Unmanned Aerial Vehicle Flight Path Research Articles

An unmanned aerial vehicle autonomous flight trajectory planning method and algorithm for the early detection of the ignition source during fire monitoring

ABSTRACT Timely identification of a fire’s origin in its initial stages is essential for minimizing human as well as material losses. Hence, formulating a methodology and algorithm for autonomously planning the flight path of an unmanned aerial vehicle (UAV) during fire monitoring to identify ignition sources early is a pressing objective. The proposed ignition detection method comprises three flight plans (FPs); Flight Plan A (FPA) involves surveying the monitored area with tacks, and assessing harmful substance concentrations in each pixel. Upon identifying a pixel surpassing the threshold concentration, Flight Plan B (FPB) directs the UAV control for targeted navigation, employing local planning based on calculating local differential operators within a nine-element mask. FPB enables the UAV to directly reach and pinpoint the fire source coordinates. Flight Plan C (FPC) outlines the UAV’s return journey to the departure point from any pixel within the monitoring zone. A multi-criteria algorithm for UAV flight path control has been devised, facilitating the determination of local target pixels and subsequent Local Flight Plan (LFP) construction. The guiding principle for constructing an LFP is the ‘at least three pixels on the tack’ rule, yielding a nine-element matrix with known harmful substance concentrations in the target pixel. It enables the identification of pixels within the LFP where obtaining this matrix is unattainable. Mathematical modelling of the UAV flight control algorithm, as per the proposed methodology, was executed using MATLAB® R2019b, demonstrating control stability and accelerated attainment of ignition source pixel coordinates. The achieved speed surpassed the set goal by 1.5 to 2 times, contingent on the ignition source’s location concerning the monitored territory’s flyby direction.

Real-Time Unmanned Aerial Vehicle-Based Traffic State Estimation for Multi-Regional Traffic Networks

Traffic state estimation is a challenging task because of the collection of sparse and noisy measurements from fixed points in the traffic network. Induction loops, as they are non-intrusive, can observe any area of the traffic network on demand and provide accurate traffic density and speed measurements. Our main contribution is the development of an optimization framework where small parts of the traffic network are monitored by Unmanned Aerial Vehicles (UAVs) and accurate estimates of traffic density and mean speeds for every region in the traffic network are returned in real-time. Assuming regional-based traffic dynamics, a cyclical UAV flight path is defined for each region. One UAV is assigned to each flight path and monitors a small area of the region below. The UAV-based traffic measurements are expressed as moving averages to smooth out fluctuations in traffic density and mean speed. A moving horizon optimization problem is formulated, which minimizes the estimation and process errors over a moving time window. The problem is non-convex and challenging to solve, because of the presence of nonlinear traffic dynamics. By considering free-flow conditions, the optimization problem is recast to a quadratic program that returns density estimations for each region of the traffic network in real-time. Simulation results compare our UAV framework to an alternative, where the whole traffic network is monitored by UAVs. Both frameworks obtain similar results, despite the alternative framework using more UAVs than our framework.

Collaborative relay for achieving long-term and low-AoI data collection in UAV-aided IoT systems

In Internet of Things (IoT) systems, sensor nodes are frequently placed in remote and unattended locations to monitor environmental data. One significant challenge is ensuring the timely and efficient transmission of data generated by these sensor nodes back to the base station. The use of unmanned aerial vehicles (UAVs) can provide a practical solution to this challenge by acting as mobile relay nodes for facilitating data transmission. In most existing works, UAVs are typically restricted to collecting data within their designated areas and returning to the base station for data offloading, resulting in suboptimal data timeliness due to long-distance flights. A limited number of works have explored the utilization of relay collaboration by UAVs for data collection, enabling efficient and immediate transmission of sensor node data to the base station. Nevertheless, UAVs positioned at significant distances from the base station face challenges in obtaining timely energy replenishment. This makes them unable to effectively support long-duration data collection missions. In order to tackle these challenges, we develop a UAV-aided IoT collaborative data collection mechanism and propose a matching games-based data collection (MGDC) scheme. In this scheme, we begin by identifying convergence nodes within the ground sensor network, responsible for uploading sensor-generated data to passing UAVs. Furthermore, we divide the mission area into multiple subareas based on the number of available UAVs. Subsequently, using a matching game algorithm, we establish relay relationships between UAVs to enable efficient relay transmissions among paired UAVs. To achieve efficient data collection of UAVs, we employ an improved adaptive large neighborhood search (IALNS) algorithm for UAV flight path planning. Finally, we incorporate an alternating charging mode to ensure all UAVs have the opportunity to return to the base station for energy recharge. Through comprehensive experimentation, we confirm the significant enhancement provided by our proposed data collection scheme compared to existing schemes. This scheme effectively reduces system age of information (AoI) and extends the runtime of the system.

