Enhanced Trust Region Sequential Convex Optimization for Multi-Drone Thermal Screening Trajectory Planning in Urban Environments

TL;DR

This paper introduces an enhanced trust region sequential convex optimization algorithm for planning efficient, collision-free drone trajectories in urban thermal screening, improving coverage and computational speed.

Contribution

We develop a novel TR-SCO algorithm that better balances obstacle avoidance, coverage, and smoothness for multi-drone thermal screening in complex urban environments.

Findings

Significant improvement in trajectory optimality over traditional methods

Enhanced computational efficiency demonstrated in simulations

Effective handling of obstacle avoidance and coverage constraints

Abstract

The rapid detection of abnormal body temperatures in urban populations is essential for managing public health risks, especially during outbreaks of infectious diseases. Multi-drone thermal screening systems offer promising solutions for fast, large-scale, and non-intrusive human temperature monitoring. However, trajectory planning for multiple drones in complex urban environments poses significant challenges, including collision avoidance, coverage efficiency, and constrained flight environments. In this study, we propose an enhanced trust region sequential convex optimization (TR-SCO) algorithm for optimal trajectory planning of multiple drones performing thermal screening tasks. Our improved algorithm integrates a refined convex optimization formulation within a trust region framework, effectively balancing trajectory smoothness, obstacle avoidance, altitude constraints, and maximum screening coverage. Simulation results demonstrate that our approach significantly improves trajectory optimality and computational efficiency compared to conventional convex optimization methods. This research provides critical insights and practical contributions toward deploying efficient multi-drone systems for real-time thermal screening in urban areas. For reader who are interested in our research, we release our source code at https://github.com/Cherry0302/Enhanced-TR-SCO.