Unmanned Aerial Vehicles Formation Research Articles

Stochastic H∞ Robust Decentralized Tracking Control of Large-Scale Team Formation UAV Network System With Time-Varying Delay and Packet Dropout Under Interconnected Couplings and Wiener Fluctuations

In this article, a robust decentralized tracking control scheme for a large-scale unmanned aerial vehicle (UAV) formation team networked control system (NCS) is proposed to overcome a non-scalable or even infeasible design problem due to high computational complexity by the centralized control. At the outset, wind external disturbance, intrinsic fluctuation, coupling from other UAV subsystems, time-varying network-induced delay and packet dropout are taken into account in the design procedure of large-scale team formation UAV network system. Moreover, an event-triggered mechanism is embedded in NCS to reduce the number of transmitted control signals to save the network traffic especially for a large-scale team formation network of UAVs. To effectively reduce the above uncertainties, a robust \textH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> decentralized tracking controller is proposed for the large-scale UAV team formation NCS which is constructed by virtual leader-follower tracking network of UAV subsystems. By the Takagi-Sugeno (T-S) fuzzy interpolation method, the \textH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> robust virtual leader-follower decentralized tracking control design problem of the large-scale UAV team formation NCS is transformed to an independent Hamilton-Jacobin inequality (HJI)-constrained optimization problem for each UAV, which is effectively solved by a linear matrix inequality (LMI)-constrained optimization problem. Finally, a large-scale UAV team formation example of 25 UAVs is proposed to validate the effectiveness of the proposed robust decentralized tracking controller of large-scale UAV team formation NCS.

Skeleton-Based Swarm Routing (SSR): Intelligent Smooth Routing for Dynamic UAV Networks

A swarm of unmanned aerial vehicles (UAVs) requires the transmission of mission-related data across the network. The resource constraints and dynamic nature of the swarm bring critical challenges to the design of UAV routing protocols. Most of the conventional ad hoc routing schemes are not intelligent and cannot adapt to the dynamic nature of UAV swarming networks. On the other hand, some artificial intelligence (AI)-based routing schemes may consume significant computational resources in the UAVs. In this article, a low-cost, adaptive routing protocol, namely skeleton-based swarm routing (SSR) , is proposed, which exploits an intelligent online learning algorithm and the topology features of the mission-driven UAV swarm to distribute the traffic over optimal routes. Here, the skeleton represents the most stable parts of the swarm formation. SSR architecture consists of three modules: 1) A geometric addressing module, which assigns geometric coordinates to each node based on the swarm skeleton structure; 2) A leaf-like routing pipe which allows the selection of multiple candidate routes around the shortest path; 3) An intelligent low-complexity learning model which determines how to distribute the packets inside the routing pipe to achieve load-balanced, high-throughput transmissions. The proposed skeleton-based scheme can also facilitate the UAV formation construction and morphing. The simulation results show that the proposed SSR protocol can noticeably improve the network performance (up to 100% throughput improvement) compared to the single path routing schemes, such as the ad-hoc on-demand distance vector (AODV) and link-quality and traffic-load aware optimized link state routing (LTA-OLSR) protocols.

Consensus-based cooperative control of parallel fixed-wing UAV formations via adaptive backstepping

The paper proposes a novel approach to control fixed-wing unmanned aerial vehicle (UAV) swarm to start and keep flying in a parallel formation of a specific geometry. The distinctive feature of this method is that it is infinitely scalable thanks to synthesizing decentralized cooperative control laws that are globally asymptotically stable. These laws use the architecture of leaderless consensus, in which each UAV communicates with the adjacent UAVs only. Unlike standard linear consensus, these formation controllers consider both the nonholonomic dynamics of fixed-wing UAV and the input constraints for the autopilot-UAV system, which enables the aircraft to take any initial position before the algorithms start running. Together, coordinated control laws generate vector field for cooperative rectilinear path following, a field that is non-uniform in direction and magnitude. The research further applies backstepping techniques to generate control inputs for a more realistic autopilot-UAV model, which is adjusted in view of the fact that the autopilot response to speed- and path-related input is naturally first-order. We have implemented adaptive parametric self-tuning in autopilot-UAV models, as flight can affect the dynamics of any UAV. As a result, each UAV takes its own spot in a parallel formation, whereby each aircraft's speed asymptotically tends to the final cruising speed of the formation while the course angle tends to final course angle, which helps keep the formation stable and its geometry precise. The effectiveness of the proposed approach has been tested in MATLAB/Simulink using six-DOF 12-state non-linear fixed-wing UAV models.