A Simulation Framework of Unmanned Aerial Vehicles Route Planning Design and Validation for Landslide Monitoring

Unmanned aerial vehicles (UAVs) have emerged as a highly efficient means of monitoring landslide-prone regions, given the growing concern for urban safety and the increasing occurrence of landslides. Designing optimal UAV flight routes is crucial for effective landslide monitoring. However, in real-world scenarios, the testing and validating of flight path planning algorithms incur high cost and safety concerns, making overall flight operations challenging. Therefore, this paper proposes the use of the Unreal Engine simulation framework to design UAV flight path planning specifically for landslide monitoring. It aims to validate the authenticity of the simulated flight paths and the correctness of the algorithms. Under the proposed simulation framework, we then test a novel flight path planning algorithm. The simulation results demonstrate that the model reconstruction obtained using the novel flight path algorithm exhibits more detailed textures, with a 3D model simulation accuracy ranging from 10 to 14 cm. Among them, the RMSE value of the novel flight route algorithm falls within the range of 10 to 11 cm, exhibiting a 2 to 3 cm improvement in accuracy compared to the traditional flight path algorithm. Additionally, it effectively reduces the flight duration by 9.3% under the same flight path compared to conventional methods. The results confirm that the simulation framework developed in this paper meets the requirements for landslide damage monitoring and validates the feasibility and correctness of the UAV flight path planning algorithm.

Enabling large-scale low-power LoRa data transmission via multiple mobile LoRa gateways

Low-Power Wide Area Networks (LPWANs) enable large-scale Internet of Things (IoT) applications. LoRa is a promising LPWAN technology, but LoRa nodes are battery-powered, so their lifetime depends on energy efficiency. In practice, LoRa nodes have short battery life because long transmission distances to gateways incur high energy costs. With expensive battery replacement, particularly in large networks, it is crucial to reduce LoRa nodes’ energy consumption. Existing low-power transmission techniques focus on static or single mobile gateways, consuming substantial remote node energy unfit for large-scale networks. This paper proposes integrating LoRa with mobility to minimize node energy use by shortening transmission distances. We design the first large-scale mobile LoRa system, MLoRaDrone, using multiple unmanned aerial vehicles (UAVs) equipped with LoRa gateways flying near nodes. First, we present a low-power mechanism for reliable node sensing and UAV communication. Next, we develop a LoRa transmission parameter assignment strategy encompassing a distributed ADMM-based optimal transmission policy and a 32-approximation channel allocation. Moreover, we propose a UAV trajectory scheduling scheme with a 4-approximation minimizing node energy and UAV flight paths. Evaluations on varied scales verify MLoRaDrone effectiveness for different node counts and UAV paths. Compared to baselines, MLoRaDrone reduces 3000-node energy consumption by up to 35.56× within 25 km2.

Path Planning with Multiple UAVs Considering the Sensing Range and Improved K-Means Clustering in WSNs

Recently, an Unmanned Aerial Vehicle (UAV)-based Wireless Sensor Network (WSN) for data collection was proposed. Multiple UAVs are more effective than a single UAV in wide WSNs. However, in this scenario, many factors must be considered, such as collision avoidance, the appropriate flight path, and the task time. Therefore, it is important to effectively divide the mission areas of the UAVs. In this paper, we propose an improved k-means clustering algorithm that effectively distributes sensors with various densities and fairly assigns mission areas to UAVs with comparable performance. The proposed algorithm distributes mission areas more effectively than conventional methods using cluster head selection and improved k-means clustering. In addition, a postprocessing procedure for reducing the path length during UAV path planning for each mission area is important. Thus, a waypoint refinement algorithm that considers the sensing ranges of the sensor node and the UAV is proposed to effectively improve the flight path of the UAV. The task completion time is determined by evaluating how the UAV collects data through communication with the cluster head node. The simulation results show that the mission area distribution by the improved k-means clustering algorithm and postprocessing by the waypoint refinement algorithm improve the performance and the UAV flight path during data collection.

