Unmanned Aerial Vehicles Formation Research Articles

Trajectory-aware spatio-temporal range query processing for unmanned aerial vehicle networks

Unmanned aerial vehicle (UAV) network is widely used in environmental monitoring, target searching and rescuing, logistics, and other fields due to its characteristics of large-scale coverage, rapid deployment and strong resistance to destruction. When users are interested in sensory data in certain areas covered by UAV networks, they can send a spatio-temporal range query with time and geography constraints through the ground station. For example, obtaining the temperature information around fire points in the forest within an hour before the fire bursts out. Then, UAVs that carry the query results will return the data to the ground station through multi-hop routing. However, most of the existing spatio-temporal range query algorithms are designed for static networks. How to conduct spatio-temporal range queries in the UAV networks is still an open problem. In this paper, we propose a Trajectory-Aware Spatio-Temporal range query processing algorithm (TAST) for UAV networks. The ground station takes advantage of the pre-planned trajectory information of UAVs to build the topology change model of the UAV network, which can reflect the changes of UAVs’ communication links and neighbors. Furthermore, the static topology change graph (TCG) is proposed for optimizing the routing of query results in the spatio-temporal query processing. Next, we propose a Trajectory-Aware Spatio-Temporal range query processing algorithm based on packet Splitting (TASTS), which is used to split large query results into multiple small packets called unit packets, and each unit packet is transmitted back to the ground station independently and efficiently. Finally, we evaluated our algorithms through simulation experiments. The simulation results show that TAST and TASTS perform well in terms of query success rate, query efficiency and overhead ratio.

A New Clustering Algorithm Toward Building Segmentation From Aerial Images by Utilizing RGB‐Component Differences

AbstractFor the image segmentation task, deep features‐based deep learning methods have strict data requirements on the quality and quantity, which limits their application in irregular scenes, such as the villages subjected to geological hazards in the complex mountains. However, traditional algorithms with shallow and middle features are unable to utilize the complex information of unmanned aerial vehicle (UAV) remote sensing images effectively. To address this issue, we proposed a new image segmentation algorithm by using the RGB component difference clustering (RGBCDC). In this method, after dimensionality reduction of RGB color space from three dimensions to two dimensions, the distance between similar objects would be diminished and the distance between disturbance areas would be enlarged. Two different UAV data sets (3,000 in total) have been used to compare the proposed algorithm with the traditional segmentation algorithms and the deep learning‐based methods. Results show that the average pixel accuracy is 13.11%, 11.86%, 13.84%, 8.00%, and 9.86% higher than Octree Quantization clustering algorithm, Region Growing algorithm, Watershed algorithm, K‐Means algorithm, and SLIC superpixel segmentation algorithm, respectively. When facing insufficient samples, the new method performed better than deep learning algorithms, such as Deeplabv3, PSPnet, UNET, and UISBB. In general, the proposed algorithm for building segmentation shows a better potential for use in uncommon situations, especially for rapid emergency rescue after serious mountain hazards.

Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture

Farming is the major profession in several republics for centuries. However, due to the immigration of individuals from rural to urban, there is prevention in farming. The use of modern technology in the precision agriculture field increases productivity and also improves the exports of a country. The productivity may suffer due to different environmental factors, diseases and insects attacks on the crops, especially tomatoes. The target area (i.e. the affected crops area due to environmental factors) identification and delivery of timely information about diseases in the crops to the ground station are mandatory to make the precautionary measurements. In flying sensor networks, the localization and clustering of multiple unmanned aerial vehicles for target areas identification is a challenging task due to energy constraints, communication range, frequent change in topology, link expiration and high mobility. In this article, we proposed the localization and clustering of multiple unmanned aerial vehicles for the identification of affected target areas in the tomato crop field. The localization of unmanned aerial vehicles depends on the weights of environmental factors, that is, relative humidity, soil moisture, temperature, light intensity, NPK (nitrogen (n), phosphorus (p) and potassium (k)) and power of hydrogen (pH). A honey bee optimization approach is used for the localization and formation of multiple unmanned aerial vehicles’ cluster to accurately identify the target areas. The performance of our bio-inspired approach is compared in terms of communication overhead, packet delivery ratio, mean end-to-end delay and energy consumption with the existing swarm intelligence–based schemes and validated via a simulation. The simulation result shows that the bio-inspired approach performs better among the selected approaches.

Virtual Electric Dipole Field Applied to Autonomous Formation Flight Control of Unmanned Aerial Vehicles.