A new consensus theory-based method for formation control and obstacle avoidance of UAVs

Unmanned aerial vehicle (UAV) formation has a wide range of applications as it can increase the success rate of task and improve the reliability of system. Formation control and obstacle avoidance are two related key problems and are studied based on the consensus theory in this paper. First, two forms of kinetic models for UAV are described, and the standard model and the model with autopilot are deployed to apply in obstacle avoidance and formation control respectively. The constraints on the maneuverability of UAV are proposed. Based on the standard consensus algorithm, the three degrees of freedom (DOFs) of UAV in the improved consensus algorithm (ICA) are converted to be consistent with the DOFs describing the relative positions among UAVs, which make it possible to add the formation information to the standard consensus algorithm. The constraints on the maneuverability of UAV are also met by using the proposed minimal adjustment strategy. In the obstacle avoidance problem, the particle swarm optimization (PSO) algorithm is introduced, and the static obstacles and dynamic obstacles are dealt with by the ICA-PSO algorithm and the model predictive control (MPC)-PSO algorithm respectively, and the two algorithms can be combined to cope with more complicated situations. Simulation results demonstrate that the ICA can make UAVs with different initial states form the specified formation while satisfying all the constraints, and a safe and efficient flight for UAV formation can be ensured with the obstacle avoidance algorithm.

Comparison between satellite and ground data with UAV-based information to analyse vineyard spatio-temporal variability

Currently, the greatest challenge for vine growers is to improve the yield and quality of grapes by minimizing costs and environmental impacts. This goal can be achieved through a better knowledge of vineyard spatial variability. Traditional platforms such as airborne, satellite and unmanned aerial vehicles (UAVs) solutions are useful investigation tools for vineyard site specific management. These remote sensing techniques are mainly exploited to get the Normalized Difference Vegetation Index (NDVI), which is useful for describing the morpho-vegetational characteristics of vineyards. This study was conducted in a vineyard in Tuscany (Italy) during the 2017, 2018 and 2019 seasons. Ground data were acquired to detect some agronomic variables such as yield (kg/vine), total soluble solids (TSS), and pruning weight (kg/vine). Remote sensed multispectral images acquired by UAV and Sentinel-2 (S2) satellite platform were used to assess the analysis of the vegetative variability. The UAV NDVI was extracted using both a mixed pixels approach (both vine and inter-row) and from pure canopy pixels. In addition to these UAV layers, the vine thickness was extracted. The aim of this study was to evaluate both classical Ordinary Least Square (OLS) and spatial statistical methods (Moran Index-MI and BILISA) to assess their performance in a multi-temporal comparison between satellite and ground data with UAV information. Good correlations were detected between S2 NDVI and UAV NDVI mixed pixels through both methods (R2 = 0.80 and MI = 0.75). Regarding ground data, UAV layers showed low and negative association with TSS (MI = - 0.34 was the lowest value) whereas better spatial autocorrelations with positive values were detected between UAV layers and both yield (MI ranged from 0.42 to 0.52) and pruning weight (MI ranged from 0.45 to 0.64). The spatial analysis made by MI and BILISA methodologies added more information to this study, clearly showing that both UAV and Sentinel-2 satellite allowed the vigour spatial variability within the vineyard to be detected correctly, overcoming the classical comparison methods by adding the spatial effect. MI and BILISA play a key role in identifying spatial patterns and could be successfully exploited by agricultural stakeholders.

