Formation Flight Research Articles

Precision landing comparison between smartphone video guidance sensor and IRLock by hardware-in-the-loop emulation

The Smartphone Video Guidance Sensor (SVGS) is an emerging technology developed by NASA Marshall Space Flight Center that uses a vision-based approach to accurately estimate the six-state position and orientation vectors of an illuminated target of known dimensions with respect to a coordinate frame fixed to the camera. SVGS is a software-based sensor that can be deployed using a host platform’s resources (CPU and camera) for proximity operations and formation flight of drones or spacecraft. The SVGS output is calculated based on photogrammetric analysis of the light blobs in each image; its accuracy in linear and angular motion at different velocities has previously been successfully demonstrated [9]. SVGS has several potential applications in guidance, navigation, motion control, and proximity operations as a reduced-cost, compact, reliable option to competing technologies such as LiDAR or infrared sensing. One of the applications envisioned by NASA for SVGS is planetary/lunar autonomous landing. This paper aims to compare the SVGS performance in autonomous landing with existing technologies: a combination of infrared beacon technology (IRLock) and LiDAR. The comparison is based on a hardware-in-the-loop emulation of a precision landing experiment using a computer-controlled linear motion to emulate the approach motion and the ROS/Gazebo environment to emulate the response of the flight controller to the environment during landing. Results suggest that SVGS performs better than the existing IRLock with LiDAR sensor combination.

New multi-UAV formation keeping method based on improved artificial potential field

Formation keeping is important for multiple Unmanned Aerial Vehicles (multi-UAV) to fully play their roles in cooperative combats and improve their mission success rate. However, in practical applications, it is difficult to achieve formation keeping precisely and obstacle avoidance autonomously at the same time. This paper proposes a joint control method based on robust H∞ controller and improved Artificial Potential Field (APF) method. Firstly, we build a formation flight model based on the “Leader-Follower” structure and design a robust H∞ controller with three channels X, Y and Z to eliminate dynamic uncertainties, so as to realize high-precision formation keeping. Secondly, to fulfill obstacle avoidance efficiently in complex situations where UAVs fly at high speed with high inertia, this paper comes up with the improved APF method with deformation factor considered. The judgment criterion is proposed and applied to ensure flight safety. In the end, the simulation results show that the designed controller is effective with the formation keeping a high accuracy and in the meantime, it enables UAVs to avoid obstacles autonomously and recover the formation rapidly when coming close to obstacles. Therefore, the method proposed here boasts good engineering application prospect.

Multi-UAV Cooperative Obstacle Avoidance of 3D Vector Field Histogram Plus and Dynamic Window Approach

In this paper, we propose a fusion algorithm that integrates the 3D vector field histogram plus (VFH+) algorithm and the improved dynamic window approach (DWA) algorithm. The aim is to address the challenge of cooperative obstacle avoidance faced by multi-UAV formation flying in unknown environments. First, according to the navigation evaluation function of the standard DWA algorithm, the target distance is introduced to correct the azimuth. Then, aiming at the problem that the fixed weight mechanism in standard DWA algorithm is unreasonable, we combine the A* algorithm and introduce variable weight factors related to azimuth to improve the target orientation ability in local path planning. A new rotation cost evaluation function is added to improve the obstacle avoidance ability of two-dimensional UAV. Then, 3D VFH+ algorithm is introduced and integrated with improved DWA algorithm to design a distributed cooperative formation obstacle avoidance control algorithm. Simulation validation suggests that compared with the traditional DWA algorithm, the improved collaborative obstacle avoidance control algorithm can greatly optimize the obstacle avoidance effect of UAVs’ formation flight.

