UAV Trajectory Research Articles

PERFORMANCE EVALUATION OF SEQUENTIAL BAND ORIENTATION BY POLYNOMIAL MODELS IN HYPERSPECTRAL CUBES COLLECTED WITH UAV

Abstract. A study on orientation of hyperspectral band cubes acquired with frame camera is presented in this paper. The camera technology is based on a tuneable Fabry-Perot Interferometer (FPI) and captures cubes of images sequentially using two sensors. However, the bands are not recorded at the same instant, which results different exterior orientation parameters (EOPs) for each image band. A technique based on polynomial model is assessed, which determines the EOPs within the hypercube from few sample bands, since a large number of bands are generated. Experiments were performed to assess the feasibility of using the polynomial technique. An analysis of the UAV trajectory was performed and the results of the polynomial technique were compared with those obtained by a conventional bundle adjustment. The trials showed that the results of both techniques were comparable, indicating that the time-dependent polynomial model can be used to estimate the EOPs of all spectral bands, without requiring a bundle adjustment including all bands. The accuracy of the block adjustment was analysed based on the discrepancies obtained from independent checkpoints. The root mean square error (RMSE) was calculated and showed an accuracy of approximately 1 GSD in planimetry and 1.5 GSD in altimetry. This accurate result is important because the proposed technique can significantly reduce the processing workload.

3D Trajectory Optimization in Rician Fading for UAV-Enabled Data Harvesting

In this paper, we consider a UAV-enabled WSN where a flying UAV is employed to collect data from multiple sensor nodes (SNs). Our objective is to maximize the minimum average data collection rate from all SNs subject to a prescribed reliability constraint for each SN by jointly optimizing the UAV communication scheduling and three-dimensional (3D) trajectory. Different from the existing works that assume the simplified line-of-sight (LoS) UAV-ground channels, we consider the more practically accurate angle-dependent Rician fading channels between the UAV and SNs with the Rician factors determined by the corresponding UAV-SN elevation angles. However, the formulated optimization problem is intractable due to the lack of a closed-form expression for a key parameter termed effective fading power that characterizes the achievable rate given the reliability requirement in terms of outage probability. To tackle this difficulty, we first approximate the parameter by a logistic ('S' shape) function with respect to the 3D UAV trajectory by using the data regression method. Then the original problem is reformulated to an approximate form, which, however, is still challenging to solve due to its non-convexity. As such, we further propose an efficient algorithm to derive its suboptimal solution by using the block coordinate descent technique, which iteratively optimizes the communication scheduling, the UAV's horizontal trajectory, and its vertical trajectory. The latter two subproblems are shown to be non-convex, while locally optimal solutions are obtained for them by using the successive convex approximation technique. Last, extensive numerical results are provided to evaluate the performance of the proposed algorithm and draw new insights on the 3D UAV trajectory under the Rician fading as compared to conventional LoS channel models.

Effect of Asymmetric Control Constraints on Fixed-Wing UAV Trajectories

Reference trajectories, for the longitudinal states of a fixed-wing unmanned aerial vehicle subjected to asymmetric control constraints, are distinguished using the characteristics of regions of asymptotic stability calculated as ellipsoidal domains by solving linear matrix inequalities. These domains are used to determine the maximum allowable deviations from the reference, for which stable closed-loop tracking can be ensured. Constraints are imposed on a linear state feedback controller with the saturation modeled as a sector nonlinearity.

Design for Guidance Method of UAV Autonomous Landing on Mobile Platform Based on Prediction of Intersection Points

The development of the technology for UVAs (Unmanned Aerial Vehicle) autonomous landing on mobile platform is introduced in this article. And the existing problems of UAVs autonomous landing on mobile platforms are also analysed. In order to solve these problems, a guidance method of UAV autonomous landing on mobile platform based on prediction of intersection points is proposed in this paper. In this method, the intersection point of the trajectory of UAV and the trajectory of the mobile platform is predicted based on the speed plan for UAV and the motion state of mobile platform. Then, the UAV will be guided to the intersection point by proportional guidance law. And at each time of the landing of the UAV, the location of the intersection point will be corrected in real time. In order to verify this method, a mathematical simulation experiment was designed. The result of this experiment shows that UAVs are able to land more accurately on the mobile platform with less overload using this method.