Research on logistics of intelligent unmanned aerial vehicle integration system

As Unmanned Aerial Vehicle (UAV) is more and more frequently used in farming and logistics, civil and military alike, researches involving UAVs also starts to boom. In the civil field, UAV is generally flown in urban areas, so buildings are the main factors hindering the normal flight of UAV. Therefore, it is necessary to calculate an optimal flight path of UAV under constraints. The intelligent logistics UAV proposed in this paper can be used to replace special couriers to deliver small goods. It is a quadcopter integrated with a webcam, ultrasound Ground Proximity Warning System (GPWS), and is controllable through a mobile APP. Gradient descent algorithm, Linear Quadratic Regulator (LQR) and improved Proportion-Integral-Derivative (PID) controllers are applied in its flight control system, MATLAB and wavelet transform to handle fuzzy image. In addition, ant colony algorithm and adaptive strategies are used in the path planning process. As a result, it can detect surrounding obstacles in-flight, and the ground control station can receive feedback information and prepare for emergency operations.

Fresh data collection for UAV-assisted IoTs based on proximity-remote region collaboration

Unmanned Aerial Vehicles (UAVs) can assist Internet of Things (IoT) systems in collecting data from sensor nodes due to their high flexibility and ease of deployment. However, in existing UAV-assisted IoTs, there is neither mission overlap nor mission collaboration between UAVs, which requires all UAVs to return to the base station to offload the collected data. This results in significant latency in data transmission. Therefore, we propose a proximity-remote region collaborative data collection scheme to overcome this problem. In this scheme, we first divide the mission region into the proximity region and the remote region, and deploy an appropriate ratio of UAVs in each region for data collection. Second, we balance the flight paths of UAVs in different regions by updating the partition. Finally, we achieve seamless relay transmission between UAVs by selecting relay points, modifying UAV flight paths, and adjusting flight speed and take-off points. Through the above process, data collected from the remote region can rapidly reach the base station through seamless collaborative relays between UAVs. Extensive experimental results have shown that the proposed scheme can effectively reduce the data transmission latency of the system compared to existing data collection schemes.

Energy and Nvidia Sionna-Based Signal Integrity: Selection Design for Off-the-Shelf Synthetic Aperture Radar-Capable Unmanned Aerial Vehicles

This paper presents the relationship between energy and signal integrity of Unmanned Aerial Vehicles (UAVs) with Synthetic Aperture Radar (SAR) capabilities for a more informed off-the-shelf selection design. The energy is expressed in the UAV and SAR power consumption, whereas signal integrity is expressed in Bit-Error Rate (BER). Low-end, mid-end, and highend—categorized in terms of their market cost—commercial UAVs and SAR modules were considered. The energy relations are based on the UAV flight path and power ratings. The signal processing and decomposition of the reconstructed objects for the model simulations are based on a transmit channel-receive chain of 16-quadrature-amplitude, low-density parity check, Rayleigh response, and single-antenna directivity done through Nvidia Sionna. The results indicate that far-reaching flight spatial path scanning is achieved with high-end UAVs and low-cost SARs, but the accuracy is relatively low. Higher accuracy is achieved with low-end UAVs with high-cost SARs. Further, the results point out that selecting UAV-SAR is critical in terms of energy and accuracy for a specific target application. The outcomes show tradeoffs in the selection design.

Optimised path planning of a UAV for inventory management applications

Inventory management in warehouses is a crucial task in the logistics industry. Manual stocktaking in larger-scale warehouses can be time-consuming and labour-intensive. To automate this process, unmanned aerial vehicles (UAVs) have gained popularity due to their potential to offer a safer, timeous, and more efficient solution. However, deploying drone systems can face challenges and therefore requires planning tasks such as path planning. This study investigates two commonly used UAV flight paths to identify the optimal path within a warehouse: zigzag and up-down flight paths. A Gazebo simulation was considered with a six-rotor UAV model to analyse the different flight paths. The accuracy of both path types is measured for comparison, and flight times were considered as a means for optimisation. The results indicated that the zigzag flight path is the most optimal with the shortest flight time. The study found that the zigzag path resulted in a 27.25% shorter estimated flight time compared to the up-down path.