Unmanned aerial vehicle passive positioning technology based on cross-location and target splitting

In order to ensure that the UAV fleet can maintain maximum electromagnetic silence and minimise the emission of UAV electromagnetic signals during formation flight, a theoretical method of pure azimuth passive positioning can be used to optimise their relative positions. This paper analyses different formation situations of UAVs and presents the best solution for pure azimuth passive positioning of UAVs in formation flight and the corresponding positions. The paper first establishes polar coordinates based on known orientation information. It uses the sine theorem for triangles in the plane and the joint cubic equations to obtain the final result. It then uses triangulation to verify the accuracy of the specific location of the UAV by solving a least squares problem. Using the idea of cross-location, two intersecting trajectories are constructed and their intersection is used to improve the specific position of the UAV. Due to the bias in the position of the location of the signal sent by the UAV, this paper uses of the idea of target segmentation to split the target into multiple sub-targets and implement them separately. This is done in order to avoid the need for more UAVs to send their signal while adjusting the UAV position, making the Drones evenly spaced around a circumference.

Model predictive control switching strategy for safe small satellite cluster formation flight

Model predictive control switching strategy for safe small satellite cluster formation flight

Mesoscale Simulation of Shaped Charge Jet Forming and Free Flight Based on B-spline and Domain Interpolation Material Point Method

Shaped charge (SC) generates a fluid-like high-speed jet (SCJ) undergoing extremely large ductile stretching without fracture. It is a formidable challenge to accurately track and monitor the mesoscale deformation characteristics of materials using fluid simulation algorithms. To address this issue, the Material Point Method (MPM) is introduced as an efficient particle-based method that discretizes the continuum into Lagrangian particles moving through a fixed Eulerian grid. By possessing all material properties, these particles facilitate tracking throughout the deformation process and enable the implementation of history-dependent constitutive models. Regrettably, the utilization of MPM in the study of SCJ formation is restricted. The objective of this study is to assess the capability of 2D-axisymmetric MPMs in modeling SCJ formation and free flight at the mesoscale, thereby providing valuable guidelines for their application in SCJs. The MPMs employed in this study are based on the B-spline (BSMPM) and domain interpolations (generalized and convected particle domain interpolations in MPM). The numerical results indicate that BSMPM with cubic and quartic splines is the most suitable method for calculating SCJs due to its exceptional continuity and alignment with the experimental data. The mesoscale evolution of particles reveals that the material undergoes impact crushing and tensile tearing, transforming into a low-speed slug and a high-speed jet. The equivalent plastic strain (EPS) in SCJs exhibits a radial expansion from the exterior to the axis in a layered manner. Particles in the outer layer with a thickness of approximately 1/2 exhibit a 'laminar' distribution, while particles near the axis exhibit 'turbulent' distribution and undergo severe deformation. The hierarchical progression of EPS and particle motion traces provides insight into the underlying causes of mesoscale experimental phenomena, such as the axial elongation of voids in the SCJ slug and the radial distribution of the material in three concentric circles.

Reinforcement learning based UAV formation control in GPS-denied environment

Highly accurate positioning is a crucial prerequisite of multi Unmanned Aerial Vehicle close-formation flight for target tracking, formation keeping, and collision avoidance. Although the position of a UAV can be obtained through the Global Positioning System (GPS), it is difficult for a UAV to obtain highly accurate positioning data in a GPS-denied environment (e.g., a GPS jamming area, suburb, urban canyon, or mountain area); this may cause it to miss a tracking target or collide with another UAV. In particular, UAV close-formation control in GPS-denied environments faces difficulties owing to the low-accuracy position, close distance between vehicles, and nonholonomic dynamics of a UAV. In this paper, on the one hand, we develop an innovative UAV formation localization method to address the formation localization issues in GPS-denied environments; on the other hand, we design a novel reinforcement learning based algorithm to achieve the high-efficiency and robust performance of the controller. First, a novel Lidar-based localization algorithm is developed to measure the localization of each aircraft in the formation flight. In our solution, each UAV is equipped with Lidar as the position measurement sensor instead of the GPS module. The k-means algorithm is implemented to calculate the center point position of UAV. A novel formation position vector matching method is proposed to match center points with UAVs in the formation and estimate their position information. Second, a reinforcement learning based UAV formation control algorithm is developed by selecting the optimal policy to control UAV swarm to start and keep flying in a close formation of a specific geometry. Third, the innovative collision risk evaluation module is proposed to address the collision-free issues in the formation group. Finally, a novel experience replay method is also provided in this paper to enhance the learning efficiency. Experimental results validate the accuracy, effectiveness, and robustness of the proposed scheme.

