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Prototyping a low-cost open-source autonomous unmanned surface vehicle for real-time water quality monitoring and visualization.

ABSTRACT: A low-cost open-source autonomous unmanned surface vehicle (USV) named "iDroneboat" is developed for real-time monitoring and visualization of water quality. The iDroneboat equipped with Internet of Things (IoT) sensors transmits real-time water quality data, including dissolved oxygen (DO), electronical conductivity (EC), pH, and water temperature (WT) to the cloud for data sharing through Long-term Evolution (LTE) communication protocols. Since material and supplies needed are readily accessible from online marketplaces or local hardware stores, the iDroneboat is easily replicable for local water quality studies and citizen-science activities. The iDroneboat appears to be a promising tool to advance environmental research activities, especially for impaired waterways (e.g., lakes, rivers, and reservoirs). The preliminary result shows that the proposed low-cost platform, iDroneboat, effectively displays water quality components in real-time to the cloud web services (e.g., ThingSpeak), ultimately contributing to citizen science activities and environmental stewardship in water research ecosystems.

SUBMITTER: Ryu JH

PROVIDER: S-EPMC9583578 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Prototyping a low-cost open-source autonomous unmanned surface vehicle for real-time water quality monitoring and visualization.

Ryu Jae Hyeon JH

HardwareX 20221013

A low-cost open-source autonomous unmanned surface vehicle (USV) named "iDroneboat" is developed for real-time monitoring and visualization of water quality. The iDroneboat equipped with Internet of Things (IoT) sensors transmits real-time water quality data, including dissolved oxygen (DO), electronical conductivity (EC), pH, and water temperature (WT) to the cloud for data sharing through Long-term Evolution (LTE) communication protocols. Since material and supplies needed are readily accessib ...[more]

PMID: 36275398

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Project description:BackgroundAutonomous driving is a growing research area that brings benefits in science, economy, and society. Although there are several studies in this area, currently there is no a fully autonomous vehicle, particularly, for off-road navigation. Autonomous vehicle (AV) navigation is a complex process based on application of multiple technologies and algorithms for data acquisition, management and understanding. Particularly, a self-driving assistance system supports key functionalities such as sensing and terrain perception, real time vehicle mapping and localization, path prediction and actuation, communication and safety measures, among others.MethodsIn this work, an original approach for vehicle autonomous driving in off-road environments that combines semantic segmentation of video frames and subsequent real-time route planning is proposed. To check the relevance of the proposal, a modular framework for assistive driving in off-road scenarios oriented to resource-constrained devices has been designed. In the scene perception module, a deep neural network is used to segment Red-Green-Blue (RGB) images obtained from camera. The second traversability module fuses Light Detection And Ranging (LiDAR) point clouds with the results of segmentation to create a binary occupancy grid map to provide scene understanding during autonomous navigation. Finally, the last module, based on the Rapidly-exploring Random Tree (RRT) algorithm, predicts a path. The Freiburg Forest Dataset (FFD) and RELLIS-3D dataset were used to assess the performance of the proposed approach. The theoretical contributions of this article consist of the original approach for image semantic segmentation fitted to off-road driving scenarios, as well as adapting the shortest route searching A* and RRT algorithms to AV path planning.ResultsThe reported results are very promising and show several advantages compared to previously reported solutions. The segmentation precision achieves 85.9% for FFD and 79.5% for RELLIS-3D including the most frequent semantic classes. While compared to other approaches, the proposed approach is faster regarding computational time for path planning.

Project description:The study of animal locomotion can be logistically challenging, especially in the case of large or unhandleable animals in uncontrolled environments. Here we demonstrate the utility of a low cost unmanned aerial vehicle (UAV) in measuring two-dimensional running kinematics from free-roaming giraffes (Giraffa camelopardalis giraffa) in the Free State Province, South Africa. We collected 120 Hz video of running giraffes, and calibrated each video frame using metatarsal length as a constant object of scale. We tested a number of methods to measure metatarsal length. The method with the least variation used close range photography and a trigonometric equation to spatially calibrate the still image, and derive metatarsal length. In the absence of this option, a spatially calibrated surface model of the study terrain was used to estimate topographical dimensions in video footage of interest. Data for the terrain models were collected using the same equipment, during the same study period. We subsequently validated the accuracy of the UAV method by comparing similar speed measurements of a human subject running on a treadmill, with treadmill speed. At 8 m focal distance we observed an error of 8% between the two measures of speed. This error was greater at a shorter focal distance, and when the subject was not in the central field of view. We recommend that future users maximise the camera focal distance, and keep the subject in the central field of view. The studied giraffes used a grounded rotary gallop with a speed range of 3.4-6.9 ms-1 (never cantering, trotting or pacing), and lower duty factors when compared with other cursorial quadrupeds. As this pattern might result in adverse increases in peak vertical limb forces with speed, it was notable to find that contralateral limbs became more in-phase with speed. Considering the latter pattern and the modest maximal speed of giraffes, we speculate that tissue safety factors are maintained within tolerable bounds this way. Furthermore, the angular kinematics of the neck were frequently isolated from the pitching of the body during running; this may be a result of the large mass of the head and neck. Further field experiments and biomechanical models are needed to robustly test these speculations.

Dataset Information

Prototyping a low-cost open-source autonomous unmanned surface vehicle for real-time water quality monitoring and visualization.

Publications

Prototyping a low-cost open-source autonomous unmanned surface vehicle for real-time water quality monitoring and visualization.

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