Inexpensive Unmanned Aerial Vehicles Research Articles

Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery

The identification of flood hazards during emerging public safety crises such as hurricanes or flash floods is an invaluable tool for first responders and managers yet remains out of reach in any comprehensive sense when using traditional remote-sensing methods, due to cloud cover and other data-sourcing restrictions. While many remote-sensing techniques exist for floodwater identification and extraction, few studies demonstrate an up-to-day understanding with better techniques in isolating the spectral properties of floodwaters from collected data, which vary for each event. This study introduces a novel method for delineating near-real-time inundation flood extent and depth mapping for storm events, using an inexpensive unmanned aerial vehicle (UAV)-based multispectral remote-sensing platform, which was designed to be applicable for urban environments, under a wide range of atmospheric conditions. The methodology is demonstrated using an actual flooding-event—Hurricane Zeta during the 2020 Atlantic hurricane season. Referred to as the UAV and Floodwater Inundation and Depth Mapper (FIDM), the methodology consists of three major components, including aerial data collection, processing, and flood inundation (water surface extent) and depth mapping. The model results for inundation and depth were compared to a validation dataset and ground-truthing data, respectively. The results suggest that UAV-FIDM is able to predict inundation with a total error (sum of omission and commission errors) of 15.8% and produce flooding depth estimates that are accurate enough to be actionable to determine road closures for a real event.

CNN-Based Dense Monocular Visual SLAM for Real-Time UAV Exploration in Emergency Conditions

Unmanned Aerial Vehicles (UAVs) for 3D indoor mapping applications are often equipped with bulky and expensive sensors, such as LIDAR (Light Detection and Ranging) or depth cameras. The same task could be also performed by inexpensive RGB cameras installed on light and small platforms that are more agile to move in confined spaces, such as during emergencies. However, this task is still challenging because of the absence of a GNSS (Global Navigation Satellite System) signal that limits the localization (and scaling) of the UAV. The reduced density of points in feature-based monocular SLAM (Simultaneous Localization and Mapping) then limits the completeness of the delivered maps. In this paper, the real-time capabilities of a commercial, inexpensive UAV (DJI Tello) for indoor mapping are investigated. The work aims to assess its suitability for quick mapping in emergency conditions to support First Responders (FR) during rescue operations in collapsed buildings. The proposed solution only uses images in input and integrates SLAM and CNN-based (Convolutional Neural Networks) Single Image Depth Estimation (SIDE) algorithms to densify and scale the data and to deliver a map of the environment suitable for real-time exploration. The implemented algorithms, the training strategy of the network, and the first tests on the main elements of the proposed methodology are reported in detail. The results achieved in real indoor environments are also presented, demonstrating performances that are compatible with FRs’ requirements to explore indoor volumes before entering the building.

Optimal Dynamic Recharge Scheduling for Two-Stage Wireless Power Transfer

Many industrial-Internet-of-Things applications require autonomous operation and incorporate devices in inaccessible locations. Recent advances in wireless power transfer (WPT) and autonomous vehicle technologies, in combination, have the potential to solve a number of residual problems concerning the maintenance of, and data collection from embedded devices. Equipping inexpensive unmanned aerial vehicles (UAV) and embedded devices with subsystems to facilitate WPT allows a UAV to become a viable mobile power delivery vehicle (PDV) and data collection agent. A key challenge is, therefore, to ensure that a PDV can optimally schedule power delivery across the network, such that it is as reliable and resource efficient as possible. To achieve this and out-perform naive on-demand recharging strategies, in this article, we propose a two-stage wireless power network (WPN) approach in which a large network of devices may be grouped into small clusters, where packets of energy inductively delivered to each cluster by the PDV are acoustically distributed to devices within the cluster. In this article, we describe a novel dynamic recharge scheduling algorithm that combines genetic weighted clustering with nearest neighbor search to jointly minimize PDV travel distance and WPT losses. The efficacy and performance of the algorithm are evaluated in simulation using experimentally derived traces, and the algorithm is shown to achieve ~ 90% throughput for large, dense networks.