Target Detection for Synthetic Aperture Radiometer Based on Satellite Formation Flight.

Synthetic aperture interferometers formed by satellite formations have been adopted to improve spatial resolution. Due to the limited number of satellites and limited integrated time, the use of sparse baselines can result in distorted reconstructed images, which will generate false targets or miss true targets. When detecting a target on the Earth from a geostationary orbit, the target usually occupies only one pixel, and it is almost submerged by noise. Considering the slow-varying characteristics of the observation area, combined with historical observation data and the motion characteristics of the target itself, a target detection method based on multi-frame snapshot images is proposed. Firstly, the observation background is estimated using multi-frame historical data, and background elimination is used to suppress the background noise. Then, potential targets are selected using the local brightness temperature characteristics of the targets. Lastly, the target motion tracks are applied to erase false targets and correct the positions of missed targets. Simulation experiments have been conducted, and the false alarm rate and the missing alarm rate are counted for randomly distributed targets.

UAV Formation Adjustment Strategy Based on Greedy Criterion

In order to ensure that the UAV cluster remains electromagnetically silent during the attempted formation flight, so that the emission of electromagnetic wave signalsj is minimised, a purely azimuthal passive positioning method can be used to adjust the position of the UAVs. As it is difficult to keep the UAVs in formation flying on the same horizontal plane for a long time and the initial position information of the UAVs is not known. Therefore, this paper uses the sine and cosine theorems of in-plane triangles to connect slightly deviating UAVs to a determined UAV position, and in combination with the characteristics of the model, constructs a greedy criterion based on the minimum number of UAVs required to transmit the signal, and in this way gives an adjustment scheme for the UAV position.

Autonomous Formation Flight Control of Large-Sized Flapping-Wing Flying Robots Based on Leader–Follower Strategy

Autonomous Formation Flight Control of Large-Sized Flapping-Wing Flying Robots Based on Leader–Follower Strategy

Least global position information based control of fixed-wing UAVs formation flight: Flight tests and experimental validation

The paper addresses the design of an innovative navigation and control method for the flight control of a formation consisting of small and low-cost fixed-wing UAVs by using a leader-follower strategy and a least global position information sharing algorithm. No additional hardware is needed except the classical global positioning system and autopilot. The novel strategy firstly involves the obtaining of the relative kinematics for the formation of UAVs by using a trajectory coordinate system and a navigation algorithm based on global position coordinates. Secondly, the flight control laws for each UAV are designed by means of the Active Disturbance Rejection Control, the convergence and robustness are discussed and proved, while the flight control laws for the whole formation are derived within a PI controller which balances the robustness and the computation of the whole control architecture. Then, by considering the requirements of positioning accuracy in low-cost constrains, the control scheme is validated both by numerical simulations and experimental flight tests along a racetrack-shaped circular path. The results prove an excellent accuracy for the navigation and control method, the follower UAVs following the trajectory of the leader UAV no matter if the flight is straight or turning type.

Design and SITL Performance of an online Distributed Target Estimation for UAV Swarm

In this paper, an online distributed target estimation for a swarm of Unmanned Aerial Vehicles in formation flight is considered. In particular, a flocking algorithm is employed to handle the formation task, while a Distributed Kalman Filter in the Information form is used to perform the estimate of a mobile target state. Software-In-The-Loop simulations are carried on in the ROS/Gazebo environment to illustrate the coupling between the formation and the estimation tasks. Moreover, the influence of swarm connectivity in the estimation process is analyzed, showing the benefits of information fusion.

