Unmanned Aerial Vehicles Formation Research Articles

Organizing Control of Unmanned Aerial Vehicle Formations in Combat Support of Piloted Aircraft

Organizing Control of Unmanned Aerial Vehicle Formations in Combat Support of Piloted Aircraft

Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network.

Collaboration between multiple Unmanned Aerial Vehicles (UAVs) to establish a Flying Ad-hoc Network (FANET) is a growing trend since future applications claim for more autonomous and rapidly deployable systems. In this context, Software-Defined Networking FANET (SDN-FANET ) separates the control and data plane and provides network programmability, which considers a centralized controller to perform all FANET control functions based on global UAV context information, such as UAV positions, movement trajectories, residual energy, and others. However, control message dissemination in an SDN-FANET with low overhead and high performance is not a trivial task due to FANET particular characteristics, i.e., high mobility, failures in UAV to UAV communication, and short communication range. With this in mind, it is essential to predict UAV information for control message dissemination as well as consider hierarchical network architecture, reducing bandwidth consumption and signaling overhead. In this article, we present a Cluster-bAsed control Plane messages management in sOftware-defined flying ad-hoc NEtwork, called CAPONE. Based on UAV contextual information, the controller can predict UAV information without control message transmission. In addition, CAPONE divides the FANET into groups by computing the number of clusters using the Gap statistics method, which is input for a Fuzzy C-means method to determine the group leader and members. In this way, CAPONE reduces the bandwidth consumption and signaling overhead, while guaranteeing the control message delivering in FANET scenarios. Extensive simulations are used to show the gains of the CAPONE in terms of Packet Delivery Ratio, overhead, and energy compared to existing SDN-FANET architectures.

Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models.

In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data.

Research on Image Navigation Algorithms for UAV

UAV (unmanned aerial vehicle) is used in agriculture filed to inspect water conservancy facilities, it not only can greatly reduce labor, but also effectively save relevant costs. The choice of UAV navigation mode is the key problem to achieve patrol of water conservancy facilities. In order to solve this problem, a new method of UAV image navigation is proposed which is different from traditional image navigation, and experimental research is carried out. Firstly, the acquired image is corrected and preprocessed to reduce image noise by perspective transformation. Then, the target information is extracted by threshold segmentation and ROI (region of interest) method. Finally, the attitude information of UAV is calculated and displayed in the original image through OpenCV (Open Source Computer Vision Library) library function. Based on the calculation results, the attitude of UAV can be adjusted by flight controller.

Fully distributed guidance laws for unmanned aerial vehicles formation flight

In this paper, three fully distributed guidance laws are designed for unmanned aerial vehicles formation flight, which have the following advantages. Adaptive technology in novel guidance laws can adapt to various graphs that only need one spanning tree. Cooperative formation does not need to set the virtual structure of formation in advance, but only needs to adjust the formation parameters in the guidance law to achieve the desired time-varying formation. This paper uses a guidance law perpendicular to the line of sight to make the flight trajectory more straight; hence, enhancing its applicability in real-world scenarios. These new guidance laws also enable group formation transformation and can optimize the unmanned aerial vehicles’ formation without global information to obtain the optimum performance of the formation. The simulation results show the practicability and effectiveness of the new method.

Tracking control of multiple unmanned aerial vehicles incorporating disturbance observer and model predictive approach

This paper studies the disturbance observer-based model predictive control approach to deal with the unmanned aerial vehicle formation flight with unknown disturbances. The distributed control problem for a class of multiple unmanned aerial vehicle systems with reference trajectory tracking and disturbance rejection is formulated. Firstly, a local distributed controller is designed by using the model predictive control method to achieve stable tracking, where the local optimization problem is solved by an adaptive differential evolution algorithm. Then, a feedforward compensation controller is introduced by using a disturbance observer to estimate and compensate disturbances, and improve the ability of anti-interference. Besides, the stability of the proposed composite controller is analyzed as well. Finally, the simulation examples are provided to illustrate the validity of proposed control structure.