RIVER MORPHOLOGY MONITORING OF A SMALL-SCALE ALPINE RIVERBED USING DRONE PHOTOGRAMMETRY AND LIDAR

Abstract. An efficient alternative to labour-intensive terrestrial and costly airborne surveys is the use of small, inexpensive Unmanned Aerial Vehicles (UAVs) or Remotely Piloted Aerial Systems (RPAS). These low-altitude remote sensing platforms, commonly known as drones, can carry lightweight optical and LiDAR sensors. Even though UAV systems still have limited endurance, they can provide a flexible and relatively inexpensive monitoring solution for a limited area of interest. This study investigated the applicability of monitoring the morphology of a frequently changing glacial stream using high-resolution topographic surface models derived from low-altitude UAV-based photogrammetry and LiDAR. An understanding of river-channel morphology and its response to anthropogenic and natural disturbances is imperative for effective watershed management and conservation. We focus on the data acquisition, processing workflow and highlight identified challenges and shortcomings. Additionally, we demonstrate how LiDAR data acquisition simulations can help decide which laser scanning approach to use and help optimise data collection to ensure full coverage with desired level of detail. Lastly, we showcase a case study of 3D surface change analysis in an alpine stream environment with UAV-based photogrammetry. The datasets used in this study were collected as part of the ISPRS Summer School of Alpine Research, which will continue to add new data layers on a biyearly basis. This growing data repository is freely available for research.

Self-Navigating UAVs for Supervising Moving Objects over Large-Scale Wireless Sensor Networks

Increasingly inexpensive unmanned aerial vehicles (UAVs) are helpful for searching and tracking moving objects in ground events. Previous works either have assumed that data about the targets are sufficiently available, or they solely rely on on-board electronics (e.g., camera and radar) to chase them. In a searching mission, path planning is essentially preprogrammed before taking off. Meanwhile, a large-scale wireless sensor network (WSN) is a promising means for monitoring events continuously over immense areas. Due to disadvantageous networking conditions, it is nevertheless hard to maintain a centralized database with sufficient data to instantly estimate target positions. In this paper, we therefore propose an online self-navigation strategy for a UAV-WSN integrated system to supervise moving objects. A UAV on duty exploits data collected on the move from ground sensors together with its own sensing information. The UAV autonomously executes edge processing on the available data to find the best direction toward a target. The designed system eliminates the need of any centralized database (fed continuously by ground sensors) in making navigation decisions. We employ a local bivariate regression to formulate acquired sensor data, which lets the UAV optimally adjust its flying direction, synchronously to reported data and object motion. In addition, we also construct a comprehensive searching and tracking framework in which the UAV flexibly sets its operation mode. As a result, least communication and computation overhead is actually induced. Numerical results obtained from NS-3 and Matlab cosimulations have shown that the designed framework is clearly promising in terms of accuracy and overhead costs.

Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning

Non-destructive crop monitoring over large areas with high efficiency is of great significance in precision agriculture and plant phenotyping, as well as decision making with regards to grain policy and food security. The goal of this research was to assess the potential of combining canopy spectral information with canopy structure features for crop monitoring using satellite/unmanned aerial vehicle (UAV) data fusion and machine learning. Worldview-2/3 satellite data were tasked synchronized with high-resolution RGB image collection using an inexpensive unmanned aerial vehicle (UAV) at a heterogeneous soybean (Glycine max (L.) Merr.) field. Canopy spectral information (i.e., vegetation indices) was extracted from Worldview-2/3 data, and canopy structure information (i.e., canopy height and canopy cover) was derived from UAV RGB imagery. Canopy spectral and structure information and their combination were used to predict soybean leaf area index (LAI), aboveground biomass (AGB), and leaf nitrogen concentration (N) using partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) with a newly proposed activation function. The results revealed that: (1) UAV imagery-derived high-resolution and detailed canopy structure features, canopy height, and canopy coverage were significant indicators for crop growth monitoring, (2) integration of satellite imagery-based rich canopy spectral information with UAV-derived canopy structural features using machine learning improved soybean AGB, LAI, and leaf N estimation on using satellite or UAV data alone, (3) adding canopy structure information to spectral features reduced background soil effect and asymptotic saturation issue to some extent and led to better model performance, (4) the ELR model with the newly proposed activated function slightly outperformed PLSR, RFR, and SVR in the prediction of AGB and LAI, while RFR provided the best result for N estimation. This study introduced opportunities and limitations of satellite/UAV data fusion using machine learning in the context of crop monitoring.

Accuracy assessment of different photogrammetric software for processing data from low-cost UAV platforms in forest conditions

Abstract: To obtain precise cartometric measurements of forests is always a challenge and high-resolution data from Unmanned Aerial Vehicle (UAV) is currently the quickest method. Generation of a fine quality orthomosaic of the acquired image series is a pre-requisite for full exploitation of such data. This study examines six of the most frequently used photogrammetric software for popular and inexpensive UAV systems. It is assumed that ground control points (GCPs) are not required. The average Root Mean Square Error (RMSE) for raw orthophoto was 1.24 m and around 0.2 m precision for both X and Y axes. Additionally, the accuracy of UAV internal GNSS receiver was checked on reference points which slightly exceeds 2 m RMSE. The range of accuracy and precision of orthomosaic are provided as a valuable reference for the use of low-cost UAV in forest inventory.

