A UAV-based Algorithm to Assist Ground SAR Teams in Finding Lost Persons Living with Dementia

Hanna, Dalia; Ferworn, Alexander

doi:10.1109/plans46316.2020.9109867

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…UAV-based approaches rely on expert input to guide the UAVs' trajectories, as well as the use of an operator to identify the lost person [19,20]. Most focus on mapping the unknown environments [3,5,22] and are typically limited to small areas.…”

Section: Urban Search For a Missing/lost Personmentioning

confidence: 99%

“…Autonomous vehicles (aerial and/or ground) have commonly been proposed for urban search and rescue (USAR) [1][2][3][4][5][6][7][8][9] and wilderness search and rescue (WiSAR) applications [10][11][12][13][14][15][16][17][18]. They have also been suggested for city searches [19][20][21][22][23][24], although, with limited scale or autonomy. Namely, most city-search approaches require human guidance or teleoperation of the robot searchers [19,20], while some also search only a small portion of an area relative to what the mobile missing person can actually cover [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…They have also been suggested for city searches [19][20][21][22][23][24], although, with limited scale or autonomy. Namely, most city-search approaches require human guidance or teleoperation of the robot searchers [19,20], while some also search only a small portion of an area relative to what the mobile missing person can actually cover [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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An Aerial Robotic Missing-Person Search in Urban Settings—A Probabilistic Approach

Haigh,

Nejat,

Benhabib

2024

Robotics

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Autonomous robotic teams have been proposed for a variety of lost-person searches in wilderness and urban settings. In the latter scenarios, for missing persons, the application of such teams, however, is more challenging than it would be in the wilderness. This paper, specifically, examines the application of an autonomous team of unmanned aerial vehicles (UAVs) to perform a sparse, mobile-target search in an urban setting. A novel multi-UAV search-trajectory planning method, which relies on the prediction of the missing-person’s motion, given a known map of the search environment, is the primary focus. The proposed method incorporates periodic updates of the estimates of where the lost/missing person may be, allowing for intelligent re-coverage of previously searched areas. Additional significant contributions of this work include a behavior-based motion-prediction method for missing persons and a novel non-parametric estimator for iso-probability-based (missing-person-location) curves. Simulated experiments are presented to illustrate the effectiveness of the proposed search-planning method, demonstrating higher rates of missing-person detection and in shorter times compared to other methods.

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