This article investigates the control and modeling of a multi-rotor fire-fighting system, consisting of a multi-rotor along with a connected water hose. The introduced system has applications in the field of polic operations, including the ability to provide quick services and support to defense teams in dealing with explosions and fires, as well as ensuring public safety. Considering the unpredictable behavior of the hose and aiming for system stability against external disturbances, including reactive forces from water spray, hose weight, and wind force, an adaptive sliding mode control method has been used. The controller has been implemented on a 16-rotor system, taking into account the reactive force from water spray in the design algorithm. The highest range of variations is related to the pitch angle, for which a new structure has been introduced to reduce it and improve the rotational status of the multi-rotor. This new structure has two additional rotors compared to the previous structure. The results obtained from the adaptive sliding mode control method are compared with the Combined Model Reference Adaptive Control (CMRAC) method, which is considered as a linear adaptive robust method. The results confirm the need for the use of a robustness nonlinear algorithm for the intended application in this research. According to the studies, the proposed structure has not been used in any previous research. Additionally, at the end of this research, the justification and necessity of using such a structure in terms of arrangement and number of rotors are explained.

This study presents the conceptual design of an unmanned Multirotor with a novel configuration whose main application is disaster management. This Multirotor can carry 800 kg of cargo for a range of 400 km, and its forward flight speed is 100 km/h. A Multirotor with the specified performance parameters has not yet been developed. Due to the stipulated performance, the electric propulsion system is inapplicable, and a hybrid propulsion system is considered for this Multirotor. The hybrid propulsion system without energy conversion was selected for the proposed design after further investigating alternative hybridization approaches. Multirotor subsystems are conceptually designed based on take-off weight, i.e., take-off weight is first estimated, and then Multirotor subsystems are designed based on it. Finally, the overall subsystems’ weight is compared to the predicted take-off weight. If there is a significant discrepancy, the procedure is repeated until the two values converge. This paper presents the last design loop, and the specifications of the electric propulsion system, fuel propulsion system, power transmission system, body structure, and skid are calculated.

In recent years, the use of air taxis as a suitable solution for transporting cargo and passengers, has been considered especially in short distances and in the city. Complex systems, such as air taxis, are involved in several subsystems with interacting and sometimes conflicting effects are difficult to be derived. Modern optimal design methods such as multidisciplinary design optimization can derive the optimal design while satisfying all the constraints and limitation. In this article, multidisciplinary design optimization of an air taxi is discussed. The optimization framework is selected based on AAO by considering structure, aerodynamics, flight mechanics, propulsion and electrical power. Total mass of air taxi is selected as cost function. Finally, the optimal results are compared and evaluated with the results of two classical design methods including "weight estimation" and " sensitivity of design coefficients". The results confirm the improvement of optimal solution with compare of classical methods.

This paper aimed at presenting a number of suggested improvements that can enhance the performance of a multi-rotor Unmanned Aerial Vehicle. Evaluating each suggestion in terms of the added benefits and feasibility concluded a final choice, which is incorporating a sinusoidal leading-edge profile to the propeller. This choice was numerically investigated with ANSYS Fluent 16. 1 through the SST K-Omega turbulence model. The performance of the modified propeller was assessed by comparing the lift and drag results to the same propeller with a straight leading-edge under the same conditions. Both models were studied at pre-stall and post-stall conditions to see the performance effect with respect to the angle of attack. The findings of this research showed 7% increase in the lift force and coefficient that were associated with the addition of the sinusoidal leading-edge including improved recovery from stall spanning from angle of attack that extends between 10° to 25° . This research also provides more insights into how the delayed stall and improved lift help the Multirotor to extend flight time and carry heavier payloads. It allows for the exploration of the inner working of the sinusoidal leading-edge and its relationship with the flow field over the propeller.