UAV-Enabled Cooperative Jamming for Improving Secrecy of Ground Wiretap Channel

This letter proposes a novel UAV-enabled mobile jamming scheme to improve the secrecy rate of ground wiretap channel. Specifically, a UAV is employed to transmit jamming signals to combat against eavesdropping. Such a mobile jamming scheme is particularly appealing since the UAV-enabled jammer can fly close to the eavesdropper and opportunistically jam it by leveraging the UAV's mobility. We aim to maximize the average secrecy rate by jointly optimizing the UAV's trajectory and jamming power over a given flight period. To make the problem more tractable, we drive a closed-form lower bound for the achievable secrecy rate, based on which the UAV's trajectory and transmit power are optimized alternately by an efficient iterative algorithm applying the block coordinate descent and successive convex optimization techniques. Simulation results demonstrate that the proposed joint design can significantly enhance the secrecy rate of the considered wiretap system as compared to benchmark schemes.

Wireless Coverage for Mobile Users in Dynamic Environments Using UAV

In this paper, the dynamic deployment of a single UAV as an aerial base station in providing wireless coverage for mobile outdoor and indoor users is studied. The problem of finding the efficient UAV trajectory is formulated with the objective to minimize the required UAV transmit power that satisfies the users’ minimum data rate. The proposed solution to the problem considers the users’ movement in a search and rescue (SAR) operation. More specifically, the outdoor rescue team members are considered to move in a group with the reference point group mobility (RPGM) model. Whilst, the indoor rescue team members are considered to move individually and in a group with random waypoint and RPGM models, respectively. The efficient UAV trajectory is developed using two approaches, namely, heuristic and optimal approaches. The employment of the heuristic approach, namely particle swarm optimization (PSO) and genetics algorithm (GA), to find the efficient UAV trajectory reduced the execution time by a factor of $\simeq ~1/60$ and $\simeq ~1/9$ compared to that when using the optimal approach of brute-force search space algorithm. Furthermore, the use of PSO algorithm reduced the execution time by a factor of $\simeq ~1/7$ compared to that when the GA algorithm is invoked.The performance of the dynamic UAV deployment also outperformed the static UAV deployment in terms of the required transmit power. More specifically, the dynamic UAV deployment required less total transmit power by a factor of about 1/2 compared to the static UAV deployment, in providing wireless coverage for rescue team to perform SAR operation within a rectangular sub-region.

Algorithm of UAV flight disturbance control based on dynamic trajectory learning

UAV is prone to yaw due to disturbance in dynamic flight with load, in order to improve the anti-disturbance ability of UAV, a flight disturbance control algorithm for UAV based on dynamic trajectory learning is proposed. The constraint parameter model of UAV flight control is constructed by time-delay tracking compensation model in homogeneous space, and the time-frequency decomposition of inertial constraint parameters of UAV flight rejection control is carried out by using Laplace transform. Adaptive weighted learning algorithm is used to deal with the dynamic trajectory of UAV under external disturbance. The disturbance error is compensated by adding a post integral at the end point of the learning trajectory. Combined with dynamic trajectory learning and dynamic motion primitive real-time feedback method, the real-time deviation correction of UAV flight trajectory is realized, and the adaptive optimal control of UAV flight disturbance is realized. The simulation results show that the proposed method can correct the error of UAV flight disturbance and improve the stability of UAV flight control.

Penalty boundary sequential convex programming algorithm for non-convex optimal control problems

A nonlinear optimal control problem with non-convex cost function and non-convex state constraints can be addressed by a series of convex programming to obtain numerical solutions in previous methods. However, a feasible initial solution is essential to ensure the convergence. In this paper, slack variables are added into the model to handle the infeasible initial point and are penalized in the cost. What is more, a new approximation point on the boundary of constraints is embraced in each iteration to increase the similarity to original problem and decrease number of iterations. Thus, a penalty boundary sequential convex programming algorithm is proposed, which is globally convergent to a Karush-Kuhn-Tucker (KKT) point of original problem under mild condition. The theoretical basis is guaranteed by a rigorous proof. Single UAV and multi-robots trajectory planning serve as simulations to verify the validity of the presented algorithm.

Hybrid teaching–learning‐based PSO for trajectory optimisation

A hybrid modified teaching–learning-based particle swarm optimisation (HMTL-PSO) initialised by the normalised step cost (NSC), named HMTL-NSCPSO, is proposed for solving trajectory optimisation with complex constraint problems. Specially, the new HMTL-NSCPSO combines the canonical PSO basic policy, the teaching–learning-based optimisation (TLBO) algorithm and the normalised step cost (NSC) function in order to promote diversity, obtain well-speed convergence and to improve search ability. The algorithm is tested on an UAV trajectory optimization problems. Experimental results validate the effectiveness of the HMTL-NSCPSO.