Minimize the routing overhead through 3D cone shaped location-aided routing protocol for FANETs

Recently, designing an effective routing protocol is challenging for flying ad-hoc networks (FANETs) due to the versatile nature of unmanned aerial vehicles (UAVs). Position-based routing schemes are seen to be a remarkable preference for routing in FANETs due to the fact that the geographical information of UAVs is readily available. In this paper, we are interested in the modification of the location-aided routing (LAR) that we call 3D cone shaped location-aided routing (3DC-LAR) a position-based routing (PBR) protocol. In the existing LAR scheme, a route discovery process done through the concept of request zone, in which only nodes presents in the request zone will forward the route request for transmitting the data. The proposed 3DC-LAR scheme modified the definition of request zone using 3D cone shape by intermediate UAVs in FANETs environment. The simulation results reveal that the proposed 3DC-LAR scheme has contributed to lead to an improvement in the terms of routing overhead.

Dynamic event‐triggered cooperative formation control for UAVs subject to time‐varying disturbances

The dynamic event-triggered cooperative formation control problem for unmanned aerial vehicles (UAVs) subject to time-varying disturbances is studied. A single UAV is described by a second-order kinematical model and the desired formation is constructed by utilising the leader–follower framework, in which the leader UAV provides the desired trajectory for each follower. Unknown external disturbances are assumed to be composed of multiple frequency waves and appropriate disturbance observers are designed. A dynamic event-triggered mechanism is proposed with its triggering threshold being dynamically adjustable rather than a fixed constant, which can reduce data transmissions more effectively and meanwhile avoid the Zeno behaviour. Then a distributed event-triggered control scheme is developed for the UAV formation and two sufficient conditions on asymptotical stability of the resulting closed loop system are derived. Furthermore, controller gains, observer gains, and triggering parameters can be jointly obtained by solving the desired linear matrix inequality (LMI). Finally, a simulated example is given to illustrate the obtained results.

A Fault-Tolerant Control Method for Unmanned Aerial Vehicle (UAV) Formation with Topological Faults Considered

A fault-tolerant control method for the UAV formation that has topological fault, control surface fault, actuator fault and uncertainty is proposed. Firstly, the formation motion model and the UAV motion model are established. Then based on the topological fault detection method, the topological fault reconstruction and optimization algorithm is proposed to realize the formation topological fault reconstruction and optimization with minimum communication cost and formation reconstruction cost. In designing the backstepping fault-tolerant control method, the actuator's fault identification module and the auxiliary system module are used to estimate and compensate for the faults of the actuator and the control surface respectively so as to realize the stable flight of the UAV formation under the conditions of actuator fault, control surface fault and uncertainty. The simulation results verify the superiority of the fault-tolerant control method for the UAV formation with topological faults.

Building Information Modelling (BIM) as an UAV Information Processor for Generating an ‘As Built Drawing’

The heritage building is a valuable asset to humans and countries due to the grandeur symbolization and the dignity representation of a country. However, the dramatic changes on the historical building physical feature has been documented poorly. Subsequently, the depletion of the building information has making the maintenance works intricate. On site surveying might difficult due to the safety issue that putting manpower into risky circumstances. Advance technology may allow many to obtain information using aid of remote sensing, photogrammetry, geographic information system (GIS) and 3D laser scanning. The projection of the As Built Drawing might possible without utilisation of BIM as an analyser for the raw information congregated. Thus, the objective of this research is to develop an ‘As Built Drawing’ for heritage building using Building Information Modelling (BIM) as an information processor. The methodology involved the Unmanned Aerial Vehicle (UAV) application and Terrestrial Laser Scanner, photogrammetry image then been analysed using BIM. The identification of design properties of heritage building was obtained which consisted of architectural properties and structural properties. Therefore, the development of ‘As Built Drawing’ will be produced completely using BIM and in the other hand solving the problem on facilities management on the historical building.