Introduction: Advances in computer vision and the development of remote sensing instruments have made indirect measurement of tree features possible. Individual tree crown delineation is an important step towards information collection and mapping trees in an urban area. This information is then used to help planners design strategies for optimization of urban ecosystem services and adapt to climate changes. Common methods of Individual tree crown delineation (ITCD) were based on very high-resolution satellite or Light Detection and Ranging (LiDAR) data. However, satellite data are usually covered by clouds and thus, cannot be appropriate for the measurement of individual trees. Aerial Laser scanning is also relatively expensive. Remote sensing with unmanned aerial vehicle (UAV) captures low altitude imagery and thus, is potentially capable of mapping complex urban vegetation. Automatic delineation of trees with UAV data makes collection of detailed information from trees in large geographic and urban regions possible. Therefore, a Multirotor UAV equipped with a high-resolution RGB camera was used in the present study to obtain aerial images and delineate individual trees. Materials & Methods: The present study has compared the performance of Inverse watershed segmentation (IWS) and region growing (RG) algorithms using point clouds derived from Structure from Motion (SfM) algorithm and UAV imagery captured with the aim of tree delineation in Fateh urban forest located in Karaj. Region growing (RG) is used to separate regions and distinguish objects in an image. It starts at the initial seed points and determines whether the neighboring pixels should be added to the growing region. If the neighboring pixels are sufficiently similar to the seed pixel, they are labeled as belonging to the seed pixel. To implement the algorithm, the window size and the growing threshold were set for all resolutions. In order to obtain the most appropriate size for the search window, we examined different window sizes with a growing threshold of 0. 5 for each resolution. Individual trees delineation was performed for each CHM resolution in the three different sites using "itcSegment" package of R software. Watershed segmentation algorithm is also similar to RG algorithm. The only difference is that it sets the growing seeds at the local minima. In other words, the local maxima in this algorithm change into local minima and vice versa. Inverse Watershed Segmentation (IWS) method was implemented in ArcGIS 10. 3 because of its capability in delineation of distinct tree entities. In the summer of 2018, three sites with different structures including a mixed uneven-aged dense stand (site 1), a mixed uneven-aged sparse stand (site 2), and a homogeneous even-aged dense stand (site 3) were surveyed and photographed, and a 3D point cloud was extracted from the images. Then, the performance of algorithms was tested using a series of different canopy height models (CHM) with spatial resolutions of 25, 50, 75, 100, and 120 cm. To generate these models, digital surface model (DSM) was subtracted from digital terrain model (DTM). Results of individual tree delineation were validated using data collected in field observation of the aforementioned sites. Results & Discussion: Results indicated that both RG and IWS algorithms yielded their best performance in the dense homogeneous structure. Moreover, the number of segments resulting from CHMs with low resolution was often much more than the actual number of trees. This was due to the occurrence of several peaks within an individual tree crown especially in low resolution images. With an F-score of 0. 88, homogeneous even-aged dense stand (site 3) showed the highest overall accuracy in RG algorithm with a pixel size of 75 cm. Generally, results indicated that RG is an appropriate approach for individual tree delineation due to its flexibility in delineation of varying crown sizes. Furthermore, this method does not assume a circular shape for tree crowns and thus, is capable of detecting and segmenting irregular crowns. Generally, delineation of trees in urban forests using CHMs obtained from UAV-captured aerial imagery was highly accurate in homogeneous sites, while such models lacked efficiency in heterogeneous sites.