Cooperative Occupancy Decision Making of Multi-UAV in Beyond-Visual-Range Air Combat: A Game Theory Approach

In this paper, a new cooperative occupancy decision-making problem for multiple unmanned aerial vehicles (UAVs) in beyond-visual-range (BVR) air combat is proposed; that is, UAV formation on each side first decides the occupancy positions of their own UAVs, then decides the target that each UAV will confront, so that each side can occupy the greatest predominance and be subject to the smallest threat possible. By analyzing the influence of the occupancy positions of the UAVs on the predominance gained by both sides and the threat against them, the problem is described as a zero-sum matrix game. Because the strategy combinations of this game are very numerous, as the best algorithm for the zero-sum matrix game, the Double Oracle (DO) algorithm still has difficulty solving the game model effectively. Therefore, we propose a DO combined with neighborhood search (DO-NS) algorithm to deal with the large-scale zero-sum matrix game. The results show that compared with the DO algorithm, the DO-NS algorithm can ensure the effectiveness of the computational time and solution quality.

Multivariable adaptive control based consensus flight control system for UAVs formation

Formation flight contributes to improving the attack, reconnaissance and survival ability of the multiple unmanned aerial vehicles (UAVs). This paper studies a multivariable adaptive control based consensus flight method for UAVs formation. A majority of existing research is focused on the leader-following consensus problem assuming that only the parameters of followers are uncertain. However, they do not consider the leader dynamic uncertainty and the unknown external disturbances. Therefore, this paper addresses the problem of the UAVs consensus flight control with parametric uncertainties and unknown external disturbances for both the leader and follower. A multivariable model reference adaptive control (MRAC) based consensus flight control scheme is designed for UAVs formation, which enables the follower UAV to track the leader UAV. The stability of the multivariable MRAC based consensus flight control system is analyzed. Simulation results show that the proposed adaptive consensus flight control scheme has stronger robustness and adaptivity than the fixed control scheme.

Adaptive Differential Evolution-based Receding Horizon Control Design for Multi-UAV Formation Reconfiguration

The complicated and constrained reconfiguration optimisation for unmanned aerial vehicles (UAVs) is a challenge, particularly when multi-mission requirements are taken into account. In this study, we evaluate the use of the adaptive differential evolution-based centralised receding horizon control approach to achieve the formation reconfiguration along a given formation group trajectory for multiple unmanned aerial vehicles in a three-dimensional (3D) environment. A rolling optimisation approach which combines the receding horizon control method with the adaptive differential evolution algorithm is proposed, where the receding horizon control method divides the global control problem into a series of local optimisations and each local optimisation problem is solved by an adaptive differential evolution algorithm. Furthermore, a novel quadratic reconfiguration cost function with the topology information of UAVs is presented, and the asymptotic convergence of the rolling optimisation is analysed. Finally, simulation examples are provided to illustrate the validity of the proposed control structure.

Efficient path planning for UAV formation via comprehensively improved particle swarm optimization

Automatic generation of optimized flyable path is a key technology and challenge for autonomous unmanned aerial vehicle (UAV) formation system. Aiming to improve the rapidity and optimality of automatic path planner, this paper presents a three dimensional path planning algorithm for UAV formation based on comprehensively improved particle swarm optimization (PSO). In the proposed method, a chaos-based Logistic map is firstly adopted to improve the particle initial distribution. Then, the common used constant acceleration coefficients and maximum velocity are designed to adaptive linear-varying ones, which adjusts to the optimization process and meanwhile improves solution optimality. Besides, a mutation strategy that undesired particles are replaced by those desired ones is also proposed and the algorithm convergence speed is accelerated. Theoretically, the comprehensively improved PSO not only speeds up the convergence but also improves the solution optimality. Finally, Monte-Carlo simulation for UAV formation under terrain and threat constraints are carried out and the results illustrate the rapidity and optimality of the proposed method.

Virtual target guidance-based distributed model predictive control for formation control of multiple UAVs

The paper proposes a Virtual Target Guidance (VTG)-based distributed Model Predictive Control (MPC) scheme for formation control of multiple Unmanned Aerial Vehicles (UAVs). First, a framework of distributed MPC scheme is designed in which each UAV only shares the information with its neighbors, and the obtained local Finite-Horizon Optimal Control Problem (FHOCP) can be solved by swarm intelligent optimization algorithm. Then, a VTG approach is developed and integrated into the distributed MPC scheme to achieve trajectory tracking and obstacle avoidance. Further, an event-triggered mechanism is proposed to reduce the computational burden for UAV formation control, which takes into consideration the predictive state errors as well as the convergence of cost function. Numerical simulations show that the proposed VTG-based distributed MPC scheme is more computationally efficient to achieve formation control of multiple UAVs in comparison with the traditional distributed MPC method.

