A Survey of Medical Drones from Flight Dynamics, Guidance, Navigation, and Control Perspectives
- URL: http://arxiv.org/abs/2602.06969v1
- Date: Mon, 05 Jan 2026 00:45:07 GMT
- Title: A Survey of Medical Drones from Flight Dynamics, Guidance, Navigation, and Control Perspectives
- Abstract summary: This paper provides a review of medical drones from the perspectives of flight dynamics and guidance, navigation, and control (GNC) systems.<n>We first discuss the medical aerial delivery mission requirements and suitable uncrewed aerial system (UAS) configurations.<n>We then address payload container design and optimization, and its effect on supplies and overall flight dynamics.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- A Multi-Robot Platform for Robotic Triage Combining Onboard Sensing and Foundation Models [51.02314427799649]
The system employs a coordinated air-ground team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs)<n>The UAV identify and provide overhead views of casualties, while UGVs equipped with specialized sensors measure vital signs and detect and localize physical injuries.
arXiv Detail & Related papers (2025-12-09T16:05:22Z) - MedGemma Technical Report [75.88152277443179]
We introduce MedGemma, a collection of medical vision-language foundation models based on Gemma 3 4B and 27B.<n>MedGemma demonstrates advanced medical understanding and reasoning on images and text.<n>We additionally introduce MedSigLIP, a medically-tuned vision encoder derived from SigLIP.
arXiv Detail & Related papers (2025-07-07T17:01:44Z) - Drone Acoustic Analysis for Predicting Psychoacoustic Annoyance via Artificial Neural Networks [0.0]
This study builds upon prior research by examining the efficacy of various Deep Learning models in predicting Psychoacoustic Annoyance.
The aim of this research is to improve our understanding of drone noise, aid in the development of noise reduction techniques, and encourage the acceptance of drone usage on public spaces.
arXiv Detail & Related papers (2024-10-29T16:38:34Z) - IoT-Based Remote Health Monitoring System Employing Smart Sensors for
Asthma Patients during COVID-19 Pandemic [0.0]
COVID19 and asthma are respiratory diseases that can be life threatening in uncontrolled circumstances.
A poverty stricken South Asian country like Bangladesh has been bearing the brunt of the COVID19 pandemic since its beginning.
This paper demonstrates how the current challenges in the healthcare system are resolvable through the design of a remote health and environment monitoring system.
arXiv Detail & Related papers (2023-03-28T07:23:03Z) - ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and
Response with AI [55.41644538483948]
Small unmanned aircraft systems (sUAS) are becoming prominent components of many humanitarian assistance and disaster response operations.
We have developed the free and open-source ADAPT multi-mission payload for deploying real-time AI and computer vision onboard a sUAS.
We demonstrate the example mission of real-time, in-flight ice segmentation to monitor river ice state and provide timely predictions of catastrophic flooding events.
arXiv Detail & Related papers (2022-01-25T14:51:19Z) - Transformers in Medical Imaging: A Survey [88.03790310594533]
Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results.
Medical imaging has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields.
We provide a review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues.
arXiv Detail & Related papers (2022-01-24T18:50:18Z) - The State of Aerial Surveillance: A Survey [62.198765910573556]
This paper provides a comprehensive overview of human-centric aerial surveillance tasks from a computer vision and pattern recognition perspective.
The main object of interest is humans, where single or multiple subjects are to be detected, identified, tracked, re-identified and have their behavior analyzed.
arXiv Detail & Related papers (2022-01-09T20:13:27Z) - Building a Decision Support System for Automated Mobile Asthma
Monitoring in Remote Areas [0.0]
This paper proposes the use of smartphone equipped with embedded sensors, to capture and analyze early symptoms of asthma triggered by exercise.
Preliminary results show that smartphones can be used to monitor and detect asthma symptoms without other networked devices.
arXiv Detail & Related papers (2021-12-11T14:18:08Z) - Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
A fully-autonomous aerial robot for high-speed object grasping has been proposed.
As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface.
Our approach has been validated in a challenging international competition and has shown outstanding results.
arXiv Detail & Related papers (2021-12-10T11:49:51Z) - Drones for Medical Delivery Considering Different Demands Classes: A
Markov Decision Process Approach for Managing Health Centers Dispatching
Medical Products [0.0]
We consider the problem of optimizing the distribution operations of a hub using drones to deliver medical supplies to different geographic regions.
By considering different geographic locations, we consider different classes of demand that require different flight ranges.
We classify the demands based on their distance from the drone hub, use a Markov decision process to model the problem and perform computational tests.
arXiv Detail & Related papers (2021-06-08T23:20:31Z) - An Integrated Framework for Sensing Radio Frequency Spectrum Attacks on
Medical Delivery Drones [0.0]
Drone susceptibility to jamming or spoofing attacks presents a danger to future medical delivery systems.
A detection framework capable of sensing attacks on drones could provide the capability for active responses.
arXiv Detail & Related papers (2020-05-04T14:13:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.