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Decision-Making Framework for Complex Operations in Forestry Robotics S&T29

  • School: School of Science and Technology
  • Study mode(s): Full-time / Part-time
  • Starting: 2022
  • Funding: UK student / EU student (non-UK) / International student (non-EU) / Fully-funded


NTU's Fully-funded PhD Studentship Scheme 2022

Project ID: S&T29

Wildfires have become a major problem in the past few decades, as climate change has pushed the geography of potential wildfire outbreaks to a broader range of regions in the world. Lack of specialised labour willing to work in forestry operations has further compounded this issue, making the need for automation, such as the introduction of robotics solutions, an inevitability.

However, despite many advances in key areas, the development of fully autonomous robotic solutions for precision forestry is still in a very early stage. This stems from many different challenges, but in particular due to limited perception capabilities, and reasoning and planning under a high level of uncertainty. Artificial perception for robots operating in outdoor natural environments has been studied for several decades. Nevertheless, despite many years of research, as described in surveys over time, a substantial number of problems have yet to be robustly solved. An even greater challenge presents itself when considering multi-robot systems (MRS) in this context.

This project will focus on the research development of a distributed framework for decision-making for forestry robots with the mission of landscaping woodland areas by mulching living flammable material for fire prevention. Several theoretical tools and models for decision-making under high-level of uncertainty exist, such as probabilistic finite state machines, behaviour trees and partially-observable Markov decision processes (POMDPs). However, in general terms, these techniques lack validation in MRS for field robotics, which is in part caused by the relative lack of maturity of this particular intersection of research areas, suggesting that further research is necessary.

The objectives of the proposed study are: (1) design a sophisticated decision-making framework capable of dealing with challenging outdoor conditions needed for implementing (a) region of interest surveillance planning and execution, (b) clearing planning and execution, and (c) seamless management of robot team coordination and resource usage; (2) investigate and compare the available solutions for decision-making, and evaluate the usage, adaptation or replacement of these methods in the context of this framework; (3) implement and test the framework both in simulation and on a real-world MRS. Overall, this project is expected to significantly contribute to artificial intelligence, cognitive robotics, and automation in precision forestry.

This project is suited for candidates that are concerned about climate change and a sustainable future and wish to make a difference, who have a background in computing/computer science, and who are excited about the field of robotics.

School strategic research priority

The proposed study is aligned to the NTU research subject area of Computer Science and Informatics, namely in what relates to work developed at the Computing and Informatics Research Centre (CIRC).

It also aligns with the Sustainable Futures research theme, since it deals with woodlands, which are a crucial part of the carbon cycle and environmental and ecosystem sustainability.

Entry qualifications

For the eligibility criteria, visit our studentship application page.

How to apply

For guidance and to make an application, please visit our studentship application page. The application deadline is Friday 14 January 2022.

Fees and funding

This is part of NTU's 2022 fully-funded PhD Studentship Scheme.

Guidance and support

Download our full applicant guidance notes for more information.

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