Blockchain: A distributed solution to UAV‐enabled mobile edge computing

Mobile edge computing (MEC) is to process, analyse, store and calculate the network data at the edge of the network. When the ground infrastructure is damaged in an emergency, the unmanned aerial vehicle (UAV) formation can be rapidly deployed to undertake the task of MEC. However, there are some potential problems to be considered in UAV-enabled MEC, such as the trust among UAVs from different sources and the stability of UAV formation network. In view of the problems existing, this study proposes a blockchain-based architecture to build a system of mutual trust, fairness, openness, and stability in this scenario. Through the implementation of blockchain technology, key data such as device computing capacity, task allocation, and task execution process are recorded openly, transparently, and irrevocably. As multi-party trust is built to reduce the occurrence of fraud, system participants can get a reasonable reward. On this basis, the smart contract is used to ensure that algorithms are accessible to the public, and the sub-blockchain technology improves the stability of the system. In the case study, the simulation results show that the resource consumption and time cost of the proposed scheme is reasonable and feasible.

Dynamic Spectrum Interaction of UAV Flight Formation Communication With Priority: A Deep Reinforcement Learning Approach

The formation flights of multiple unmanned aerial vehicles (UAV) can improve the success probability of single-machine. Dynamic spectrum interaction solves the problem of the ordered communication of multiple UAVs with limited bandwidth via spectrum interaction between UAVs. By introducing reinforcement learning algorithm, UAVs can continuously obtain the optimal strategy by continuously interacting with the environment. In this paper, two types of UAV formation communication methods are studied. One method allows for information sharing between two UAVs in the same time slot. The other method is the adoption of a dynamic time slot allocation scheme to complete the alternate use of time slots by the UAV to realize information sharing. The quality of experience (QoE) is introduced to evaluate the results of UAV sharing, and the M/G/1 queuing model is used for priority and to evaluate the packet loss of UAV. In terms of algorithms, a combination of deep reinforcement learning (DRL) and the long-short-term memory (LSTM) network is adopted to accelerate the convergence speed of the algorithm. The experimental results show that, compared with the Q-learning and deep Q-network (DQN) methods, the proposed method achieves faster convergence and better performance with respect to the throughput rate.

Navigation information augmented artificial potential field algorithm for collision avoidance in UAV formation flight

In recent years, multiple Unmanned Aerial Vehicle (UAV) formation flight has attracted worldwide research interest, for its potential benefits of scalability and flexibility. In complex urban environments, the successful operation of those UAVs requires the system to provide certain safety level. As one of the key requirements, collision avoidance improves the system’s ability to accommodate operational environment variations, and to perform multiple tasks. To achieve this, artificial potential field (APF) has been recognized as one of the most suitable methods along with drone control. Although there has been substantial relevant work on the APF for single UAV in static environment, more efforts are desired to address formation maneuvers in complex environments such as urban. Most importantly, the traditional APF algorithms do not account for random errors in navigation solutions, which can bring potential risk to the UAV system. In response, this paper proposes a new APF algorithm that employs navigation information in complex urban environments, and the goal is to realize UAV formation collision avoidance. By augmenting the APF algorithm with UAV navigation information, the potential risk caused by navigation uncertainty can be mitigated, especially in the Global Navigation Satellite System (GNSS) challenged environment. The principle of the new APF approach is adaptively estimating the parameters of potential field force function, using the variance of navigation information and user-defined confidence probability. This new approach is applied in the synchronized UAV formation collision avoidance control. As a result, the UAVs can achieve fast position and attitude adjustment with high safety confidence. To verify the algorithm, quadrotors with emulated GNSS receivers are used to generate observation data. These data are incorporated into a complex urban environment simulation, where multiple sets of virtual obstacles are injected. Results show that the proposed method can achieve safe and efficient collision avoidance for cooperative formation flight in urban GNSS challenged environment.

Time‐varying formation of second‐order discrete‐time multi‐agent systems under non‐uniform communication delays and switching topology with application to UAV formation flying

In this study, the time-varying formation control problem for the second-order discrete-time multi-agent systems is investigated, where both the non-uniform communication time-delays and switching topology are taken into account. A linear discrete-time formation protocol is developed based on the neighbouring relative position and velocity information. Using the state transformation method and properties of the stochastic matrix, the formation feasibility condition is given and sufficient conditions for the discrete-time multi-agent systems to accomplish the time-varying formation are established. An unmanned aerial vehicles (UAVs) formation experiment platform is constructed. Using four quadrotor UAVs, UAV formation flying experiments are performed to verify the effectiveness and reliability of the discrete-time formation protocol. The experimental results show that the theoretical results can be used to deal with the time-varying formation control problem for multiple UAVs system with communication delays and switching topology.

