A Survey of Medical Drones from Flight Dynamics, Guidance, Navigation, and Control Perspectives

TL;DR

The paper addresses the challenge of deploying medical drones by uniting flight dynamics with guidance, navigation, and control considerations. It surveys mission requirements, UAS configurations, payload container designs, and environmental effects, and analyzes GNC algorithms and their limitations in the medical context. Key contributions include a structured taxonomy of payload types, a review of minimum-vibration trajectory methods, BVLOS and loss-of-communication planning, and comprehensive coverage of sensor fusion, perception, and DAA approaches. The findings underscore the need for holistic systems engineering, where airframe, payload, and GNC are co-designed to preserve payload integrity under environmental stressors, with future work pointing toward metamaterials for vibration isolation, intelligent payload systems, and robust ice-protection strategies for all-weather medical drone operations.

Abstract

The integration of drones into the medical field has revolutionized healthcare delivery by enabling rapid transportation of medical supplies, organs, and even emergency assistance in remote or disaster-stricken areas. While other survey papers focus on the healthcare supply chain, operations, and medical emergency response aspects, this paper provides a comprehensive review of medical drones from the perspectives of flight dynamics and guidance, navigation, and control (GNC) systems. We first discuss the medical aerial delivery mission requirements and suitable uncrewed aerial system (UAS) configurations. We then address payload container design and optimization, and its effect on supplies and overall flight dynamics. We also explore the fundamental principles of GNC in the context of medical drone operations, highlighting key challenges arising from vibration, air temperature, pressure, and humidity, which affect the quality of medical supplies. The paper examines various GNC algorithms that can mitigate these challenges, as well as the algorithms' limitations. With these considerations, this survey aims to provide insights into optimizing GNC frameworks for medical drones, emphasizing research gaps and directions to improve real-world healthcare applications.