Academic Journal

Trajectory optimization for UAV-based medical delivery with temporal logic constraints and convex feasible set collision avoidance.

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Τίτλος: Trajectory optimization for UAV-based medical delivery with temporal logic constraints and convex feasible set collision avoidance.
Συγγραφείς: Chen, Kaiyuan, Suo, Yuhan, Cui, Shaowei, Xi, Yuanqing, Liang, Wannian, Wang, Shuo
Πηγή: Global Transitions; 2026, Vol. 8 Issue 1, p110-119, 10p
Θεματικοί όροι: Trajectory optimization, Obstacle avoidance (Robotics), Medical care, Aeronautical navigation, Convex domains, Convex programming
Περίληψη: This paper addresses the problem of trajectory optimization for unmanned aerial vehicles (UAVs) performing time-sensitive medical deliveries in urban environments. Specifically, we consider a single UAV with 3-degree-of-freedom dynamics tasked with delivering blood packages to multiple hospitals, each with a predefined time window and priority. Mission objectives are encoded using Signal Temporal Logic (STL), enabling the formal specification of spatial-temporal constraints. To ensure safety, city buildings are modeled as 3D convex obstacles, and obstacle avoidance is handled through a Convex Feasible Set (CFS) method. The entire planning problem—combining UAV dynamics, STL satisfaction, and collision avoidance—is formulated as a convex optimization problem that ensures tractability and can be solved efficiently using standard convex programming techniques. Simulation results demonstrate that the proposed method generates dynamically feasible, collision-free trajectories that satisfy temporal mission goals, providing a scalable and reliable approach for autonomous UAV-based medical logistics. [ABSTRACT FROM AUTHOR]
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Βάση Δεδομένων: Biomedical Index
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  Data: Trajectory optimization for UAV-based medical delivery with temporal logic constraints and convex feasible set collision avoidance.
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  Data: Global Transitions; 2026, Vol. 8 Issue 1, p110-119, 10p
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  Data: <searchLink fieldCode="DE" term="%22Trajectory+optimization%22">Trajectory optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Obstacle+avoidance+%28Robotics%29%22">Obstacle avoidance (Robotics)</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+care%22">Medical care</searchLink><br /><searchLink fieldCode="DE" term="%22Aeronautical+navigation%22">Aeronautical navigation</searchLink><br /><searchLink fieldCode="DE" term="%22Convex+domains%22">Convex domains</searchLink><br /><searchLink fieldCode="DE" term="%22Convex+programming%22">Convex programming</searchLink>
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  Label: Abstract
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  Data: This paper addresses the problem of trajectory optimization for unmanned aerial vehicles (UAVs) performing time-sensitive medical deliveries in urban environments. Specifically, we consider a single UAV with 3-degree-of-freedom dynamics tasked with delivering blood packages to multiple hospitals, each with a predefined time window and priority. Mission objectives are encoded using Signal Temporal Logic (STL), enabling the formal specification of spatial-temporal constraints. To ensure safety, city buildings are modeled as 3D convex obstacles, and obstacle avoidance is handled through a Convex Feasible Set (CFS) method. The entire planning problem—combining UAV dynamics, STL satisfaction, and collision avoidance—is formulated as a convex optimization problem that ensures tractability and can be solved efficiently using standard convex programming techniques. Simulation results demonstrate that the proposed method generates dynamically feasible, collision-free trajectories that satisfy temporal mission goals, providing a scalable and reliable approach for autonomous UAV-based medical logistics. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Global Transitions is the property of KeAi Communications Co. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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              Text: 2026
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