Motion Capability Analysis for Multiple Fixed-Wing UAV Formations With Speed and Heading Rate Constraints

This paper analyzes the motion capability of multiple fixed-wing unmanned aerial vehicle (UAV) formations by taking speed and heading rate constraints of individual UAVs into account. Unlike most of the existing literature investigating multi-UAV formation flying problems, which mainly focus on planner/controller design in order to achieve desired configurations, this paper aims at providing a quantitative description of the UAV formation maneuverability, which can serve as a metric for evaluating formation trajectory plans. Three common formations, namely perfect rigid formation, semiperfect rigid formation, and flexible formation are considered in this paper. Motion capabilities of these formations are analyzed in terms of turning performance. In particular, the feasible set of the formation heading rate is employed as the evaluation metric, based on which the relationships among these three formations are discussed. It has been proven that perfect rigid formation suffers from a rather limited mobility, while flexible formation has the largest feasible set of the formation heading rate in some situations. An interesting finding of this paper is that flexible formation is not necessarily more agile than semiperfect rigid formation with the same formation width, due to the special stall speed constraint of fixed-wing UAVs. Besides rigorous mathematical derivations, simulations have also been performed to further support the theoretical analyses.

Cooperative path planning for leader – follower formation of Multi UAV based on the minimum energy consumption for load transportation

Today, unmanned aerial vehicles are highly considered for both military and commercial fields due to low cost, high maneuverability, and good survival. One of the most important design challenges of multi-UAV systems is mission planning. In the broad class of unmanned aerial vehicles (UAV), Quadrotor is an important member. The capabilities of this vehicle in load transportation has attracted the attention of many research groups around the world. In this paper, path planning based on the minimum energy consumption is studied for load transportation. The purpose of the present study is to investigate the effect of proposed cost function in order to obtain optimal path for transporting loads based on reducing the energy consumption of quadrotors. The results indicate the 35.29% energy consumption of multi UAV compared to the energy consumption of an individual quadrotor. This also leads to an increase in load carrying capacity. On the other hand, the leader- follower formation is preserved until the end of the path based on the defined relationships. The simulation results illustrate the power and efficiency of the method to overcome the high nonlinearity nature of the problem such as path optimization of multi-rotor helicopters.

System Architecture for Real-Time Surface Inspection Using Multiple UAVs

This paper presents a real-time control system for surface inspection using multiple unmanned aerial vehicles (UAVs). The UAVs are coordinated in a specific formation to collect data of the inspecting objects. The communication platform for data transmission is based on the Internet of Things (IoT). In the proposed architecture, the UAV formation is established via using the angle-encoded particle swarm optimisation to generate an inspecting path and redistribute it to each UAV where communication links are embedded with an IoT board for network and data processing capabilities. Data collected are transmitted in real time through the network to remote computational units. To detect potential damage or defects, an online image processing technique is proposed and implemented based on histograms. Extensive simulation, experiments and comparisons have been conducted to verify the validity and performance of the proposed system.

Ultraviolet anti-collision and localization algorithm in UAV formation network

Ultraviolet (UV) Communication is a communication mode used 200nm˜280nm wavelength ultraviolet light as an information carrier. It has the advantages of all-weather operations, non-line-of-sight (NLOS) communication with a wide field and strong anti-interference ability etc. Solar blind characteristics can achieve anti-collision in unmanned aerial vehicles (UAV) formation flying. The distance calculation formula for line-of-sight (LOS) and NLOS is analyzed. One kind of three dimensional (3D) localization algorithm based on wireless UV communication is put forward. UAVs need anti-collision and location methods to ensure flight safety when flying or performing tasks in a complex atmospheric environment. Computer simulation result shows that the ranging precision of UV ranging algorithm can reach 3m, The node localization accuracy rate of localization algorithm can reach more than 80%. Numerical simulation results show that the localization algorithm has a high location accuracy, which can meet the requirements of anti-collision in UAV formation.

Formation Control of Multiple UAVs Incorporating Extended State Observer-Based Model Predictive Approach

This paper studies the extended state observer-based state space predictive control approach to deal with the multiple unmanned aerial vehicle formation flight with unknown disturbances. The distributed control problem for a class of multiple unmanned aerial vehicle systems with reference trajectory tracking and disturbance rejection is formulated. Firstly, a local distributed controller is designed by using the state space predictive control approach based on an error model to achieve stable tracking. Then, a feedforward compensation controller is introduced by using the extended state observer to estimate and compensate disturbances and improve the ability of anti-interference. Besides, the bounded stability of the designed extended state observer is analyzed as well. Finally, the simulation examples are provided to illustrate the validity of the proposed control structure.

