AquaFly Project: Autonomous Multi-Drone Water Sampling with a Payload Deployment and Retraction Mechanism

The AquaFly project is a research collaboration between the Flight Systems and Control (FSC) Laboratory at the University of Toronto Institute for Aerospace Studies (UTIAS) and Queen’s University Biology Department in Canada to offer a fast, reliable, and efficient method of sampling water bodies by using autonomous Unmanned Aerial Systems (UAS). To this end, two UAS are custom-designed and flown for autonomous water sampling, incorporating an octocopter configuration and a ratchet and pawl mechanism for retracting and releasing payloads. This design offers easy-to-use and safe deployment and retraction without continuous motor engagement while eliminating load swinging and the risk of instability by positioning the sampler close to the drone’s center of mass. Furthermore, the system enables autonomous water sampling in remote or inaccessible areas with pre-programmed flight paths and sampling locations, ensuring consistent data collection. The sampling mechanism allows controlled retrieval and release of payloads and further facilitates multi-drone operations to support a variety of sampling missions. This innovative UAS has the potential to revolutionize water sampling and enhance efficiency and safety in environmental monitoring, research, and resource management. Flight tests over several water bodies demonstrate successful simultaneous water sampling missions with two UAS. To the best of the authors’ knowledge, this is the first work to include multiple UAS, each equipped with a payload deployment and retraction mechanism, for autonomous water sampling.

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Publication Details

Journal
Unmanned Systems
Published
2024-08-11
DOI
https://doi.org/10.1142/s230138502550058x
Citations
1
Primary Topic
Underwater Vehicles and Communication Systems
Type
article
Field-Weighted Citation Impact
0.34
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article

AquaFly Project: Autonomous Multi-Drone Water Sampling with a Payload Deployment and Retraction Mechanism

Hugh H. T. Liu, Seyedreza Fattahi Massoum, Cindi Feng
1 citations
Unmanned Systems
Underwater Vehicles and Communication Systems
0.34
article

AquaFly Project: Autonomous Multi-Drone Water Sampling with a Payload Deployment and Retraction Mechanism

Hugh H. T. Liu, Seyedreza Fattahi Massoum, Cindi Feng
article en
1 citations

Abstract

The AquaFly project is a research collaboration between the Flight Systems and Control (FSC) Laboratory at the University of Toronto Institute for Aerospace Studies (UTIAS) and Queen’s University Biology Department in Canada to offer a fast, reliable, and efficient method of sampling water bodies by using autonomous Unmanned Aerial Systems (UAS). To this end, two UAS are custom-designed and flown for autonomous water sampling, incorporating an octocopter configuration and a ratchet and pawl mechanism for retracting and releasing payloads. This design offers easy-to-use and safe deployment and retraction without continuous motor engagement while eliminating load swinging and the risk of instability by positioning the sampler close to the drone’s center of mass. Furthermore, the system enables autonomous water sampling in remote or inaccessible areas with pre-programmed flight paths and sampling locations, ensuring consistent data collection. The sampling mechanism allows controlled retrieval and release of payloads and further facilitates multi-drone operations to support a variety of sampling missions. This innovative UAS has the potential to revolutionize water sampling and enhance efficiency and safety in environmental monitoring, research, and resource management. Flight tests over several water bodies demonstrate successful simultaneous water sampling missions with two UAS. To the best of the authors’ knowledge, this is the first work to include multiple UAS, each equipped with a payload deployment and retraction mechanism, for autonomous water sampling.

Unmanned SystemsVol. 13(03)
University of Toronto (CA)
Openalex Percentile: Top 43%
Underwater Vehicles and Communication Systems
0.34
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