TOWARDS THE AUTOMATIC DETECTION OF GEOSPATIAL CHANGES BASED ON DIGITAL ELEVATION MODELS PRODUCED BY UAV IMAGERY

Abstract. Reliable and accurate geospatial-databases (Digital Elevation Models, DEMs) are an essential component of Geographic Information Systems (GIS). One of their most important uses is change detection – an invaluable tool for environmental interpretation and evidence-based action. High-performance and inexpensive Unmanned Aerial Vehicles (UAVs) are increasingly used for the acquisition of timely geospatial information (imagery) for the production of DEMs for geospatial change detection. DEMs produced from UAV imagery have very high resolution and very good internal accuracy. However, their absolute location accuracy is inferior to other mapping technologies. Therefore, existing change detection methods, which are based on the point-by-point comparison, will perform poorly when processing DEMs created from UAV imagery since they are limited in reliably separating real physical changes from artifacts related to DEM inherent inaccuracy or errors. This paper presents a novel methodology that overcomes these deficiencies, by implementing a hierarchical analysis and modeling process, in which a sequence of methods is used to automatically identify and match unique homological features, such as building corners or topographic maxima, in the various height models. These provide geospatial anchors that bring out local geospatial discrepancies between the models. Those are then used to "repair" (align) the models to the same geospatial reference system, at which point change-detection is performed. Experimental results showed that when calculating point-by-point height differences, 98.99% of the area was falsely classified as changed, whereas implementing our method adequately detected all the actual changes in the area with no false positives, correctly classifying 0.16% of the area as changed.

A Special Issue on Hyperspectral Imaging [From the Guest Editors

Hyperspectral imaging (HSI) for geoscience remote sensing and Earth observation has been around since the mid-1980s. For many years, it was the purview of research laboratories and proof-of-concept satellite missions. Hundreds of research articles have been published exploring algorithms for analysis and demonstrating applications across many Earth observational fields of interest. Most have focused on a relatively small group of publicly available data sets due to the high cost of sensor operation and collection of ground reference data necessary for algorithm and product evaluation. More recently, as the cost of focal planes and sensor technology has decreased, together with the emergence of inexpensive unmanned aerial vehicle (UAV) systems and Cube- Sats/smallSats in space access, the availability of hyperspectral imagery is poised to grow dramatically. Several airborne and spaceborne systems have been deployed or are being planned. Commercial remote sensing and image processing software systems provide more support and extended functionality for such data, while the practical use of hyperspectral data in both commercial and scientific applications has increased. The community now has access to relatively low-cost UAV systems and data and will soon see more routine availability of space-based HSI data. This special issue of IEEE Geoscience and Remote Sensing Magazine on HSI was put together based on the recognition that the field is on the cusp of expanded data availability. While formerly the domain of a relatively small group of specialists, it is anticipated HSI data will soon become available to a wider range of scientists and data users. This special issue was compiled to bring together a comprehensive look at the characteristics, phenomenology, algorithms, and applications as a resource for practitioners and students who wish to gain familiarity with the field.

The potential of unmanned aerial systems for sea turtle research and conservation: a review and future directions

The use of satellite systems and manned aircraft surveys for remote data collection has been shown to be transformative for sea turtle conservation and research by enabling the collection of data on turtles and their habitats over larger areas than can be achieved by surveys on foot or by boat. Unmanned aerial vehicles (UAVs) or drones are increasingly being adopted to gather data, at previously unprecedented spatial and temporal resolutions in diverse geographic locations. This easily accessible, low-cost tool is improving existing research methods and enabling novel approaches in marine turtle ecology and conservation. Here we review the diverse ways in which incorporating inexpensive UAVs may reduce costs and field time while improving safety and data quality and quantity over existing methods for studies on turtle nesting, at-sea distribution and behaviour surveys, as well as expanding into new avenues such as surveillance against illegal take. Furthermore, we highlight the impact that high-quality aerial imagery captured by UAVs can have for public outreach and engagement. This technology does not come without challenges. We discuss the potential constraints of these systems within the ethical and legal frameworks which researchers must operate and the difficulties that can result with regard to storage and analysis of large amounts of imagery. We then suggest areas where technological development could further expand the utility of UAVs as data-gathering tools; for example, functioning as downloading nodes for data collected by sensors placed on turtles. Development of methods for the use of UAVs in sea turtle research will serve as case studies for use with other marine and terrestrial taxa.