Multi-UAVs tracking target in urban environment by model predictive control and Improved Grey Wolf Optimizer

In this paper, based on the Model Predictive Control (MPC) and Improved Grey Wolf Optimizer (IGWO), a hybrid method is proposed to plan the optimal trajectories of multi-UAVs for target tracking in urban environment. Firstly, the target tracking problem in urban environment is modeled detailedly by formulating the visibility region, the sensor coverage area, the restricted region, the space constraints, etc. Based on the model of target tracking, the centralized MPC method is then adopted as the framework to obtain the trajectories of UAVs in real time, the objective of which is to optimize tracking performance under various constraints. In consideration of the computational complexity of this problem, IGWO, a novel intelligent algorithm with the advantages of good stability and strong search ability is utilized to solve the MPC formulation. It imitates the social hierarchy and predatory strategy of wolf pack, and some improvement strategies are also introduced e.g. the individual memory and the principle of survival of the fittest. Finally, the proposed method is demonstrated in a simulated urban environment. The simulation results show the effectiveness of the proposed hybrid method to solve target tracking problem.

Effect of <i>a priori</i> Information about the Target Location on Optimal Trajectories of Multiple Cooperative UAVs

Effect of <i>a priori</i> Information about the Target Location on Optimal Trajectories of Multiple Cooperative UAVs

Control and Optimization of UAV Trajectory for Aerial Coverage in Photogrammetry Applications

Photogrammetry is a well-studied and much-used analysis tool. Typical use cases include area surveillance, flood monitoring and related tasks. Usually, an Unmanned Aerial System (UAS) ...

A Moving Frame Trajectory Tracking Method of a Flying-Wing UAV Using the Differential Geometry

The problem of UAV trajectory tracking is a difficult issue for scholars and engineers, especially when the target curve is a complex curve in the three-dimensional space. In this paper, the coordinate frames during the tracking process are transformed to improve the tracking result. Firstly, the basic concepts of the moving frame are given. Secondly the transfer principles of various moving frames are formulated and the Bishop frame is selected as a final choice for its flexibility. Thirdly, the detailed dynamic equations of the moving frame tracking method are formulated. In simulation, a moving frame of an elliptic cylinder helix is formulated precisely. Then, the devised tracking method on the basis of the dynamic equations is tested in a complete flight control system with 6 DOF nonlinear equations of the UAV. The simulation result shows a satisfactory trajectory tracking performance so that the effectiveness and efficiency of the devised tracking method is proved.

Parallel and distributed computing for UAVs trajectory planning

The problem of generating optimal flight trajectories for an unmanned aerial vehicle in the presence of no-fly zones is computationally expensive. It is usually solved offline, at least for those parts which cannot satisfy real time constraints. An example is the core paths graph algorithm, which discretizes the operational flight scenario with a finite dimensional grid of positions-directions pairs. A weighted and oriented graph is then defined, wherein the nodes are the earlier mentioned grid points and the arcs represent minimum length trajectories compliant with obstacle avoidance constraints. This paper investigates the exploitation of two parallel programming techniques to reduce the lead time of the core paths graph algorithm. The former employs some parallelization techniques for multi-core and/or multi-processor platforms. The latter is targeted to a distributed fleet of unmanned aerial vehicles. Here the statement of the problem and preliminary development are discussed. A two-dimensional scenario is analysed by way of example to show the applicability and the effectiveness of the approaches.

UAV Path Planning in Mixed‐Obstacle Environment via Artificial Potential Field Method Improved by Additional Control Force

AbstractThis paper deals with a UAV path planning problem in the environment where both solid obstacles and soft obstacles exist. The artificial potential field approach is updated by introducing an additional control force and integrating it with the concept of receding horizon control for UAV trajectory optimization. The original problem is converted into a multi‐objective optimization problem by regarding the involved additional control term as the optimization variable. Seeing as the establishment of an additional control force of soft obstacles is dependent on the probability of certain specifications such as survivability, the additional control term accomplishes the description of the specified property index better than those that have been considered in the past, such as distance and control energy. The optimal solution to the path planning problem, in terms of the additional control force method, and the computational efficiency according to the receding horizon control, both contribute to the proposed algorithm. Meanwhile, the proposed method is able to protect the UAV from local minima of additional control force. The numerical examples verify the advantages of this approach.

