| Popis: |
The coordination of multiple Unmanned Aerial Vehicles (UAVs) to carry out aerial surveys is a major challenge for emergency responders. In particular, UAVs have to fly over kilometre-scale areas while trying to discover casualties as quickly as possible. To aid in this process, it is desirable to exploit the increasing availability of data about a disaster from sources such as crowd reports, satellite re- mote sensing, or manned reconnaissance. In particular, such inform- ation can be a valuable resource to drive the planning of UAV flight paths over a space in order to discover people who are in danger. However challenges of computational tractability remain when plan- ning over the very large action spaces that result. To overcome these, we introduce the survivor discovery problem and present as our solu- tion, the first example of a continuous factored coordinated Monte Carlo tree search algorithm. Our evaluation against state of the art benchmarks show that our algorithm, Co-CMCTS, is able to localise more casualties faster than standard approaches by 7% or more on simulations with real-world data. |