Estimation of the Relative States of Satellite Formation Flights Using the Adaptive Extended Kalman Filter

Estimation of the Relative States of Satellite Formation Flights Using the Adaptive Extended Kalman Filter

Clothoid-Based Path Planning for a Formation of Fixed-Wing UAVs

Unmanned aerial vehicles (UAVs) are playing an increasingly crucial role in many applications such as search and rescue, delivery services, and military operations. However, one of the significant challenges in this area is to plan efficient and safe trajectories for UAV formations. This paper presents an optimization procedure for trajectory planning for fixed-wing UAV formations using graph theory and clothoid curves. The proposed planning strategy consists of two main steps. Firstly, the geometric optimization of paths is carried out using graphs for each UAV, providing piece-wise linear paths whose smooth connections are made with clothoids. Secondly, the geometric paths are transformed into time-dependent trajectories, optimizing the assigned aircraft speeds to avoid collisions by solving a mixed-integer optimal control problem for each UAV of the flight formation. The proposed method is effective in achieving suboptimal paths while ensuring collision avoidance between aircraft. A sensitivity analysis of the main parameters of the algorithm was conducted in ideal conditions, highlighting the possibility of decreasing the length of the optimal path by about 4.19%, increasing the number of points used in the discretization and showing a maximum path length reduction of about 10% compared with the average solution obtained with a similar algorithm using a graph based on random directions. Furthermore, the use of clothoids, whose parameters depend on the UAV performance constraints, provides smoother connections, giving a significant improvement over traditional straight-line or circular trajectories in terms of flight dynamics compliance and trajectory tracking capabilities. The method can be applied to various UAV formation scenarios, making it a versatile and practical tool for mission planning.

Robust Multi-Model Estimation for Reliable Relative Navigation Based on Observability and Abnormity Analysis

High-precision relative positioning and navigation is a fundamental requirement for many applications such as flight formation, spacecraft docking and collision avoidance. The main purpose of this paper is to develop a robust multi-model estimation algorithm for reliable navigation when there are abnormities of measurement and motion. In order to deal with these abnormities, we propose a quantitative evaluation method of relative navigation system by introducing the degree of observability (DoO) and the degree of abnormity (DoA). In addition, we design a feedforward information fusion and a feedback information allocation method based on DoO and DoA, and thus form a multi-model robust estimation algorithm. In order to testify the effectiveness and robustness of the proposed algorithm, a practical experiment with real data sets gathered in urban areas has been carried out. The results showed that the maximum relative positioning RMSE reduction ratio can reach 75%, and the maximum relative velocity RMSE reduction ratio can reach 51% compared with EKF. Therefore, the proposed method can guarantee the accuracy and robustness of relative navigation under abnormal conditions.

Assignment and Take-Off Approaches for Large-Scale Autonomous UAV Swarms

In the last decade, the popularity of UAVs has increased tremendously. Nowadays, many researchers are interested in UAV swarms. Coordinating a swarm of UAVs is a complicated task and many problems should be addressed before wide-spread adoption. In this work, we focus on the take-off for large-scale UAV swarms, with an extra focus on the assignment phase. The assignment phase is the first take-off stage whereby we decide which UAV on the ground goes to which place in the air. A good assignment algorithm, is quick, and at the same time reduce the total distance travelled as much as possible. We assess the performance of three different assignment algorithms: a heuristic, the original Kuhn-Munkres algorithm (KMA), and the KMA adapted for GPU use. Each algorithm was tested while varying the number of UAVs, as well as the type of flight formation. During the experiments, we measured the calculation time, total distance travelled, and number of flight paths crossing. In terms of total distance travelled, the KMA always outperforms the heuristic. However, the KMA takes longer (orders of magnitude) to calculate the assignment. Realistically, the KMA algorithm can only be used as long as the swarm does not contain more than 500 UAVs. From that point the GPU version of the KMA is faster. We can conclude that, in most cases, it is recommendable to use the KMA for the assignment as it will reduce the distance travelled to a minimum and, consequently, also reduce the number of flight paths crossing.