Real-time unmanned aerial vehicle flight path prediction using a bi-directional long short-term memory network with error compensation

Abstract In recent years, unmanned aerial vehicle (UAV) autonomous flight technology has been applied in many fields. However, in the process of autonomous operation, the UAV may deviate from the set flight path due to various disturbance factors, which results in mission failure. In order to find the abnormal situation in time and take corresponding measures, it is necessary to monitor the operation state of the UAV. Predicting the UAV flight path is the main monitoring method at present; however, the accuracy and real-time of the existing prediction methods are limited. Therefore, this paper proposes an error compensation Bessel bidirectional long short-term memory real-time path prediction model deployed in ground stations. First, because of inconsistency of the units in all directions of the original positioning information provided by global positioning system, the Bessel geodetic coordinate transformation is introduced to unify the units of three-dimensional coordinate data, so as to improve the prediction accuracy. Second, considering the problems of poor data quality and data missing in the operation process, the least square fitting method is used to supplement and correct the positioning coordinate data to obtain more reliable and accurate path observation values as the model input. Finally, a deep learning path prediction model based on bi-directional long short-term memory (BiLSTM) network is constructed, and the appropriate network parameters are determined with the prediction accuracy and time as evaluation indicators. In order to further improve the prediction accuracy, a compensator based on proportional integral differential error control theory is designed according to the output characteristics of the BiLSTM network, which is used for providing compensation values for the prediction results of the model. The training and testing results using the actual flight data of UAV operation show that, under the experimental environment built, the model proposed in this paper can complete the UAV flight path prediction with root mean square error < 1 meter within 0.1 second, and has better performance and higher prediction accuracy than other neural network models.

A novel UAV path planning approach: Heuristic crossing search and rescue optimization algorithm

Unmanned aerial vehicle (UAV) path planning plays an important role in the flight process of an UAV, which needs an effective algorithm to deal with UAV path planning problem. The search and rescue optimization algorithm (SAR) is easy to implement and has the characteristics of flexible, but it has slow convergence speed and has not been applied to UAV path planning. To address these problems, a heuristic crossing search and rescue optimization algorithm (HC-SAR) is proposed. HC-SAR combines a heuristic crossover strategy with the basic SAR to improve the convergence speed and maintain the population diversity in the optimization process. Furthermore, a real-time path adjustment strategy is proposed to straighten the UAV flight path. In addition, cubic B-spline interpolation is used to smooth the generated path. Comprehensive experiments including two-dimensional and three-dimensional environments for different threat zone are conducted to validate the performance of HC-SAR. The results show that HC-SAR has a high convergence speed and can successfully obtain a safe and efficient path, and it significantly outperforms SAR, differential evolution (DE), ant lion optimizer (ALO), squirrel search algorithm (SSA) and salp swarm algorithm (SSA) in all the cases. These results suggest that the proposed algorithm can effectively serve as an alternative for solving UAV path planning problem.

Integrating UAV and BIM for automated visual building inspection: a systematic review and conceptual framework

Unmanned Aerial Vehicle (UAV) has gained popularity in visual building inspection due to its unique advantages of high mobility and low cost. However, it has been a challenge to efficiently manage the large number of images collected by the camera-equipped UAV for analysis and support building condition assessment. One potential solution is to manage the UAV data and building data through the platform of Building Information Modelling (BIM). However, little research has been found that successfully combined UAV and BIM for the visual inspection of buildings. This research explores the state-of-the-art UAV and BIM for visual building inspection through a systematic literature review. A mixed quantitative-qualitative analysis is conducted to provide insights into the application of UAV and BIM in visual building inspection based on identified academic publications (i.e., 48 articles on UAV, 31 articles on BIM, and 4 articles on integrating UAV and BIM). Furthermore, challenges and possible research opportunities are highlighted to guide future research: (1) integrating UAV and BIM to automate the visual building inspection process, including the data collection and data management; (2) considering the safety concern induced by the complex surrounding environment for the BIM-based UAV flight path planning; (3) developing an efficient way for managing UAV images in BIM. In addition, a conceptual framework for integrating UAV and BIM towards automated visual building inspection is proposed to serve as a roadmap.