Printed unmanned aerial vehicles using paper-based electroactive polymer actuators and organic ion gel transistors.

We combined lightweight and mechanically flexible printed transistors and actuators with a paper unmanned aerial vehicle (UAV) glider prototype to demonstrate electrically controlled glide path modification in a lightweight, disposable UAV system. The integration of lightweight and mechanically flexible electronics that is offered by printed electronics is uniquely attractive in this regard because it enables flight control in an inexpensive, disposable, and easily integrated system. Here, we demonstrate electroactive polymer (EAP) actuators that are directly printed into paper that act as steering elements for low cost, lightweight paper UAVs. We drive these actuators by using ion gel-gated organic thin film transistors (OTFTs) that are ideally suited as drive transistors for these actuators in terms of drive current and frequency requirements. By using a printing-based fabrication process on a paper glider, we are able to deliver an attractive path to the realization of inexpensive UAVs for ubiquitous sensing and monitoring flight applications.

Object-based spatiotemporal analysis of vine canopy vigor using an inexpensive unmanned aerial vehicle remote sensing system

Remotely sensed imagery provides a rapid assessment of spatial variability in grapevine canopy vigor that correlates with crop performance. Unmanned aerial vehicles (UAVs) provide a low-cost image acquisition platform with high spatial and temporal resolutions. Using a UAV and digital cameras, aerial images of a Texas vineyard were captured at postflowering, veraison, and harvest. Imagery was processed to generate orthophotos in units of reflectance, which were then segmented to extract per-vine estimates of canopy area (planimetric extent) and normalized difference vegetation index (NDVI)-based canopy density. Derived canopy area and density values were compared to the harvest variables of number of clusters, cluster size, and yield to explore correlations. Planimetrically derived canopy area yielded significant, positive relationships, whereas NDVI-based canopy density exhibited no significant relationships due to sensor-related radiometric inaccuracy. A vine performance index was calculated to map spatial variation in canopy vigor for the entire growing season. Future management zones were delineated using spatial grouping analysis.

A Fluid Dynamic Based Coordination of a Wireless Sensor Network of Unmanned Aerial Vehicles: 3-D Simulation and Wireless Communication Characterization

A fluid dynamic algorithm based on smoothed particle hydrodynamics (SPH) is proposed for coordination of a team of unmanned aerial vehicles (UAVs) in a wireless sensor network. SPH is a Lagrangian particle method typically used to model compressible and quasi-incompressible fluid flows. In this study, SPH is used to develop a decentralized controller for a swarm of fixed-wing UAVs, which move in 3-D space under constraints of airspeed and turning radius. Vector field path-following is used to guide the swarm towards the goal. We investigate circular, racetrack and counter-rotating loiter patterns for the UAVs in the goal region. This fluid dynamics coordination treatment allows UAVs to avoid collisions with obstacles and other flying UAVs. 3-D simulations are used to test the SPH-based control algorithm. Simulations were used to explore special cases, such as the modeling of obstacles with virtual SPH particles, and the use of a variable kernel to control the inter-vehicle separation. Finally, an aerial mobile sensor network is set up using SPH as the control mechanism, and an experimental characterization of air-to-air and air-to-ground communications is conducted. The experiments use two ground stations and three Delta-wing UAVs with a wingspan of 32 inches as nodes. Each node has a IEEE 802.15.4 ZigBee radio operating in the 2.4 GHz band. The low computational costs involved in the distributed SPH-based control algorithm make it an attractive option for implementation on simple inexpensive microprocessors. The results of simulations and experiments demonstrate the viability of setting up a mobile sensor network of inexpensive UAVs based on SPH.

Aerial surveys and tagging of free-drifting icebergs using an unmanned aerial vehicle (UAV)

Ship-based observations of free-drifting icebergs are hindered by the dangers of calving ice. To improve the efficacy and safety of these studies, new unmanned aerial vehicles (UAVs) were developed and then deployed in the Southern Ocean. These inexpensive UAVs were launched and recovered from a ship by scientific personal with a few weeks of flight training. The UAVs sent real-time video back to the ship, allowing researchers to observe conditions in regions of the icebergs not visible from the ship. In addition, the UAVs dropped newly developed global positioning system (GPS) tracking tags, permitting researchers to record the precise position of the icebergs over time. The position reports received from the tags show that the motion of free-drifting icebergs changes rapidly and is a complex combination of both translation and rotation.