A Multi-UAV Formation Maintaining Method Based on Formation Reference Point

In recent years, Unmanned Aerial Vehicles (UAV), have been increasingly utilized by scholars. Efficient control of UAV swarms opens a set of new challenges. There are several formation control methods, however, artificial potential field (APF) method has imperfections in formation maintaining and virtual structure is insufficient in collision avoidance. In this paper, we proposed a multi-UAV formation maintaining method combined artificial potential field method and virtual structure approach. The method uses the concepts of attraction and repulsion in the artificial potential field method, combined with formation reference points inspired by the virtual structure points in the virtual structure approach to control the formation. On the other hand, we express the leaders, the followers, and the formation reference points uniformly. When the formation of UAVs is determined, after the leader arrives at target point and hovers, the followers can track the formation reference points. The method can avoid collision between UAVs and achieve the purpose of formation maintaining. Finally, a UAV swarm consists of four UAVs is used for demonstration and verification, and the results of experiment prove the validity of the algorithm.

A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization

The multi-objective optimization problem has been encountered in numerous fields such as high-speed train head shape design, overlapping community detection, power dispatch, and unmanned aerial vehicle formation. To address such issues, current approaches focus mainly on problems with regular Pareto front rather than solving the irregular Pareto front. Considering this situation, we propose a many-objective evolutionary algorithm based on decomposition with dynamic resource allocation (MaOEA/D-DRA) for irregular optimization. The proposed algorithm can dynamically allocate computing resources to different search areas according to different shapes of the problem’s Pareto front. An evolutionary population and an external archive are used in the search process, and information extracted from the external archive is used to guide the evolutionary population to different search regions. The evolutionary population evolves with the Tchebycheff approach to decompose a problem into several subproblems, and all the subproblems are optimized in a collaborative manner. The external archive is updated with the method of shift-based density estimation. The proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms using a variety of test problems with irregular Pareto front. Experimental results show that the proposed algorithm out-performs these five algorithms with respect to convergence speed and diversity of population members. By comparison with the weighted-sum approach and penalty-based boundary intersection approach, there is an improvement in performance after integration of the Tchebycheff approach into the proposed algorithm.

Signal-domain Kalman filtering: An approach for maneuvering target surveillance with wideband radar

As unmanned aerial vehicles (UAVs), owing to their low cost and wide variety of applications, become increasingly indispensable to modern society, and monitoring their illegal use through multichannel radar tracking has drawn widespread attention. Unfortunately, traditional tracking methods face challenges due to the UAV target with high-maneuverability and low-observability. In addition, the traditional measurements suffer some significant information loss because of their treatments like thresholding, clustering and peak sampling, which decreases tracking performance. In order to track UAV precisely, we propose a novel UAV tracking method based on the joint estimation of range and direction of arrival (DOA) in this paper. A complex-valued reference signal is introduced by coherently integrating in sliding windows to obtain SNR gain and preserve the complete structure and motion information of UAV. Besides motion state, the complex-valued reference signal is also utilized to predict the current return signal based on dynamic equation, and then a Bayes based method is derived to jointly estimate the range and DOA errors by comparing reference signal and return signal. In order to adapt to the maneuverable feature, a precise measurement model for motion variables is constructed to realize tracking combined with Kalman filter. Due to the utilization of the complex-valued reference signal and the precise measurement model, the proposed method has outstanding performance in the scene of low SNR and high maneuverability. In addition, there is no any information loss when the raw data is used to realize estimating and tracking. Finally, simulated and real-measured experiments confirm its remarkable performance.