Background and Objective: Sisangan forest park is one of the important habitats of Buxus Hyrcana in Iran. Unfortunately, the park has suffered from dieback in recent years, and many Box trees have been destroyed. Monitoring and management of this zone can be effective in controlling, protecting, and supporting it. However, due to the destruction of Box trees, on a large scale, it is not possible to accurately estimate the area using the available data. On the other hand, manual measurements are also very timeconsuming and tedious. Therefore, a way must be found to do this process accurately and automatically. Unmanned aerial vehicles (UAV) have made this possible by using highly accurate sensors (spatial resolution). Another solution that can be used to automatically separate dieback trees from green trees is to use different classification methods. The aim of this study is to prove the ability of low-cost UAV data with conventional sensors to detect and zoning areas that have suffered Dieback. Since the cost of UAVs with multispectral sensors (red edge band and near infrared) is very high, it should be possible to reduce this cost. Since the cost of UAV with multispectral sensors (red-edge and near-infrared band) is very high, it should be possible to reduce this cost. Materials and Methods: Sisangan Forest Park has located 30km to the east of Nowshahr County, Mazandaran province, at latitude 36º 33′ 30″ to 36º 35′ 30″ N, and longitude 51º 47′ to 51º 49′ 30″ E. This park is both a tourist destination and many important plant species of the country grow in it. One of the most important of these species is the Buxus Hyrcana. But unfortunately, in recent years they have become snag due to pests and insect infestations. Multirotor UAVs have been used in this research. The camera installed on this device is capable of capturing 20 megapixel images. Imaging operations were performed on December 28, 2017, at 10: 00 AM, which lasted 45 minutes. The study area was visited for field sampling and its different points were identified in terms of density of snags and preserved Buxus Hyrcana. Then, three circular pieces with a radius of 60 meters and an area of 1. 13 hectares were designed in the zone and the density of snag stands and preserved Buxus Hyrcana stands were determined in these three samples. In each plot, 50 training points were recorded in the places where the Buxus Hyrcana stands were located and also 50 points were recorded in the places where the preserved Buxus Hyrcana stands, floor grass cover, and blackberry was located. In this study, in order to evaluate the accuracy of UAV images in identifying and classifying zones covered with Dieback, the smallest Dieback stands with the smallest canopy width were also recorded. Because UAV images require geometric corrections, they were first corrected geometrically and geographically. They were classified with ENVI software. According to the above explanations, 100 points were recorded in each sample plot, 75 of which were monitored for the classification process and 25 of which were used to evaluate the classification accuracy. Three monitored artificial neural network classification algorithms, maximum likelihood and minimum distance were used to classify these images. Finally, after performing each of the classification steps, a low-pass filter with a size of 3 by 3 pixels was used for smoothing the images. Kappa coefficients and overall accuracy indices were also used to evaluate the results. Results and Discussion: In this number of sample plots, 579 stands were measured. Buxus Hyrcana was by far the most frequent in the zone. European hornbeam, Parrotia persica, and Oak were in the next ranks, respectively. The results showed that the artificial neural network algorithm had the best results compared to the other two algorithms. But the results of the artificial neural network also fluctuate according to the condition of the sample piece. This algorithm with an overall accuracy of 97. 47% and a kappa coefficient of 0. 94 had the best results in the separation and detection of the Buxus Hyrcana snags in the sample plot with the dominance of Buxus Hyrcana snags. After the artificial neural network algorithm, the maximum likelihood algorithm showed more favorable results in separating the Buxus Hyrcana snag stands. The minimum distance algorithm showed good results, but it was not as accurate as of the previous two algorithms. All three algorithms showed poorer results in separating the bases in the sample plot with the dominance of live bases in the sample than the other two sample plots. The sample piece with the predominance of live and green bases compared to the other two sample pieces has more phenomena and effects and in terms of image texture, there are many significant differences compared to the other two sample pieces. All three algorithms showed poorer results in separating the stands in the sample plot by dominance the preserved stands in the sample than the other two sample plots. The sample plot with the predominance of preserved stands compared to the other two sample plots has more phenomena and in terms of image texture compared to the other two sample plots has a lot of significant differences. In this sample plot, in addition to the presence of preserved and snag stands, grass cover and blackberry accessions can also be seen. In this study, the results of classification and detection of Buxus Hyrcana snags using an artificial neural network algorithm were much better than the maximum likelihood and minimum distance algorithms. One of the reasons for the better results of the artificial neural network algorithm is its nonlinearity and non-parametricity. But in classification by traditional algorithms such as statistical methods, they have lower accuracy because they have less flexibility. Parametric types of traditional methods, such as the maximum likelihood algorithm, due to depending on Gaussian statistics, if the data are not normal, cannot have the desired accuracy in classifying and separating classes from each other. In traditional algorithms such as maximum likelihood and minimum distance algorithms, training data play a vital role. In these methods, it is assumed that the distribution within the training samples should be normal so that if this condition cannot be met, the classification accuracy will be greatly reduced. While artificial neural network methods operate based on the characteristics and structure of the data itself. Conclusion: The results of this study showed that using the data and ordinary images of a low-cost UAV, it is possible to study the condition of Dieback after the outbreak of the disease and determine its area. Despite the high cost of purchasing expensive sensors to monitor vegetation status, these methods presented in this article can be done at a much lower cost. This method can be of great help to the relevant institutions in determining the area of snag coatings.