This project aims to design and develop a Mobile Robotic System which will autonomously and safely navigate within an agriculture setting to monitor and respond to key sensor data of moisture and temperature to optimise particular farming tasks to support farmers and reduce risks of contamination and/or infection.
Developing an autonomous system capable of operating in harsh changeable environment such as in agriculture settings, is a significant research challenge. The dynamic cluttered setting and the uneven and changing terrain are major challenges to an autonomous vehicle to safely map and navigate its way around a farm and complete set tasks.
The autonomous robotic system will be equipped with a number of sensors that will continuously record data from the environment. This is equally challenging as the sensing system will have to be robust suitable for the rough agriculture setting and data recorded will have to be reliable for both off and online analysis.
Each of these challenges present a significant research challenge which will require serious effort to investigate and solve.
This is a multi-disciplinary PhD project that sits at the interface of engineering and agriculture.
We are seeking a committed and motivated student, preferably with a 1st or 2.1 class and/or a master’s degree in robotics, mechatronics, electrical engineering and electronics. Successful candidates must have strong programming skills in Python and/or C++. Knowledge and experience in using ROS is preferred.
The successful PhD student will be co-supervised by Dr Heba Lakany and Dr Ian Sandall and work closely with our industrial partner in the agriculture sector.
The student will be provided with a broad training and development programme. The student will spend some of their time working on site with the industrial partner to enable them to develop appropriate communication skills and broaden their multi-disciplinary knowledge and skills and understanding of the application of their research. The student will be provided with opportunities to attend appropriate discipline related training (i.e. formal training on software packages). The students will be encouraged to take up opportunities to develop and train in public engagement (i.e. attending the Royal Society training programme) and will be invited to join various relevant academic networks to develop a professional network.
Open to students worldwide
This project is a funded Studentship for 3.5 years in total and will provide UK tuition fees and maintenance at the UKRI Doctoral Stipend rate (£18,622 per annum, 2023/24 rate).
Zhao J, Liu S, Li J. Research and Implementation of Autonomous Navigation for Mobile Robots Based on SLAM Algorithm under ROS. Sensors (Basel). 2022 May 31;22(11):4172. doi: 10.3390/s22114172. PMID: 35684793; PMCID: PMC9185640.
Jiang G, Yin L, Jin S, Tian C, Ma X, Ou Y. A Simultaneous Localization and Mapping (SLAM) Framework for 2.5D Map Building Based on Low-Cost LiDAR and Vision Fusion. Applied Sciences. 2019; 9(10):2105. https://doi.org/10.3390/app9102105.
Linnhoff C, Hofrichter K, Elster L, Rosenberger P, Winner H. Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors. Sensors (Basel). 2022 Jul 14;22(14):5266. doi: 10.3390/s22145266. PMID: 35890948; PMCID: PMC9315550.
Lorenzo Leso, Patrícia F.P. Ferraz, Gabriel A.S. Ferraz, Giuseppe Rossi, Matteo Barbari, Factors affecting evaporation of water from cattle bedding materials, Biosystems Engineering, Volume 205,2021, Pages 164-173,ISSN 1537- 5110, https://doi.org/10.1016/j.biosystemseng.2021.03.002.