Adaptive model predictive control with extended state observer for multi‐UAV formation flight

SummaryThis article studies the adaptive model predictive control with extended state observers (ESO) to deal with multiple unmanned aerial vehicles formation flight in presence of external disturbances and system uncertainties. Specifically, to deal with the mismatch of predictive model caused by external disturbances and system uncertainties, ESOs are introduced to estimate the lumped disturbances, where the ultimately bounded property of observer system can be guaranteed by using the Lyapunov stability theorem. With these observations, the distributed adaptive model predictive controller is designed to achieve trajectory tracking and disturbance rejection simultaneously for multiple unmanned aerial vehicles, as well as taking the state and input saturation into account. Moreover, the stability of proposed model predictive controller is analyzed. Finally, the simulation examples are provided to illustrate the validity of the proposed control scheme.

A cooperative interference resource allocation method based on improved firefly algorithm

To deal with the radio frequency threat posed by modern complex radar networks to aircraft, we researched the unmanned aerial vehicle (UAV) formations radar countermeasures, aiming at the solution of radar jamming resource allocation under system countermeasures. A jamming resource allocation method based on an improved firefly algorithm (FA) is proposed. Firstly, the comprehensive factors affecting the level of threat and interference efficiency of radiation source are quantified by a fuzzy comprehensive evaluation. Besides, the interference efficiency matrix and the objective function of the allocation model are determined to establish the interference resource allocation model. Finally, A mutation operator and an adaptive heuristic are integtated into the FA algorithm, which searches an interference resource allocation scheme. The simulation results show that the improved FA algorithm can compensate for the deficiencies of the FA algorithm. The improved FA algorithm provides a more scientific and reasonable decision-making plan for aircraft mission allocation and can effectively deal with the battlefield threats of the enemy radar network. Moreover, in terms of convergence accuracy and speed as well as algorithm stability, the improved FA algorithm is superior to the simulated annealing algorithm (SA), the niche genetic algorithm (NGA), the improved discrete cuckoo algorithm (IDCS), the mutant firefly algorithm (MFA), the cuckoo search and fireflies algorithm (CSFA), and the best neighbor firefly algorithm (BNFA).

Distributed backstepping based control of multiple UAV formation flight subject to time delays

In this study, the authors propose a backstepping-based, distributed formation control method that is stable independent of time delays in communication among multiple unmanned aerial vehicles (UAVs). Centralised formation control of UAVs requires each agent to maintain a separation distance from other agents, which burdens the communication network of the UAVs. To overcome this problem, the authors consider a distributed control scheme wherein each agent updates its attitude and position based on the state information gathered through its neighbours. Instead of directly controlling the thrust generated by the propellers, they partition the mathematical model of the UAV into two subsystems, a linear attitude control loop and a non-linear position control loop. A backstepping-based outer position controller is then designed that interfaces seamlessly with the inner attitude controller of the cascaded control system. The closed-loop stability is established using a rigorous Lyapunov–Krasovskii analysis under the influence of distributed network time delays. Using the directed graph topology and a distributed backstepping structure, it is shown that the stability criterion is delay-independent. The proposed control algorithms are verified in simulation and then implemented in hardware, and actual flight test experiments prove the validity of these algorithms.

Method for analyzing the stability of information transfer between unmanned aerial vehicles in the formation

The use of a formation consisting of adaptive autonomous mobile agents allows solving a wide range of tasks that are often beyond the capabilities of individual agents. A multi-agent formation is a complex high-order dynamic system, so analyzing the stability of such a system is a complex task. At present, the problem of estimating the stability of a formation formed by substantially nonlinear high-order agents with variable dynamic parameters is not sufficiently considered. This task is particularly important for a formation that is affected by complex unstable environmental conditions, in particular for the formation of unmanned aerial vehicles (UAVs). A method for analyzing the stability of formations of nonlinear agents with different types and orders of transfer function has been developed for studying information exchange in UAV networks. The new approach is based on the use of the Popov frequency criterion and the piecewise linear approximation of the hodograph. A computational experiment was performed to analyze the stability of a formation with transfer functions of various types and orders from the 1st to the 10th. The conducted studies revealed a significant difference in the calculated boundary coefficients of formation stability in the linear and nonlinear modes, which confirms the need to analyze the nonlinear stability under the influence of strong destabilizing influences on the formation.