Intention-aware Motion Planning
The goal of this industrially sponsored project is to research and extend previous techniques to give a new approach to categorising motion and inferring intent to support robust maritime autonomy decisions in Unmanned Surface Vehicles.
Maritime systems have to manage high levels of data sparsity and inhomogeneity to reason effectively in terms of the grammar of motion adopted by different objects.
Elements of topology-based trajectory classification for inferring motion semantics and categorisation, distributed tracking & planning with reactive models, Bayesian reasoning and learning algorithms will be combined and extended for noisy data sampled on large spatiotemporal scales to give high-confidence inference of intent to inform autonomous decisions.
The following papers are indicative of the types of techniques that will be further developed, and applied within a deployed robotics application, through this project:
• F. T. Pokorny, M. Hawasly, S. Ramamoorthy, Multiscale topological trajectory classification with persistent homology, In Proc. Robotics: Science and Systems (R:SS), 2014.
• S. Albrecht, S. Ramamoorthy, On convergence and optimality of best-response learning with policy types in multiagent systems, In Proc. Conference on Uncertainty in Artificial Intelligence (UAI), 2014.
Applicants should have, or expect to obtain one of the following qualifications:
BSc/MSc Computer Science or Electrical/Electronic Engineering (or equivalent) 1st/70% average or above.
Other engineering, science or mathematical backgrounds studied to a suitably qualified level may also be considered.
The applicant should have excellent mathematical skills (especially in the areas of linear algebra, geometry and topology, probability and statistics), in addition to computer programming and robotics experience.
This is a fully funded industrial sponsored studentship covering Home fees and stipend (£14,057 for 2015/16). This project is only valid for UK students due to the nature of the funding.