Research on UAV positioning model based on pure azimuth passive positioning

The traditional leader-follower Unmanned Aerial Vehicle (UAV) formation cooperative positioning (CP) algorithm, based on relative ranging, requires at least four leader UAV positions to be known accurately, using relative distance with leader UAVs to achieve the unknown position follower UAV’s high-precision positioning. When the number of the known position leader UAVs is limited, the traditional CP algorithm is not applicable. Aiming at the minimum cooperative unit, which consists of a known position leader UAV and an unknown position follower UAV, this paper proposes a CP method based on the follower UAV’s moving vector. Considering the follower UAV can only acquire the single distance with the leader UAV at each distance-sampling period, it is difficult to determine the follower UAV’s spatial location. The follower UAV’s moving vector is used to construct position observation of the follower UAV’s inertial navigation system (INS). High-precision positioning is achieved by combining the follower UAV’s moving vector. In the process of CP, the leader UAV obtains a high-precision position by an INS/Global Positioning System (GPS) loosely integrated navigation system and transmits its position information to the follower UAV. Based on accurate modeling of the follower UAV’s INS, the position, velocity and heading observation equation of the follower UAV’s INS are constructed. The improved extended Kalman filtering is designed to estimate the state vector to improve the follower UAV’s positioning accuracy. In addition, considering that the datalink system based on radio signals may be interfered with by the external environment, it is difficult for the follower UAV to obtain relative distance information from the leader UAV in real time. In this paper, the availability of the relative distance information is judged by a two-state Markov chain. Finally, a real flight test is conducted to validate the performance of the proposed algorithm.

This paper proposes a method of unmanned aerial vehicle (UAV) formation with connecting devices to take advantage of the aerodynamic advantages of the UAV fleet during close range formation flight. By combining the theory of multi-rigid-body system with the traditional six-degree-of-freedom equations of aircraft, a multi-rigid-body dynamics model of the connected UAV formation is established, which can describe the dynamic characteristics of the UAV formation when the formation changes. On this basis, the attitude control system of the UAV formation consisting of UAVs with only one control surface is designed, and the full-state non-linear numerical simulation model of the UAV formation is built in Simulink. The simulation results validate the rationality of the modeling method, and show that the attitude control system designed can effectively deal with the difficulties caused by the coupling of longitudinal and lateral controls and the complex aerodynamic forces of UAV formation.

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.

This paper addressed a well-documented open problem regarding unmanned aerial vehicles (UAVs) formation. Considering dynamic system features, a novel optimal formation algorithm for UAV system is presented in this paper. The UAV formation optimization problem can be first modeled as a linear quadratic optimization problem based on the analysis of the discrete UAV tracking error dynamic model. Subsequently, in order to achieve the purpose of minimizing the error of the system state including speed and position, a new optimal formation control design method is proposed. Finally, the effectiveness of the algorithm is verified by numerical simulation.

In this paper, a novel three dimensional (3D) positioning framework of fluid antenna system (FAS)-enabled unmanned aerial vehicles (UAVs) is developed. In the proposed framework, a set of controlled UAVs including an active UAV and four FAS-enabled passive UAVs cooperatively estimate the real-time 3D position of a target UAV. Here, the active UAV transmits a measurement signal to the passive UAVs via the reflection from the target UAV. Each passive UAV estimates the distance of the active-target-passive UAV link and selects an antenna port to share the distance information with the base station (BS), which calculates the real-time position of the target UAV. As the target UAV is moving due to its task operation, the controlled UAVs must optimize their trajectories and select optimal antenna port for transmitting the positioning information, aiming to estimate the real-time position of the target UAV. We formulate this problem as an optimization problem whose goal is to minimize the target UAV positioning error via optimizing the trajectories of all controlled UAVs and antenna port selection of passive UAVs. To address this problem, an attention-based recurrent multi-agent reinforcement learning (AR-MARL) scheme is proposed, which enables each controlled UAV to use the local Q function to determine its trajectory and antenna port while optimizing the target UAV positioning performance without knowing the trajectories and antenna port selections of other controlled UAVs. Different from current MARL methods that use feedforward neural networks to approximate Q functions, the proposed method uses a recurrent neural network (RNN) that incorporates historical state-action pairs of each controlled UAV, and an attention mechanism to analyze the importance of these historical state-action pairs, thus improving the global Q function approximation accuracy and the target UAV positioning accuracy. Simulation results show that the proposed scheme can reduce the average positioning error by up to 17.5% and 58.5% compared to the value decomposition based-MARL scheme with FAS and the proposed AR-MARL method without FAS.