Research on Obstacle Avoidance Control of Multiple UAV Formation based on Genetic Algorithm

The UAV (Unmanned Aerial Vehicle) group cooperative formation flight technology has the advantages of wide coverage, large activity radius, strong overall search ability and high efficiency of the aircraft group. Therefore, it is suitable for various complex tasks in the military field such as battlefield environment reconnaissance, tactical attack and cooperative search. This paper proposes a consistency control strategy based on GA (Genetic Algorithm) and applies it to multi-UAV formation obstacle avoidance, which can effectively solve the collision between UAVs and between UAV formation and obstacles. Demodulate the received ground desired control command, and introduce an additional auxiliary traction acceleration through GA to avoid the local optimal solution. The auxiliary traction acceleration is related to the speed and relative position of UAV and obstacles. It can be used as a disturbance to solve the local optimal solution, and also as an auxiliary acceleration to improve the speed of avoiding moving obstacles. Finally, the rapid formation and obstacle avoidance of UAV fleet during flight are realized, and the survivability of the fleet in the battlefield environment is improved.

Neural network–based optimal fault compensation control of the nonlinear multi-agent system and its application to UAVs formation flight

This article investigates the optimal consensus problem for unmanned aerial vehicle formation systems with actuator faults based on nonlinear multi-agent systems. Initially, for fault-free multi-agent system, the distributed optimal controllers are constructed based on the adaptive dynamic programming technique. A critic neural network is applied to approximate the solution of the nonlinear Hamilton–Jacobi–Bellman equations, in which the weight updating laws are built to guarantee the weight vectors of the critic neural network convergence. Second, the fault compensators and corresponding tuning laws are proposed to compensate for actuator faults. Through a combination of optimal controllers and fault compensators, the distributed optimal fault-tolerant controllers are obtained. Then, according to Lyapunov extension theorem, some stability criteria for ensuring the stability of the aircraft and the normal flight of the unmanned aerial vehicle formation are established in the event of an actuator failure. Finally, an example of an unmanned aerial vehicle formation system is introduced to verify the efficiency and reliability of the designed optimal fault-tolerant control scheme.

Formation flying along artificial halo orbit around Sun–Earth L2 point for interferometric observations

This paper proposes a method of using multiple spacecraft flying in formation in an artificially reduced halo orbit around the 2nd Lagrange point in the Sun–Earth system (SEL2) to make interferometric observations. One of the requirements for interferometric observations is to collect baseline vectors between telescopes or antennas. A shape-based approach is introduced to design a formation flight orbit that spirally spreads to satisfy this requirement. In this approach, orbits are designed on the basis of a linear theory. However, the formation orbits based on the linear theory quickly diverge because of the unstable dynamics around the SEL2. Therefore, the state-dependent Riccati equation, which allows for nonlinear control, is applied to orbit maintenance. The control system consists of two components: one for maintaining the reference orbit around the SEL2 and the other for controlling the relative position between the multiple spacecraft. The performance of the developed formation flight system is verified through a numerical simulation, confirming that the position accuracy requirement of the infrared interferometer is satisfied. It is also shown that both controls can be achieved with a low-thrust magnitude by using an electric propulsion system.

Augmented reality for training formation flights: An analysis of human factors

Pilot training has been, for decades, aided by flight simulators with different characteristics and degrees of fidelity. However, many studies indicate that, despite the recognized contribution of simulator training, actual flying practice is still necessary, depending on the trained task. This work introduces the proposal of using augmented reality for in-flight training, where elements in the environment outside the aircraft are displayed through an augmented reality headset to create a simulation scenario. The training of basic formation flight is used as an example, as it requires flying with at least two aircraft, resulting in high operational costs and risk of collision between aircraft. In this case, the augmented reality system replaces the real leader aircraft with a projection. In order to evaluate the Technology Readiness Level (TRL) of this proposal, this work presents a prototype of an augmented reality system integrated into a flight simulator to conduct an evaluation campaign. We investigate how the introduction of the augmented reality system impacts on human factors, such as stress and workload, as well as performance. Although the results obtained in a simulated environment are not equivalent to those from an in-flight campaign, the experimental campaign performed in the flight simulator provides a way of evaluating the impact on the pilot of some aspects of the proposed solution, such as the performance of occlusion routines and some ergonomic aspects of the augmented reality headset.