The following paper presents a method for the use of a virtual electric dipole potential field to control a leader-follower formation of autonomous Unmanned Aerial Vehicles (UAVs). The proposed control algorithm uses a virtual electric dipole potential field to determine the desired heading for a UAV follower. This method’s greatest advantage is the ability to rapidly change the potential field function depending on the position of the independent leader. Another advantage is that it ensures formation flight safety regardless of the positions of the initial leader or follower. Moreover, it is also possible to generate additional potential fields which guarantee obstacle and vehicle collision avoidance. The considered control system can easily be adapted to vehicles with different dynamics without the need to retune heading control channel gains and parameters. The paper closely describes and presents in detail the synthesis of the control algorithm based on vector fields obtained using scalar virtual electric dipole potential fields. The proposed control system was tested and its operation was verified through simulations. Generated potential fields as well as leader-follower flight parameters have been presented and thoroughly discussed within the paper. The obtained research results validate the effectiveness of this formation flight control method as well as prove that the described algorithm improves flight formation organization and helps ensure collision-free conditions.

Model and simulations of multipath bridge routing for inter-swarm UAV communications in EMANE/CORE

ABSTRACT The unmanned aerial vehicle (UAV) network is a special type of mobile ad hoc network (MANET). 1 There are two types of UAV swarm communications: Communications within a swarm (i.e. intra-swarm) and communication between different swarms (i.e. inter-swarm). This paper proposes the concept of bridge routing to establish the route(s) for efficient inter-swarm communications. The bridge routing scheme is a progressive process with two functions: UAV formation control and data transmissions. Based on the bridge routing topology, we have implemented the multipath user datagram protocol (MP-UDP), as a sub-layer between the transport and application layers. Compared with the multipath transmission control protocol (MP-TCP), MP-UDP is more suitable to the dynamic network topologies, since it does not use the high-overhead sliding window control. We have used the emulator tools, the Extendable Mobile Ad -hoc Network Emulator (EMANE) and Common Open Research Emulator (CORE) to implement and study the QoS performance of MP-UDP and bridge routing in UAV swarm networks. Our results demonstrate the improved throughput performance for inter-swarm communications. Approved for Public Release; distribution unlimited 88ABW-2020-2122 on 13 July 2020. Other requests shall be referred to AFRL/RIT 525 Brooks Rd Rome, NY 13,441

Movement of a group of unmanned aerial vehicles in formation

This paper presents a method for organising the movement of unmanned aerial vehicles in formation. In the presented method of UAV collisions are prevented by a modification of the potential field method. The movement of the group is accomplished by incrementing the origin coordinates of the formation at each iteration of data exchange between UAVs. This paper presents a graph showing the negligible positioning error of drones in formation over time as the heterogeneous swarm of quadcopters moves towards the target position. Simulation results show that the flight time of a group of UAVs to the target swarm position using the proposed method is longer than using the master-slave method. The model of a Wi-Fi network as an information exchange network between UAVs was used in the developed simulation environment. This approach facilitates the implementation of the presented method on real aircrafts.

On Maneuverability of Fixed-Wing Unmanned Aerial Vehicle Formations

The problem of determining feasible maneuver envelope in multi-unmanned aerial vehicle (multi-UAV) formations is considered. In the leader–follower framework, an analysis is carried out relating the three fundamental physical constraints of follower UAVs, namely, bank angle, stalling speed, and maximum speed, to the leader’s maneuver. The closed-form relationships are further analyzed in detail to determine the set of follower UAVs that characterize the envelope boundary and their dependency on the maneuver. The maneuver envelope for a general multiple UAV formation is expressed in the turn radius-speed space of the leader, and validation studies are considered for various formation sizes and geometries. As a first, the work presents a complete and analytic solution to the problem considering arbitrary formation configuration and accounting for all the fundamental fixed-wing UAV physical constraints.

Multi-unmanned aerial vehicle swarm formation control using hybrid strategy

This study proposes a novel hybrid strategy for formation control of a swarm of multiple unmanned aerial vehicles (UAVs). To enhance the fitness function of the formation, this research offers a three-dimensional formation control for a swarm using particle swarm optimization (PSO) with Cauchy mutant (CM) operators. We use CM operators to enhance the PSO algorithm by examining the varying fitness levels of the local and global optimal solutions for UAV formation control. We establish the terrain and the fixed-wing UAV model. Furthermore, it also models different control parameters of the UAV as well. The enhanced hybrid algorithm not only quickens the convergence rate but also improves the solution optimality. Lastly, we carry out the simulations for the multi-UAV swarm under terrain and radar threats and the simulation results prove that the hybrid method is effective and gives better fitness function.

