once saturated before the UK left the EU. On top of this current global events such as war and climate change have had a catastrophic effect on the agricultural supply chain. To help combat the shortages in labour the UK agricultural sector needs automate the farming role that have labour shortages. This is especially pertinent in roles such as crop harvesting where labour shortages are declining at an alarming rate. This could easily be rectified by replacing this labour shortage with autonomous harvesting machines.

A key issue in this area of automation is that some crops block out the gps signals that the robot uses to navigate from waypoint to waypoint.  A solution to this is to develop a GNSS RTK robot system to provide accurate longitude and latitude coordinates for the robot to navigate from location to location. Whilst GNSS RTK

The research methodology used in this project will be entirely quantitative. The project first involves the creation of a simulation of the system. This will be implemented in the webots environment (previous comments about gazebo not being suitable for GPS simulation will not apply with webots, this is because noise can be added to GPS nodes allowing for the simulation of poor GPS which will in turn allow us simulate a GPS RTK solution). A new SLAM algorithm will be created that combines, 6G, graph SLAM and visual inertial SLAM technologies with GNSS RTK localisation technology. A physical robot will be built that will use the algorithms that were implemented in the simulation and the performance of these algorithms can be tested and evaluated in the real world.

Calvin’s PhD project is being carried out under the primary supervision of Dr Edwin Ren.