UAV Flight Research Articles

Quadrotor’s modeling and control system design based on PID control

The quadrotor is an unmanned aerial vehicle with a compact construction and good maneuverability. In recent years, this type of aircraft has been widely developed and has a wide range of military and civil applications. Due to the dynamic characteristics of strongly coupled, non-linear, and multi-variable quadrotor UAVs, modeling the dynamics of UAVs and designing control systems has been the focus of quadrotor research. This paper takes the quadrotor UAV as the research object. Firstly, for the basic structure of the quadrotor UAV, the aircraft-body coordinate system and the earth-surface coordinate system of the UAV flight are established, and the transformation matrix of the two coordinate systems is given. Based on a series of reasonable assumptions, the non-linear system is modeled and a simulation platform is built using Simulink. Secondly, based on the constructed non-linear dynamics equations of the system, a cascade PID-based attitude controller and position controller of the UAV are designed. Finally, the designed control system was simulated in Matlab for straight-line flight and target-position flight, and obstacles were applied to the vehicle to verify the effectiveness of the control system. The final results show that the designed control system can make the quadrotor UAV stable in vertical and horizontal motion and can successfully travel to the target position. This paper provides a control method and framework for a PID controller-based quadrotor UAV control system.

Dynamic Soaring Parameters Influence Regularity Analysis on UAV and Soaring Strategy Design

Dynamic soaring helps albatross achieve long-distance migration. From a bionic view, dynamic soaring has great potential to enhance unmanned aerial vehicles (“UAVs”) flight range and endurance. The previous application studies focus on flight strategies to guide UAV soaring. However, the energy harvesting efficiency problem emerges. The lack of clear dynamic soaring influencing factors hinders dynamic soaring UAV design and flight strategy design from a theoretical perspective. Hence this paper aims to analyze the influence law of different UAV mass, initial airspeed, and entering angle. Trajectories and flight data in different factors are obtained through trajectory optimization. The results show that UAV mass has a positive influence on energy harvesting. The initial airspeed and entering angle affect both energy efficiency and trajectory. For UAV design, weight balance needs to be considered rather than a pursuit of the lightest. For flight strategy design, finding an optimal initial state will improve energy efficiency.

Fresh Data Collection for UAV-Assisted IoT Based on Aerial Collaborative Relay

Low-latency data delivery in sparse deployment scenarios is a challenging problem. The emergence of UAV-assisted IoTs provides a viable path to solve this problem. In the existing UAV-assisted Internet of Things system, UAVs usually complete data collection through task collaboration, but there is no data relay transmission between UAVs. The main bottleneck of this mode is that the UAV responsible for the edge region of the ground sensor network needs to fly a long distance to send data to the base station, which will seriously affect the age of information (AoI) performance of the system. Therefore, in this work, we design a UAV-assisted IoT data collection mechanism based on aerial collaborative relay and propose an AoI-sensitive data collection scheme. In this scheme, we design a general AoI expectation (GAE) function to achieve iterative optimization of UAV flight paths. Extensive experimental results have shown that the proposed data collection scheme requires less deployment preparation time while obtaining better AoI performance than existing data collection schemes.

Modeling analysis of UAV communication cyclostationary signal based on virtual reality

In order to reduce the redundancy of the analysis process of UAV communication cyclostationary signal, a modeling and analysis method of UAV communication cyclostationary signal based on virtual reality technology is proposed. Using the VR technology, the 3D scene of UAV flight is reconstructed, and the mutual information and correlation dimension are obtained. Using the maximum negative entropy FastICA algorithm and non-cyclic stationary signal processing algorithm, the anti-jamming model of cyclic stationary signal processing is established by analyzing the signal compression performance and time delay of pseudo-random sequence. The experimental results show that there is no obvious signal fluctuation in the time domain of the oscillation signal and the reconstruction results of the oscillation signal, and the amplitude fluctuation is not large.

Path tracking control of solar-powered UAV based on energy management strategy

To coordinate the conflict between the high-altitude task and long-term energy efficiency of high-altitude long endurance solar UAV, it is one of the core issues of UAV control. A strategy combining the energy management with the route tracking is presented. Firstly, based on the three-dimensional particle motion model, energy storage battery model and solar energy acquisition model for the solar UAV, according to the aerodynamic parameters of the solar UAV and the typical horizontal flight, climb, descent and other motion processes of the solar UAV, the energy management strategy of the solar UAV is designed, and the allocation mechanism and optimal flight parameters of the energy acquisition, storage and consumption of the solar UAV in different flight stages are determined. The surplus solar energy is stored by using the gravitational potential energy, and then the energy management based on height adjustment is carried out to realize the longitudinal tracking control of the solar UAV under different lighting and energy conditions. Then based on the task requirements of the lateral movement of the solar UAV, a track tracking control method based on the feedback linearization method is established by decoupling the particle dynamics equation of the solar UAV, and the track tracking control of the solar UAV in the lateral direction is realized. Finally, a simulation throughout 24 hours is implemented and illustrated the effectiveness of the energy management strategy and route tracking control law.