A Simulated Annealing Algorithm and Grid Map-Based UAV Coverage Path Planning Method for 3D Reconstruction

With the extensive application of 3D maps, acquiring high-quality images with unmanned aerial vehicles (UAVs) for precise 3D reconstruction has become a prominent topic of study. In this research, we proposed a coverage path planning method for UAVs to achieve full coverage of a target area and to collect high-resolution images while considering the overlap ratio of the collected images and energy consumption of clustered UAVs. The overlap ratio of the collected image set is guaranteed through a map decomposition method, which can ensure that the reconstruction results will not get affected by model breaking. In consideration of the small battery capacity of common commercial quadrotor UAVs, ray-scan-based area division was adopted to segment the target area, and near-optimized paths in subareas were calculated by a simulated annealing algorithm to find near-optimized paths, which can achieve balanced task assignment for UAV formations and minimum energy consumption for each UAV. The proposed system was validated through a site experiment and achieved a reduction in path length of approximately 12.6% compared to the traditional zigzag path.

Adaptive Robust Trajectory Tracking Control of Multiple Quad-Rotor UAVs with Parametric Uncertainties and Disturbances.

Recently, formation flying of multiple unmanned aerial vehicles (UAVs) found numerous applications in various areas such as surveillance, industrial automation and disaster management. The accuracy and reliability for performing group tasks by multiple UAVs is highly dependent on the applied control strategy. The formation and trajectories of multiple UAVs are governed by two separate controllers, namely formation and trajectory tracking controllers respectively. In presence of environmental effects, disturbances due to wind and parametric uncertainties, the controller design process is a challenging task. This article proposes a robust adaptive formation and trajectory tacking control of multiple quad-rotor UAVs using super twisting sliding mode control method. In the proposed design, Lyapunov function-based adaptive disturbance estimators are used to compensate for the effects of external disturbances and parametric uncertainties. The stability of the proposed controllers is guaranteed using Lyapunov theorems. Two variants of the control schemes, namely fixed gain super twisting SMC (STSMC) and adaptive super twisting SMC (ASTSMC) are tested using numerical simulations performed in MATLAB/Simulink. From the results presented, it is verified that in presence of disturbances, the proposed ASTSMC controller exhibits enhanced robustness as compared to the fixed gain STSMC.

Typical Fault Estimation and Dynamic Analysis of a Leader-Follower Unmanned Aerial Vehicle Formation

In this paper, the typical fault estimation and dynamic analysis are presented for a leader-follower unmanned aerial vehicle (UAV) formation system with external disturbances. Firstly, a dynamic model with proportional navigation guidance (PNG) control of the UAV formation is built. Then, an intermediate observer design method is adopted to estimate the system states and faults simultaneously. Based on the graph theory, the topology relationship between each node in the UAV formation has been also analyzed. The estimator and the system error have been created. Moreover, the typical faults, including the components failure, airframe damage, communication failure, formation collision, and environmental impact, are also discussed for the UAV system. Based on the fault-tolerant strategy, five familiar fault models are proposed from the perspectives of fault estimation, dynamical disturbances, and formation cooperative control. With an analysis of the results of states and faults estimation, the actuator faults can be estimated precisely with component failure and wind disturbances. Furthermore, the basic dynamic characteristics of the UAV formation are discussed. Besides, a comparison of two cases related to the wind disturbance has been accomplished to verify the performance of the fault estimator and controller. The results illustrate the credibility and applicability of the fault estimation and dynamic control strategies for the UAV system which are proposed in this paper. Finally, an extension about the UAV formation prognostic health management system is expounded from the point of view of the fault-tolerant control, dynamic modeling, and multifault estimation.

UAV Formation Shape Control via Decentralized Markov Decision Processes

In this paper, we present a decentralized unmanned aerial vehicle (UAV) swarm formation control approach based on a decision theoretic approach. Specifically, we pose the UAV swarm motion control problem as a decentralized Markov decision process (Dec-MDP). Here, the goal is to drive the UAV swarm from an initial geographical region to another geographical region where the swarm must form a three-dimensional shape (e.g., surface of a sphere). As most decision-theoretic formulations suffer from the curse of dimensionality, we adapt an existing fast approximate dynamic programming method called nominal belief-state optimization (NBO) to approximately solve the formation control problem. We perform numerical studies in MATLAB to validate the performance of the above control algorithms.