Research on Formation Reconfiguration of UAVs Based on RRT Algorithm

To adapt the complexity and flexibility of battlefield environment, a method of formation reconfiguration based on Rapidly-exploring Random Tree (RRT) algorithm is proposed, which shows the advantage of formation of Unmanned Aerial Vehicles (UAVs). Firstly, the kinematic model for UAVs is built, and the feasibility of combination of traditional RRT algorithm and formation reconfiguration of UAVs is analyzed. Secondly, the strategies of trajectory correction comprising node removal and transition trajectory are adopted. Then the dynamic and collision avoidance constraints are discussed respectively, which are essential for exploring the process of RRT algorithm as well as adjusting the trajectory of UAVs. Finally, the simulation and flight experiment are carried out to verify the effectiveness of the proposed method. The results show that the reconfiguration method is able to achieve the formation reconfiguration rapidly and safely. Moreove, the planed trajectory can satisfy the tracing requirement, which is of significance for flight of UAVs in the battlefield environment.

Calibrating Sentinel-2 Imagery with Multispectral UAV Derived Information to Quantify Damages in Mediterranean Rice Crops Caused by Western Swamphen (Porphyrio porphyrio)

Making agricultural production compatible with the conservation of biological diversity is a priority in areas in which human–wildlife conflicts arise. The threatened Western Swamphen (Porphyrio porphyrio) feeds on rice, inducing crop damage and leading to decreases in rice production. Due to the Swamphen protection status, economic compensation policies have been put in place to compensate farmers for these damages, thus requiring an accurate, quantitative, and cost-effective evaluation of rice crop losses over large territories. We used information captured from a UAV (Unmanned Aerial Vehicle) equipped with a multispectral Parrot SEQUOIA camera as ground-truth information to calibrate Sentinel-2 imagery to quantify damages in the region of Ebro Delta, western Mediterranean. UAV vegetation index NDVI (Normalized Difference Vegetation Index) allowed estimation of damages in rice crops at 10 cm pixel resolution by discriminating no-green vegetation pixels. Once co-registered with Sentinel grid, we predicted the UAV damage proportion at a 10 m resolution as a function of Sentinel-2 NDVI, and then we extrapolated the fitted model to the whole Sentinel-2 Ebro Delta image. Finally, the damage predicted with Sentinel-2 data was quantified at the agricultural plot level and validated with field information compiled on the ground by Rangers Service. We found that Sentinel2-NDVI data explained up to 57% of damage reported with UAV. The final validation with Rangers Service data pointed out some limitations in our procedure that leads the way to improving future development. Sentinel2 imagery calibrated with UAV information proved to be a viable and cost-efficient alternative to quantify damages in rice crops at large scales.

Resilience evaluation for UAV swarm performing joint reconnaissance mission.

The resilience of unmanned aerial vehicle (UAV) swarm is its joint capability to resist possible threat, adapt to disruptive events, and restore its intended performance under a specific time period. The quantitative assessment of the UAV swarm resilience requires a thorough understanding of its missions. In this paper, a mission-oriented framework is proposed to implement the resilience evaluation for the UAV swarm. Guided by the framework, the resilience evaluation for the UAV swarm performing joint reconnaissance mission is studied. A UAV swarm model is developed for joint reconnaissance mission based on complex networks and agent-based models. The following aspects of the UAV swarm are considered in the proposed model, namely, the mission orientation, UAV attributes, swarm topology, UAV cooperative strategy, UAV information exchange and fusion strategy, potential threats, recovery strategies, etc. Then, a novel performance metric is proposed to measure the mission capability of the UAV swarm performing joint reconnaissance mission. Results from the simulations show that, compared with existing studies, the proposed approach can provide more realistic and objective resilience evaluation for the mission-oriented UAV swarm. The above works can be used to support the decision making and the optimal design of the UAV swarm, given different missions.

Finite-Time Formation of Unmanned Aerial Vehicles with Switching Topologies and Disturbances: An Average Dwell Time Approach

This paper investigates the finite-time formation problem of unmanned aerial vehicles (UAVs) with switching topologies and external disturbances. The formation problem is first transformed into the finite-time stability and bounded problems of a switched system, respectively. In particular, the finite-time unachievable topology is concerned for unreliable information exchanges. By employing the average dwell time (ADT) method, sufficient criteria are established to deal with the switching topologies such that the desired time-varying formation can be achieved in finite time. Then, the topology-dependent controller can be designed in terms of matrix technique. Finally, an illustrative example is given to verify the effectiveness of our proposed formation strategy.