This paper proposes an algorithm for cooperative guidance of an unmanned aerial vehicle (UAV) formation following a set of waypoints. Waypoints are linked by straight lines to form a sequence, which effectively tasks the formation to cooperatively follow a rectilinear path between these points. Unlike in earlier known papers, we propose a Fermat’s spiral rather than a circular arc as a fillet to smooth the trajectory. This is because a Fermat’s spiral has zero curvature at its origin, which helps avoid breaks in curvature observable when using straight lines and circular arcs. To make a UAV formation and to follow the path, we propose a novel guidance vector field that is direction- and magnitude-nonuniform. This is where the novelty of this paper lies, as it proposes the vector field method for following a Fermat’s spiral. Known studies used line-of-sight (LOS) guidance to that end. In that case, the UAV formation shapes itself using consensus-based coordination topology, i.e. the group has no leader. To test the algorithm for cooperative waypoint following when smoothing is provided by fillets, we utilized complete nonlinear models of two fixed-wing UAVs, each equipped with a tuned autopilot. Numerical simulation shows the proposed approach efficient.

With technological development of multi sensors, UAV (unmanned aerial vehicle) can identify and locate key targets in essential monitoring areas or geological disaster-prone areas by taking video sequence images, and precise positioning of the video sequence images is constantly a matter of great concern. In recent years, precise positioning of aerial images has been widely studied. But it is still a challenge to simultaneously realize precise, robust and dynamic positioning of UAV’s patrolling video sequence images in real time. In order to solve this problem, a visual positioning model for patrolling video sequence images based on DOM rectification is proposed, including a robust block-matching algorithm and a precise polynomial-rectifying algorithm. First, the robust block-matching algorithm is used to obtain the best matching area for UAV’s video sequence image on DOM (Digital Orthophoto Map), a pre-acquired digital orthophoto map covering the whole UAV’s patrolling region. Second, the precise polynomial-rectifying algorithm is used to calculate accurate rectification parameters of mapping UAV’s video sequence image to the best matching area obtained above, and then real time positioning of UAV’s patrolling video sequence images can be realized. Finally, the above two algorithms are analyzed and verified by three practical experiments, and results indicate that even if spatial resolution, surface specific features, illumination condition and topographic relief are significantly different between DOM and UAV’s patrolling video sequence images, proposed algorithms can still steadily realize positioning of each UAV’s patrolling video sequence image with about 2.5 m level accuracy in 1 s. To some extent, this study has improved precise positioning effects of UAV’s patrolling video sequence images in real time, and the proposed mathematical model can be directly incorporated into UAV’s patrolling system without any hardware overhead.

Unmanned Aerial Vehicle (UAV) is a new type of flying work tool with the ability to execute tasks. Under strong electromagnetic interference, communication can be carried out by ultraviolet (UV) .Ultraviolet Communication was a communication mode used 200nm~280nm wavelength ultraviolet light as an information carrier. It has the advantage of all-weather operations, non-line-of-sight (NLOS) communication and strong anti- interference. When performing a mission, the UAV formation usually needs to form a fixed formation during the flight to ensure the stability of the link communication network. Moreover, due to the limited energy carried by the UAV, the energy consumption of UAV network should be reduced as much as possible on the basis of maintaining communication in order to maximize the flight time of UAV. The UAVs form persistent formation that need to know the location information of all the UAVs in the formation. When the GPS is not working properly, a kind of two dimensional (2D) localization algorithm which based on the wireless UV ranging method provides relative location information for the UAV formation. This paper proposes a persistent formation generation algorithm for the location information of UAVs that are not shared, the algorithm enables the UAV formation to generate a stable network topology with the least communication links and low energy consumption.

This paper describes an accurate attitude determination technique using multiple global navigation satellite system (GNSS) receivers and antennas for small unmanned aerial vehicles (UAVs). A laser survey using a small UAV needs highly accurate position and attitude information for reconstructing 3D models. The proposed technique uses the multiple GNSS antenna's positions computed by the GNSS carrier phase measurements to estimate the UAV's position and attitude. To solve the GNSS carrier phase ambiguity, the reliable ambiguity search is performed with a couple of constraints for the base line vector. The performance of the proposed technique is evaluated in the static test. From the test, the proposed technique could estimate UAV's attitude in 0.1 degree accuracy. Furthermore, the accurate UAV's position could be estimated in almost 100 % availability.