Adaptive distributed formation maintenance for multiple UAVs: Exploiting proximity behavior observations

The formation maintenance of multiple unmanned aerial vehicles (UAVs) based on proximity behavior is explored in this study. Individual decision-making is conducted according to the expected UAV formation structure and the position, velocity, and attitude information of other UAVs in the azimuth area. This resolves problems wherein nodes are necessarily strongly connected and communication is strictly consistent under the traditional distributed formation control method. An adaptive distributed formation flight strategy is established for multiple UAVs by exploiting proximity behavior observations, which remedies the poor flexibility in distributed formation. This technique ensures consistent position and attitude among UAVs. In the proposed method, the azimuth area relative to the UAV itself is established to capture the state information of proximal UAVs. The dependency degree factor is introduced to state update equation based on proximity behavior. Finally, the formation position, speed, and attitude errors are used to form an adaptive dynamic adjustment strategy. Simulations are conducted to demonstrate the effectiveness and robustness of the theoretical results, thus validating the effectiveness of the proposed method.

Hybrid Dec-POMDP/PID Guidance System for Formation Flight of Multiple UAVs

This paper proposes a guidance system with a new hybrid architecture for multiple fixed-wing unmanned aerial vehicles (UAVs) formation flight. The proposed architecture is hybrid in the sense of combining two control policies found in guidance systems: the Decentralized Partially Observable Markov Decision Process (Dec-POMDP) policy and the well-known PID controller. The Dec-POMDP policy part enables every UAV to avoid collision with any other UAV from the formation. The PID part is activated for regions far from the collision risk zone and is implemented in such a way that each UAV is completely controlled by only its own data, independent of its neighbors. The major novelty of the proposed system, compared to other hybrid approaches, is its decentralized nature which favors overcoming the usual problems that come with centralization such as the high dependence on a central decision node, a leader, or a ground station. Inherited from both parts, the overall system performance is robust in noisy and uncertain environments. Simulations were extensively performed to show the effectiveness of the proposed guidance system in distinct scenarios.

Formation Reconstruction and Trajectory Replanning for Multi-UAV Patrol

This article addresses the dynamic formation reconstruction and trajectory replanning problem in the air patrol task using multiple fixed-wing unmanned aerial vehicle formations. Unlike most of the formation flying related work, this article considers a more practical issue that some of the vehicles may break down during operation. In this case, a more reasonable coping strategy is proposed which is to reconstruct the formation such that the task objectives can be achieved optimally. To perform the patrol task, a virtual target is introduced which moves along the patrol path with a predetermined speed. Considering the fact that not all the vehicles have access to the patrol path information, a decentralized estimator is designed for each vehicle to estimate the virtual target state respectively based on which the individual reference trajectories can be generated. As these reference trajectories do not satisfy relevant physical constraints, including system model, control input limits, no-fly zone avoidance, and intervehicle collision avoidance, a novel model predictive trajectory replanning algorithm is developed to generate feasible reference trajectories for each vehicle in real time, which is computationally attractive by incorporating a situation-dependent mechanism. Simulations have been conducted to validate the effectiveness of our proposed algorithm.

Massive MIMO for Cellular-Connected UAV: Challenges and Promising Solutions

Massive multiple-input multiple-output (MIMO) is a promising technology for enabling cellu-lar-connected unmanned aerial vehicle (UAV) communications in the future. Equipped with full-dimensional large arrays, ground base stations (GBSs) can apply adaptive fine-grained 3D beamforming to mitigate the strong interference between high-altitude UAVs and low-altitude terrestrial users, thus significantly enhancing the network spectral efficiency. However, the performance gain of massive MIMO critically depends on accurate channel state information of both UAVs and terrestrial users at the GBSs, which is practically difficult to achieve due to UAV-in-duced pilot contamination and UAVs' high mobility in 3D. Moreover, the increasingly popular applications relying on a large group of coordinated UAVs or UAV swarm as well as the practical hybrid GBS beamforming architecture for massive MIMO further complicate the pilot contamination and channel/beam tracking problems. In this article, we provide an overview of the above challenging issues, propose new solutions to cope with them, and discuss promising directions for future research. Preliminary simulation results are also provided to validate the effectiveness of the proposed solutions.

Resilience optimization for multi-UAV formation reconfiguration via enhanced pigeon-inspired optimization

This paper develops a novel optimization method oriented to the resilience of multiple Unmanned Aerial Vehicle (multi-UAV) formations to achieve rapid and accurate reconfiguration under random attacks. First, a resilience metric is applied to reflect the effect and rapidity of multi-UAV formation resisting random attacks. Second, an optimization model based on a parameter optimization problem to maximize the system resilience is established. Third, an Adaptive Learning-based Pigeon-Inspired Optimization (ALPIO) algorithm is designed to optimize the resilience value. Finally, typical formation topologies with six UAVs are investigated as a case study to verify the proposed approach. The experimental results indicate that the proposed scheme can achieve resilience optimization for a multi-UAV formation reconfiguration by increasing the system resilience values to 97.53% and 81.4% after random attacks.

