Semi‐Markovian planning to coordinate aerial and maritime medical evacuation platforms

Abstract The transfer of patients between two aircraft using an underway watercraft increases medical evacuation reach and flexibility in maritime environments. The selection of any one of multiple underway watercraft for patient exchange is complicated by participating aircraft utilization histories and participating watercraft positions and velocities. The selection problem is modeled as a semi‐Markov decision process with an action space, including both fixed land and moving watercraft exchange points. Monte Carlo tree search with root parallelization is used to select optimal exchange points and determine aircraft dispatch times. Model parameters are varied in simulation to identify representative scenarios where watercraft exchange points reduce incident response times. We find that an optimal policy with watercraft exchange points outperforms an optimal policy without watercraft exchange points and a greedy policy by 35% and 40%, respectively. In partnership with the United States Army, we deploy for the first time the watercraft exchange point by executing a mock patient transfer with a manikin between two HH‐60M medical evacuation helicopters and an underway Army Logistic Support Vessel south of the Hawaiian island of Oahu. Both helicopters were dispatched in accordance with our optimized decision strategy.

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Publication Details

Journal
AI Magazine
Published
2025-08-26
DOI
https://doi.org/10.1002/aaai.70023
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
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article

Semi‐Markovian planning to coordinate aerial and maritime medical evacuation platforms

Kyle Hollins Wray, Mykel J. Kochenderfer, Mahdi Al‐Husseini
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AI Magazine
Robotic Path Planning Algorithms
article

Semi‐Markovian planning to coordinate aerial and maritime medical evacuation platforms

Kyle Hollins Wray, Mykel J. Kochenderfer, Mahdi Al‐Husseini
article en

Abstract

Abstract The transfer of patients between two aircraft using an underway watercraft increases medical evacuation reach and flexibility in maritime environments. The selection of any one of multiple underway watercraft for patient exchange is complicated by participating aircraft utilization histories and participating watercraft positions and velocities. The selection problem is modeled as a semi‐Markov decision process with an action space, including both fixed land and moving watercraft exchange points. Monte Carlo tree search with root parallelization is used to select optimal exchange points and determine aircraft dispatch times. Model parameters are varied in simulation to identify representative scenarios where watercraft exchange points reduce incident response times. We find that an optimal policy with watercraft exchange points outperforms an optimal policy without watercraft exchange points and a greedy policy by 35% and 40%, respectively. In partnership with the United States Army, we deploy for the first time the watercraft exchange point by executing a mock patient transfer with a manikin between two HH‐60M medical evacuation helicopters and an underway Army Logistic Support Vessel south of the Hawaiian island of Oahu. Both helicopters were dispatched in accordance with our optimized decision strategy.

AI MagazineVol. 46(3)
Boston University (US), Northeastern University (US), Vaughn College of Aeronautics and Technology (US), United States Army (US), American Institute of Aeronautics and Astronautics (US), Nanjing University of Aeronautics and Astronautics (CN)
Openalex Percentile: Top 79%
Robotic Path Planning Algorithms
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Semi‐Markovian planning to coordinate aerial and maritime medical evacuation platforms — Kyle Hollins Wray, Mykel J. Kochenderfer, et al. · AI Magazine (2025) | TGRS Research Map | TGRS