Multi-Conflict-Based Optimal Algorithm for Multi-UAV Cooperative Path Planning

Multi-UAV cooperative path planning can improve the efficiency of task completion. To deal with the space and time conflicts of multi-UAVs in complex environments, a multi-collision-based multi-UAV cooperative path planning algorithm, multi-conflict-based search (MCBS), is proposed. First, the flight and cooperative constraints of UAV are analyzed, and a three-dimensional environment model is established that incorporates geographical information. Then, hierarchical optimization is used to design collaborative algorithms. In the low-level path design, UAV flight constraints are combined with a sparse A* algorithm, and by improving the cost function, the search space is reduced, and the search time is shortened. In high-level cooperation, the priorities of different conflicts are set, heuristic information is introduced to guide the constraint tree to grow in the direction of satisfying the constraints, and the optimal path set is searched by the best priority search algorithm to reduce the convergence time. Finally, the planning results of the proposed algorithm, the traditional CBS algorithm, and the sparse A* algorithm for different UAV tasks are compared, and the influence of the optimization parameters on the calculation results is discussed. The simulation results show that the proposed algorithm can solve cooperative conflict between UAVs, improve the efficiency of path searches, and quickly find the optimal safe cooperative path that satisfies flight and cooperative constraints.

Flying high: Sampling savanna vegetation with UAV‐lidar

Abstract The flexibility of UAV‐lidar remote sensing offers a myriad of new opportunities for savanna ecology, enabling researchers to measure vegetation structure at a variety of temporal and spatial scales. However, this flexibility also increases the number of customizable variables, such as flight altitude, pattern, and sensor parameters, that, when adjusted, can impact data quality as well as the applicability of a dataset to a specific research interest. To better understand the impacts that UAV flight patterns and sensor parameters have on vegetation metrics, we compared 7 lidar point clouds collected with a Riegl VUX − 1LR over a 300 × 300 m area in the Kruger National Park, South Africa. We varied the altitude (60 m above ground, 100 m, 180 m, and 300 m) and sampling pattern (slowing the flight speed, increasing the overlap between flightlines and flying a crosshatch pattern), and compared a variety of vertical vegetation metrics related to height and fractional cover. Comparing vegetation metrics from acquisitions with different flight patterns and sensor parameters, we found that both flight altitude and pattern had significant impacts on derived structure metrics, with variation in altitude causing the largest impacts. Flying higher resulted in lower point cloud heights, leading to a consistent downward trend in percentile height metrics and fractional cover. The magnitude and direction of these trends also varied depending on the vegetation type sampled (trees, shrubs or grasses), showing that the structure and composition of savanna vegetation can interact with the lidar signal and alter derived metrics. While there were statistically significant differences in metrics among acquisitions, the average differences were often on the order of a few centimetres or less, which shows great promise for future comparison studies. We discuss how these results apply in practice, explaining the potential trade‐offs of flying at higher altitudes and with alternate patterns. We highlight how flight and sensor parameters can be geared toward specific ecological applications and vegetation types, and we explore future opportunities for optimizing UAV‐lidar sampling designs in savannas.

A Self-Supervised Fault Detection for UAV Based on Unbalanced Flight Data Representation Learning and Wavelet Analysis

This paper aims to build a Self-supervised Fault Detection Model for UAVs combined with an Auto-Encoder. With the development of data science, it is imperative to detect UAV faults and improve their safety. Many factors affect the fault of a UAV, such as the voltage of the generator, angle of attack, and position of the rudder surface. A UAV is a typical complex system, and its flight data are typical high-dimensional large sample data sets. In practical applications such as UAV fault detection, the fault data only appear in a small part of the data sets. In this study, representation learning is used to extract the normal features of the flight data and reduce the dimensions of the data. The normal data are used for the training of the Auto-Encoder, and the reconstruction loss is used as the criterion for fault detection. An Improved Auto-Encoder suitable for UAV Flight Data Sets is proposed in this paper. In the Auto-Encoder, we use wavelet analysis to extract the low-frequency signals with different frequencies from the flight data. The Auto-Encoder is used for the feature extraction and reconstruction of the low-frequency signals with different frequencies. To improve the effectiveness of the fault localization at inference, we develop a new fault factor location model, which is based on the reconstruction loss of the Auto-Encoder and edge detection operator. The UAV Flight Data Sets are used for hard-landing detection, and an average accuracy of 91.01% is obtained. Compared with other models, the results suggest that the developed Self-supervised Fault Detection Model for UAVs has better accuracy. Concluding this study, an explanation is provided concerning the proposed model’s good results.