This manuscript presents an application of the multi-layer control scheme to guide a formation of unmanned aerial vehicles (UAV) in positioning and trajectory tracking missions. In such case, each part of the formation control problem is dealt by an individual layer, which is independent module dealing with a specific part of the navigation problem. These layers are responsible to generate the desired path of the formation, to provide the desired posture of the robots, and to establish the control signal of each robot to reach their desired positions. The formation controller here introduced is able to coordinate the robots to the desired formation, including the possibility of time-varying position and/or shape, while a nonlinear underactuated controller previously proposed is responsible to guide the UAVs to their desired positions. The stability analysis of the closed-loop system is demonstrated in the sense of Lyapunov, resulting that the formation errors are ultimately bounded. In the sequel, a strategy to guide a formation of multiple unmanned aerial vehicles (MUAV) to accomplish positioning and trajectory-tracking tasks is also proposed. Delaunay triangulation is here used to split the platoon of UAVs in N-triangles, which are individually guided by the multi-layer control scheme (MLCS). The advantage of the MLCS in such proposal is the maintenance of the the Jacobian matrix order, regardless of the number of robots in the formation, which reduces the complexity of the control problem. Simulation results demonstrate the ability to accomplish the aforementioned tasks, thus validating the proposal.

With the development of technologies related to unmanned vehicles, including unmanned aerial vehicle and unmanned surface vehicle, unmanned vehicles play a pivotal role in maritime management and cruise activities. Meanwhile, lots of evidence shows that a single unmanned vehicle is not qualified for operation under complex environment, whereas the unmanned aerial vehicle–unmanned surface vehicle formation consists of multiple unmanned aerial vehicles and unmanned surface vehicles, which can enhance the performance of the single unmanned vehicle significantly. However, the cooperative operation performance of the unmanned aerial vehicle–unmanned surface vehicle formation is constrained by its communication capacity. Consequently, a cooperative communication framework is designed for the unmanned aerial vehicle–unmanned surface vehicle formation. A distributed dynamic network topology is constructed for fulfilling effective communication for the unmanned aerial vehicle–unmanned surface vehicle formation based on ad hoc network. Experiments are carried out by employing the built unmanned aerial vehicle–unmanned surface vehicle formation platform. Results verify the superiority of the designed cooperative communication framework for the unmanned aerial vehicle–unmanned surface vehicle formation.

Local and asymmetrical potential field approach to leader tracking problem in rigid formations of fixed-wing UAVs

Manipulating objects using arms mounted to unmanned aerial vehicles (UAVs) is attractive because UAVs may access many locations that are otherwise inaccessible to traditional mobile manipulation platforms such as ground vehicles. Historically, UAVs have been employed in ways that avoid interaction with the environment at all costs. The recent trend of increasing small UAV lift capacity and the reduction of the weight of manipulator components make the realization of mobile manipulating UAVs imminent. Despite recent work, several major challenges remain to be overcome before it will be common practice to manipulate objects from UAVs. Among these challenges, the constantly moving UAV platform and compliance of manipulator arms make it difficult to position the UAV and end-effector relative to an object of interest precisely enough for reliable manipulation. Solving this challenge will bring UAVs one step closer to being able to perform meaningful tasks such as infrastructure repair, disaster response, law enforcement, and personal assistance. Toward a solution to this challenge, this thesis describes a way forward that uses the UAV as a means to crudely position a manipulator within reach of the end-effector's goal position in the world. The manipulator then performs the fine positioning of the end-effector, rejecting position perturbations caused by UAV motions. An algorithm to coordinate the redundant degrees of freedom of an aerial manipulation system is described that allows the motions of the manipulator to serve as inputs to the UAV's position controller. To demonstrate this algorithm, the manipulator's six degrees of freedom are servoed using visual sensing to drive an eye-in-hand camera to a specified pose relative to a target while treating motions of the host platform as perturbations. Simultaneously, the host platform's degrees of freedom are regulated using kinematic information from the manipulator. This ultimately drives the UAV to a position that allows the manipulator to assume a pose relative to the UAV that maximizes reachability, thus facilitating the arm's ability to compensate for undesired UAV motions. Maintaining this loose kinematic coupling between the redundant degrees of freedom of the host UAV and manipulator allows this type of controller to be applied to a wide variety of platforms, including manned aircraft, rather than a single instance of a purpose-built system. As a result of this loose coupling, careful consideration must be given to the manipulator design so that it can achieve useful poses while minimally influencing the stability of the host UAV. Accordingly, the novel application of a parallel manipulator mechanism is described.

A novel fault-tolerant communication topology management method for the leader–follower unmanned aerial vehicle (UAV) formation is proposed to minimize the formation communication cost while keeping the formation shape, even in the case of communication faults during the formation flight. This method is based on Edmonds’ algorithm for the minimum cost arborescence problem in graph theory. When a formation shape is given before the formation flight, this method can get the optimal initial communication topology with the minimum formation communication cost for keeping the formation shape. When some communication faults occur during the formation flight, which will cause the formation shape cannot be kept, this method can reconfigure the communication topology in time to guarantee the safety of all UAVs and recover the formation shape, and then it can reoptimize the communication topology by UAV position reconfiguration in the formation shape to minimize the formation communication cost for continuously keeping the formation shape. The effectiveness of this method is demonstrated through several simulation experiments.

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