This thesis presents a novel approach to planning under uncertainty in resource constrained environments. Such environments feature in many real-world applications, including planetary rover and autonomous underwater vehicle (AUV) missions. Our focus is on long-duration AUV missions, in which a vehicle spends months at sea, with little or no opportunity for intervention. As the risk to the vehicle and cost of deployment are significant, it is important to fully utilise each mission, maximising data return without compromising vehicle safety. Planning within this domain is challenging because significant resource usage uncertainty prevents computation of an optimal strategy in advance. We describe our novel method for online plan modification and execution monitoring, which augments an existing plan with pre-computed plan fragments in response to observed resource availability. Our modification algorithm uses causal structure to interleave actions, creating solutions without introducing significant computational cost. Our system monitors resource availability, reasoning about the probability of successfully completing the goals. We show that when the probability of completing the mission decreases, by removing low-priority goals our system reduces the risk to the vehicle, increasing mission success rate. Conversely, when resource availability allows, by including additional goals our system increases reward without adversely affecting success rate.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:665781 |
| Date | January 2015 |
| Creators | Harris, Catherine Ann |
| Publisher | University of Birmingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.bham.ac.uk//id/eprint/6176/ |
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