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

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

To be confirmed