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

2025-06-06

AI Summary

This paper addresses time-critical medical supply delivery by unmanned aerial vehicles (UAVs) in urban environments, subject to multi-hospital time-window constraints, cargo priority levels, 3D convex building obstacles, and 3-degree-of-freedom dynamical feasibility. Method: We propose a trajectory optimization framework that jointly integrates formal task specification—expressed in Signal Temporal Logic (STL)—with real-time safety guarantees via convex feasible sets (CFS) for obstacle avoidance, all embedded within a unified convex optimization formulation. Contribution/Results: To the best of our knowledge, this is the first approach to achieve joint convexification of STL semantic constraints and geometric safety requirements. The resulting trajectories are dynamically feasible, collision-free, and strictly satisfy spatiotemporal deadlines and cargo priority ordering. Extensive simulations demonstrate high reliability and scalability in complex urban settings with dense, heterogeneous obstacles.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Optimize UAV medical delivery trajectories with time constraints
• Ensure collision avoidance using convex feasible set method
• Formulate mission objectives via Signal Temporal Logic (STL)

Innovation

• UAV trajectory optimization with Signal Temporal Logic
• Convex Feasible Set for 3D obstacle avoidance
• Convex optimization for efficient medical delivery planning