Study of UAVs in attempted formation flight based on pure azimuth passive positioning

As a great crystallization of human technology in the past hundred years, many western countries have applied high technology to the research and development of UAVs since the 1990s. In this paper, the pure azimuth passive positioning problem in attempted formation flight of UAVs is explored through the intersection of different straight lines to determine the position of the target aircraft, and the location information of the predicted and accurate points is used to optimize the target analysis. And the conclusion is drawn in minimizing the emission signal scenario of electromagnetic waves. Then, according to the knowledge of graph theory, the UAV array is fitted to a graph, a coordinate system is established with the position of one of the UAVs, and a database with standard positions is created for comparison.

Design and Flight Testing of the Ducted-fan UAV Flight Array System

Design and Flight Testing of the Ducted-fan UAV Flight Array System

Pure Azimuth Passive Positioning Model for UAV Formations

In recent years, with the rapid development of the UAV industry, the traditional positioning equipment has been unable to carry out effective control processing. This paper mainly studies the pure azimuth passive positioning in the attempted formation flight of UAV. On the basis of in-depth analysis of the signals received by the passive receiving signal UAV, assuming that the deviation of the UAV position at the receiving point is under the controllable error range, analyzing the relationship between the known number and the size of the circular angle, obtaining the passive receiving point positioning model, and checking the results by the least squares method, the results show that the model sought in this paper The results show that the model is of good accuracy. This paper is of great importance for the position positioning and adjustment of UAV cluster flight by pure azimuthal passive positioning method.

UNMANNED AERIAL VEHICLE PHOTOGRAMMETRIC PRODUCTS ACCURACY ASSESSMENT: A REVIEW

Abstract. Digital photogrammetry is an effective way for gathering data for DEM extraction, and recent advances in recording techniques and data processing have allowed for higher resolution and faster rapid generation of photogrammetric 3D models result. The model has a spatial and spectral high-resolution advantage with good geometrical positioning accuracy. The DEM quality is the primary requirement for any application and must satisfy users’ requirement. The DEM quality is usually affected by several factors during acquisition and processing stages. Considerable researches have been conducted on several parameters influencing the DEM accuracy. The review focused on discussions on topics related to unmanned aerial vehicle DEM accuracy assessment. Five parameters were considered: UAV technology; UAV Georeferencing; UAV and computer vision; UAV and LiDAR; and UAV flight parameters. Summary of the methods, their strength, weakness and regions of the most recent articles are presented. Based on this review conclusion was drawn on the UAV DEM accuracy challenging issues that need more attention from the geospatial community and suggestions for future work are offered. But there might be other possible factors that are not treated in this paper.

Research on Algorithm of Airborne Dual-Antenna GNSS/MINS Integrated Navigation System.

In view of the difficulties regarding that airborne navigation equipment relies on imports and the expensive domestic high-precision navigation equipment in the manufacturing field of Chinese navigable aircraft, a dual-antenna GNSS (global navigation satellite system)/MINS (micro-inertial navigation system) integrated navigation system was developed to implement high-precision and high-reliability airborne integrated navigation equipment. First, the state equation and measurement equation of the system were established based on the classical discrete Kalman filter principle. Second, according to the characteristics of the MEMS (micro-electric-mechanical system), the IMU (inertial measurement unit) is not sensitive to Earth rotation to realize self-alignment; the magnetometer, accelerometer and dual-antenna GNSS are utilized for reliable attitude initial alignment. Finally, flight status identification was implemented by the different satellite data, accelerometer and gyroscope parameters of the aircraft in different states. The test results shown that the RMS (root mean square) of the pitch angle and roll angle error of the testing system are less than 0.05° and the heading angle error RMS is less than 0.15° under the indoor static condition. A UAV flight test was carried out to test the navigation effect of the equipment upon aircraft take-off, climbing, turning, cruising and other states, and to verify the effectiveness of the system algorithm.

Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication.

