Trajectory Optimization for UAV-Based Medical Delivery with Temporal Logic Constraints and Convex Feasible Set Collision Avoidance

TL;DR

This paper presents a convex optimization-based method for planning safe, feasible UAV trajectories for urgent medical deliveries in urban areas, satisfying temporal and spatial constraints efficiently.

Contribution

It introduces a novel convex optimization framework integrating STL-based mission specifications with convex obstacle avoidance for UAV trajectory planning.

Findings

Generated collision-free, temporally compliant UAV trajectories in simulations.

The method ensures scalability and real-time applicability for urban medical logistics.

Trajectories satisfy all specified temporal and spatial constraints reliably.

Abstract

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.