UAV Formation Transformation Based on Two-Stage Path Planning Method

With the maturity of unmanned aerial vehicle (UAV) technology, the application of UAV in various fields is more in-depth. A method to quickly and accurately solve the flight paths of a large number of UAVs from one formation to another is crucial for application scenarios that require UAVs to perform formation transformation tasks. In this paper, the path planning of UAV swarm formation transformation is divided into two stages: rough planning and fine planning. In the rough planning stage, a 0-1 variable planning model is established to match the start and end points of the UAV flight path, and the model is solved using a Strengthen Elitist GA Algorithm to quickly obtain high-quality feasible solutions. In the fine planning stage, four models including behavioral mode, obstacle avoidance criterion, optimal waypoint selection and target exchange are established. On this basis, a heuristic fixed-step space search algorithm is designed and implemented to plan the path finely, and the path planning for the UAV formation transformation in two scenarios is finally completed.

Research on multi-functional logistics intelligent Unmanned Aerial Vehicle

As Unmanned Aerial Vehicle (UAV) is more and more frequently used in farming and logistics, civil and military alike, researches involving UAVs also starts to boom. In the civil field, UAV is generally flown in urban areas, so buildings are the main factors hindering the normal flight of UAV. Therefore, it is necessary to calculate an optimal flight path of UAV under constraints. The intelligent logistics UAV proposed in this paper can be used to replace special couriers to deliver small goods. It is a quadcopter integrated with a webcam, ultrasound Ground Proximity Warning System (GPWS), and is controllable through a mobile APP. Gradient descent algorithm, Linear Quadratic Regulator (LQR) and improved Proportion–Integral– Derivative (PID) controllers are applied in its flight control system, MATLAB and wavelet transform to handle fuzzy image. In addition, ant colony algorithm and adaptive strategies are used in the path planning process. As a result, it can detect surrounding obstacles in-flight, and the ground control station can receive feedback information and prepare for emergency operations.

Multi-Agent Path Finding in Unmanned Aircraft System Traffic Management With Scheduling and Speed Variation

The development of an unmanned aircraft system (UAS) traffic management (UTM) system for the safe integration of unmanned aerial vehicles (UAVs) requires pre-flight conflict detection and resolution (CDR) methods to provide collision-free flight paths for all UAVs before takeoff. A popular solution consists in adapting multi-agent path finding (MAPF) techniques. However, standard MAPF solvers consider only a fixed takeoff time and fixed uniform speed for each UAV flight path, which can lead to inefficiencies in the resolution of instances. Therefore, in this article, we propose incorporating scheduling elements into MAPF solvers, which allows us to adjust the takeoff times and speeds of each UAV to solve conflicts. We introduce two time-related resolution techniques: 1) takeoff scheduling, whereby the start time of a UAV agent is delayed, and 2) speed adjustment, wherein the speed of a UAV agent is decreased over a segment on its flight path. Importantly, we present a distinction of conflict types, which enables us to combine replanning resolution to the aforementioned temporal resolution techniques. We evaluate our proposed approaches on a realistic, high-density UAV delivery scenario in Tokyo, Japan. We show that the combination of takeoff scheduling, replanning, and speed-adjustment resolution techniques improves the efficiency of route planning by reducing the average delay per flight path and the number of rejected flight paths.

Unmanned aerial vehicle transport of frozen blood samples using phase change materials

Appropriate cryopreservation of blood samples during transit from the point of collection to testing sites is an important element of quality sample management in clinical and veterinary diagnostics and research. Here, a heat-insulated design that employs 23.3% w/w salt water as the phase change material (PCM) for encapsulating a 1.5-mL vial of frozen blood sample was studied to assess its efficacy in maintaining sub-zero temperatures during transport. A two-dimensional finite element numerical simulation model with 5457 elements was found to exhibit mesh independence with 10% convergence error. For the PCM pre-cooled in dry ice and vial sample kept initially at 20 °C before flight, this model is predicted to maintain a stable transport temperature of −20 °C for at least 70 min. It also predicted to have uniform temperature distribution across the PCM holder, PCM, sample vial and sample. Experimental data obtained using a thermocouple confirmed the accuracy of the simulation model. Sensor data collected from a bespoke unmanned aerial vehicle (UAV) allowed mapping of the acceleration forces during flight and facilitated the development of a mechanical setup to experimentally show that frozen samples are protected from shear forces generated under mimicked transport conditions. The capability of real-time sample temperature monitoring with feedback control on UAV flight path demonstrates the viability of the drone delivery system for quality transport of thermal-sensitive samples. • UAV transport of frozen blood samples done here with salt water phase change material. • 2D finite element model achieved mesh independence with 10% convergence error. • 70 min stable transfer at −20 °C attained after 30 min of dry ice pre-cooling. • Sensor data from UAV mimicked the flight conditions in a sloshing test setup. • Preliminary tests with frozen sheep's blood show lower coagulation from sloshing.