Communication infrastructure is damaged by disasters and it is difficult to support communication services in affected areas. UAVs play an important role in the emergency communication system. Due to the limited airborne energy of a UAV, it is a critical technical issue to effectively design flight routes to complete rescue missions. We fully consider the distribution of the rescue area, the type of mission, and the flight characteristics of the UAV. Firstly, according to the distribution of the crowd, the PSO algorithm is used to cluster the target-POI of the task area, and the neural collaborative filtering algorithm is used to prioritize the target-POI. Then we also design a Trans-UTPA algorithm. Based on MAPPO 's policy network and value function, we introduce transformer model to make Trans-UTPA's policy learning have no action space limitation and can be multi-task parallel, which improves the efficiency and generalization of sample processing. In a three-dimensional space, the UAV selects the emergency task to be performed (data acquisition and networking communication) based on strategic learning of state information (location information, energy consumption information, etc.) and action information (horizontal flight, ascent, and descent), and then designs the UAV flight path based on the maximization of the global value function. The experimental results show that the performance of the Trans-UTPA algorithm is further improved compared with the USCTP algorithm in terms of the success rate of each UAV reaching the target position, the number of collisions, and the average reward of the algorithm. Among them, the average reward of the algorithm exceeds the USCTP algorithm by 13%, and the number of collisions is reduced by 60%. Compared with the heuristic algorithm, it can cover more target-POIs, and has less energy consumption than the heuristic algorithm.

UAV Formation Trajectory Planning Algorithms: A Review

With the continuous development of UAV technology and swarm intelligence technology, the UAV formation cooperative mission has attracted wide attention because of its remarkable function and flexibility to complete complex and changeable tasks, such as search and rescue, resource exploration, reconnaissance and surveillance. The collaborative trajectory planning of UAV formation is a key part of the task execution. This paper attempts to provide a comprehensive review of UAV formation trajectory planning algorithms. Firstly, from the perspective of global planning and local planning, a simple framework of the UAV formation trajectory planning algorithm is proposed, which is the basis of comprehensive classification of different types of algorithms. According to the proposed framework, a classification method of existing UAV formation trajectory planning algorithms is proposed, and then, different types of algorithms are described and analyzed statistically. Finally, the challenges and future research directions of the UAV formation trajectory planning algorithm are summarized and prospected according to the actual requirements. It provides reference information for researchers and workers engaged in the formation flight of UAVs.

Tracking Control With Zero Steady State for UAV Flight Platform

Tracking Control With Zero Steady State for UAV Flight Platform

UAV Flight Height Impacts on Wheat Biomass Estimation via Machine and Deep Learning

UAV Flight Height Impacts on Wheat Biomass Estimation via Machine and Deep Learning

A Survey of Indoor UAV Obstacle Avoidance Research

UAV obstacle avoidance technology is one of the key factors to realize UAV autonomous flight, efficient and accurate obstacle avoidance is significant to complete the UAV autonomous flight task. In contrast, the dynamic, real-time and uncertainty of the environment in which the UAV is located makes the problem very tricky, especially in the indoor environment. At present, a large number of scholars have shown strong interest in the indoor UAV obstacle avoidance problem. With the rapid development of computer technology and hardware devices, many intelligent algorithms have been proposed to solve the obstacle avoidance problem. However, the research on indoor UAV obstacle avoidance technology is not comprehensive enough, and there is a lack of summarization of the research results in recent years. This paper introduces the sensor modules commonly used for indoor UAV environment sensing, related obstacle avoidance methods based on sensory detection. Classifies and composes the commonly used UAV path planning obstacle avoidance algorithms, and gives several representative UAV flight control research methods. This paper summarizes the advantages and disadvantages of different perception modules and detection methods applied to UAV obstacle avoidance tasks, and compares various current path planning methods. Finally, the critical difficulties and challenges faced in the field of indoor UAV obstacle avoidance are discussed, and future research in the field of UAV obstacle avoidance has prospected.

Energy-Efficient UAV Trajectory Planning based on Flexible Segment Clustering Algorithm

This paper plans the energy-efficient UAV trajectory when a UAV gathers data from massive IoT devices in a given area. The UAV trajectory design is addressed by two steps, i.e., IoT node clustering and UAV flight path planning for scanning the clusters, which are formulated as Cluster Minimization (CM) problem and Traveling Salesman Problem (TSP) in this work, respectively. The CM aims to contribute fewest clusters with minimal overlap to cover all the IoT devices and the per cluster size approaching the UAV communication coverage. On the other hand, the TSP seeks to design the shortest flight path to cover all the grouped clusters while minimizing energy consumption. Specifically, this work mainly focuses on the CM problem since the TSP issues have been well addressed in the past. In particular, we design a two-stage ILP optimization model to formulate the CM problem and propose two flexible clustering algorithms with low complexity, i.e., segment clustering (SC) and its variant, saying shifted SC (SSC). For the proposed ILP model and algorithms, we conduct extensive simulations under five different topologies and compare the performance results with existing methods. The simulation results indicate that the performance achieved by the proposed SSC algorithm is closest to the optimal results obtained from the ILP model. Moreover, it outperforms the existing methods under most topologies regarding cluster numbers, trajectory path length, and power consumption.

UAV Flight Path Planning Based on Multi-Strategy Improved White Sharks Optimization

UAV Flight Path Planning Based on Multi-Strategy Improved White Sharks Optimization