Leader-Follower UAV Formation Model Based on R5DOS-Intersection Model

This paper proposes a formation of multiple unmanned aerial vehicles (UAVs) based on the R5DOS (RCC-5 and orientation direction) intersection model. After improving the R5DOS-intersection model, we evenly arranged 16 UAVs in 16 spatial regions. Compared with those of the rectangular formation model and the grid formation model, the communication costs, time costs, and energy costs of the R5DOS model formation were effectively reduced. At the same time, the operation time of UAV formation was significantly enhanced. The leader-follower method can enhance the robustness of the UAV formation and ensure the integrity of communication during UAV formation operation. Finally, we conducted a simulation experiment on the model and found that the R5DOS model formation was stable and could maintain the desired formation. The randomly generated UAVs could quickly fly to the formation path in a short time, establish formation, and carry out operations. When the leader fails, the follower could travel to the original trajectory of the failed leader in a short time, replace the leader, and continue to communicate and improve the robustness of the formation. To sum up, the UAV formation based on the R5DOS model has the advantages of long operation time, strong endurance, low communication cost, and stable formation, which is of great significance for research on UAV formation.

UAVs-UGV Leader Follower Formation Using Adaptive Non-Singular Terminal Super Twisting Sliding Mode Control

Leader follower formation of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has found numerous applications such as surveillance of critical infrastructure, industrial automation and disaster management emergency. For completion of high precision group tasks, the choice of appropriate control mechanism is of utmost importance. In presence of environmental effects,external disturbances and parametric uncertainties in the UAVs and UGV models, the controller design process is a challenging task. In order to address the aforementioned problems and to ensure minimum tracking errors and fast convergence of the states, this article proposes an adaptive robust formation and trajectory tracking control scheme for a leader follower formation of UAVs and UGV using Non-Singular Terminal Super Twisting Sliding Mode Control Method.Adaptive compensators are derived based on Lyapunov function method and stability of the proposed controllers is guaranteed. Two variants of the control schemes namely Adaptive Super Twisting SMC (AST-SMC) and Adaptive Non-Singular Terminal Super Twisting SMC (ANSTS-SMC) are tested using numerical simulations performed in MATLAB/Simulink. From the results presented, and with the proposed ANSTS-SMC control scheme, the measured $[X~Y]$ tracking errors for leader, follower1 and follower2 UAVs are $[{0~0.01}]m$ , $[{0.01~0.02}]m$ and $[{0.01,~0.02}]m$ respectively, While with the AST-SMC method the peak $[X~Y]$ tracking errors for leader, follower1 and follower2 UAVs are $[{0.05~0.05}]m$ , $[{0.1~0.2}]m$ and $[{0.05~0.1}]m$ respectively. The proposed leader follower formation can be effectively used to monitor solar/PV panels and cables in large solar parks.

Communication Among Heterogeneous Unmanned Aerial Vehicles (UAVs): Classification, Trends, and Analysis

In the past decade, unmanned aerial vehicles (UAVs) have become a major research topic in robotics, despite their existence since 1915. UAVs can perform various activities efficiently and effectively; therefore, involving a group of UAVs in performing a certain task has become a growing research trend. Research involving multiple UAVs has been carried out in various areas such as force protection, warfare in the military, remote sensing, disaster response activities, and surveillance. While performing critical tasks, efficiency and robustness are important considerations that can be achieved by involving heterogeneous UAV teams with effective communication and coordination techniques. Communication among heterogeneous UAVs is an interesting and critical research area that needs to be thoroughly explored. In this research, an evidence-based approach is adopted to explore research carried out on multiple heterogeneous UAVs and their communication patterns while performing various activities. A mapping study research technique is employed to systematically collect, analyze, and assess the evidence available on the topic under discussion. The time period defined for this study was set from 2005 to 2019, and 46 primary studies were considered for thematic analysis. The findings show that research studies fall under the category of validation research by constructing simulations to provide a proof-of-concept implementation of the proposed solutions. The communication patterns among heterogeneous UAVs involve various components of UAV communication, networking, formation, and path planning. The application areas that were largely focused on were search, rescue, monitoring, and surveillance missions. The overall trend showed that interest in multiple heterogeneous UAV usage increases over time with a focus on UAV networks, formations, and path planning while considering communication as an implicit part of all these structures. This research has conducted an in-depth analysis of existing research on heterogeneous UAVs and provides classification and trends of various themes emerging within this research area.