An intelligent UAV path planning optimization method for monitoring the risk of unattended offshore oil platforms

To ensure the safe operation of static and dynamic devices in a limited platform space and prevent security threats caused by accidents and external intruders, the unmanned aerial vehicle (UAV) is becoming an important tool to monitor and reduce the operational risk of unattended offshore oil platforms. However, UAVs may encounter location errors, adversely affecting patrol efficiency and accuracy. Therefore, the optimal UAV path planning is vital to ensure complete monitoring routes and reduce the risk presented by the marine environment, devices, and employees on unattended offshore platforms. This paper established a multi-objective mathematical model with the shortest flight path and minimum correction times for the intelligent UAV patrol of unattended offshore platforms. An intelligent algorithm with large-scale constraints for UAV path planning was proposed based on non-dominated sorting genetic algorithms. A flight path length of 8076.11 m and 25 correction times presented the optimal solution. The results indicated that the proposed algorithm could be used to plan an effective and accurate three-dimensional (3D) UAV flight path according to the size of the offshore oil platform. This is highly significant for the intelligent risk monitoring of unattended offshore platforms in practical engineering in the future.

Joint deployment and trajectory optimization in UAV-assisted vehicular edge computing networks

As the general mobile edge computing (MEC) scheme cannot adequately handle the emergency communication requirements in vehicular networks, unmanned aerial vehicle (UAV)-assisted vehicular edge computing networks (VECNs) are envisioned as the reliable and cost-efficient paradigm for the mobility and flexibility of UAVs. UAVs can perform as the temporary base stations to provide edge services for road vehicles with heavy traffic. However, it takes a long time and huge energy consumption for the UAV to fly from the stay charging station to the mission areas disorderly. In this paper, we design a pre-dispatch UAV-assisted VECNs system to cope with the demand of vehicles in multiple traffic jams. We propose an optimal UAV flight trajectory algorithm based on the traffic situation awareness. The cloud computing center (CCC) server predicts the real-time traffic conditions, and assigns UAVs to different mission areas periodically. Then, a flight trajectory optimization problem is formulated to minimize the cost of UAVs, while both the UAV flying and turning energy costs are mainly considered. In addition, we propose a deep reinforcement learning(DRL)-based energy efficiency autonomous deployment strategy, to obtain the optimal hovering position of UAV at each assigned mission area. Simulation results demonstrate that our proposed method can obtain an optimal flight path and deployment of UAV with lower energy consumption.

Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

Solar energy is one of the most strategic energy sources for the world’s economic development. This has caused the number of solar photovoltaic plants to increase around the world; consequently, they are installed in places where their access and manual inspection are arduous and risky tasks. Recently, the inspection of photovoltaic plants has been conducted with the use of unmanned aerial vehicles (UAV). Although the inspection with UAVs can be completed with a drone operator, where the UAV flight path is purely manual or utilizes a previously generated flight path through a ground control station (GCS). However, the path generated in the GCS has many restrictions that the operator must supply. Due to these restrictions, we present a novel way to develop a flight path automatically with coverage path planning (CPP) methods. Using a DL server to segment the region of interest (RoI) within each of the predefined PV plant images, three CPP methods were also considered and their performances were assessed with metrics. The UAV energy consumption performance in each of the CPP methods was assessed using two different UAVs and standard metrics. Six experiments were performed by varying the CPP width, and the consumption metrics were recorded in each experiment. According to the results, the most effective and efficient methods are the exact cellular decomposition boustrophedon and grid-based wavefront coverage, depending on the CPP width and the area of the PV plant. Finally, a relationship was established between the size of the photovoltaic plant area and the best UAV to perform the inspection with the appropriate CPP width. This could be an important result for low-cost inspection with UAVs, without high-resolution cameras on the UAV board, and in small plants.