references.bib

@inproceedings{ewers_gis_2023,
  title = {{{GIS Data Driven Probability Map Generation}} for {{Search}} and {{Rescue Using Agents}}},
  booktitle = {{{IFAC World Congress}} 2023},
  author = {Ewers, Jan-Hendrik and Anderson, David and Thomson, Douglas},
  year = {2023},
  pages = {1466--1471},
  doi = {10.1016/j.ifacol.2023.10.1834},
  urldate = {2023-11-23},
  abstract = {Predicting the final resting location of a missing person is critical for search and rescue operations with limited resources. To improve the accuracy and speed of these predictions, simulated agents can be created to replicate the behavior of the missing person. In this paper, we introduce an agent-based model, to simulate various psychological profiles, that move over a physical landscape incorporating real-world data in their decision-making without relying on per-location training. The resultant probability density map of the missing person's location was the result of a combination of Monte Carlo simulations and mobility-time-based sampling. General trends in the data were comparable to historical data sets available. This work presents a flexible agent that can be employed by search and rescue that easily extends to various locations.},
  copyright = {All rights reserved},
  langid = {english},
  oa = {true}
}

@article{ewers_optimal_2023,
  title = {Optimal Path Planning Using Psychological Profiling in Drone-assisted Missing Person Search},
  author = {Ewers, Jan-Hendrik and Anderson, David and Thomson, Douglas},
  year = {2023},
  month = sep,
  journal = {Advanced Control for Applications},
  pages = {e167},
  issn = {2578-0727, 2578-0727},
  doi = {10.1002/adc2.167},
  urldate = {2023-09-25},
  abstract = {Search and rescue operations are all time-sensitive and this is especially true when searching for a vulnerable missing person, such as a child or elderly person suffering dementia. Recently, Police Scotland Air Support Unit have begun the deployment of drones to assist in missing person search with success, although the efficacy of the search relies upon the expertise of the drone operator. In this paper, several algorithms for planning the search path are compared to determine which approach has the highest probability of finding the missing person in the shortest time. In addition to this, the use of {\textbackslash}'a priori psychological profile information of the subject to create a probability map of likely locations within the search area was explored. This map is then used within a non-linear optimisation to determine the optimal flight path for a given search area and subject profile. Two optimisation solvers were compared; genetic algorithms, particle swarm optimisation. Finally, the most effective algorithm was used to create a coverage path for a real-life location, for which Police Scotland Air Support Unit completed multiple test flights. The generated flight paths based on the predicted intent of the lost person were found to perform statistically better than those of the expert police operators.},
  copyright = {All rights reserved},
  langid = {english},
  oa = {true}
}