Convex Optimization Design in Formation Control Trajectory of Multi UAV

Convex Optimization Design in Formation Control Trajectory of Multi UAV

UAVs Trajectory Planning by Distributed MPC under Radio Communication Path Loss Constraints

In this paper, a distributed linear MPC approach to solve the trajectory planning problem for rotary-wing UAVs is proposed, where the objective of the UAV system is to form a communication network to multiple targets with given radio communication capacities. The approach explicitly incorporates constraints on radio communication path losses, computed by using SPLAT! that is able to take into account terrain models and antenna locations. In order to enhance the online optimization, at each time sample the terrain below each UAV and the communication path losses are approximated with linear functions of the spatial coordinates. This leads to linear MPC sub-problems, which are solved by using convex quadratic programming. An algorithm for automatic initialization and optimal reconfiguration of the communication topology in case of failures or severe radio path loss due to e.g. channel fading, is proposed. The communication network that is provided by the UAVs is considered to be a payload communication capacity that is normally independent of the command and control datalink used to control the UAVs. The performance of the distributed linear MPC trajectory planning and the reconfiguration algorithm is studied on two simulation cases with four UAVs and two targets.

Flight path planning for unmanned aerial vehicles with landmark-based visual navigation

In this paper we present an algorithm to determine a shortest trajectory of a fixed-wing UAV in scenarios with no-fly areas. The innovative feature is that not only the kinematic and dynamic properties, but also the navigational capabilities of the air vehicle are taken into account. We consider a UAV with landmark-based visual navigation, a technique which can cope with long-term GPS outages. A navigation update is obtained by matching onboard images of selected landmarks with internally stored geo-referenced images. To achieve regular updates, a set of landmarks must be identified which are passed by the air vehicle in a proper sequence and with appropriate overflight directions.The algorithm is based on a discretization of the airspace by a specific network. Each path in the network corresponds to a trajectory which avoids the no-fly areas and respects the flight performance of the air vehicle. Full functionality of the navigation can be ensured by dynamically adapting the network to the environmental conditions. A shortest trajectory is then obtained by the application of standard network algorithms.

Unmanned aerial vehicle trajectory tracking algorithm comparison

PurposeThe purpose of this paper is to analyze and compare the performance of several different UAV trajectory tracking algorithms in normal and abnormal flight conditions to investigate the fault‐tolerant capabilities of a novel immunity‐based adaptive mechanism.Design/methodology/approachThe evaluation of these algorithms is performed using the West Virginia University (WVU) UAV simulation environment. Three types of fixed‐parameter algorithms are considered as well as their adaptive versions obtained by adding an immunity‐based mechanism. The types of control laws investigated are: position proportional, integral, and derivative control, outer‐loop nonlinear dynamic inversion (NLDI), and extended NLDI. Actuator failures on the three channels and increased turbulence conditions are considered for several different flight paths. Specific and global performance metrics are defined based on trajectory tracking errors and control surface activity.FindingsThe performance of all of the adaptive controllers proves to be better than their fixed parameter counterparts during the presence of a failure in all cases considered.Research limitations/implicationsThe immunity inspired adaptation mechanism has promising potential to enhance the fault‐tolerant capabilities of autonomous flight control algorithms and the extension of its use at all levels within the control laws considered and in conjunction with other control architectures is worth investigating.Practical implicationsThe WVU UAV simulation environment has been proved to be a valuable tool for autonomous flight algorithm development, testing, and evaluation in normal and abnormal flight conditions.Originality/valueA novel adaptation mechanism is investigated for UAV control algorithms with fault‐tolerant capabilities. The issue of fault tolerance of UAV control laws has only been addressed in a limited manner in the literature, although it becomes critical in the context of imminent integration of UAVs within the commercial airspace.

Dynamic Graph-Search Algorithm for Global Path Planning in Presence of Hazardous Weather

This paper discusses several alternatives, based on graph search, to calculate UAV trajectories that avoid regions with dangerous weather effects and with the least deviation from the shortest trajectory. It is also explained how to adapt graph search algorithms to be used for 3D trajectories with UAVs, and which design considerations should be taken into account (for example the discretization of the airspace). Also, an extension of the Lazy Theta??? algorithm is presented; a dynamic algorithm that calculates the trajectory while the weather hazards information is updated. The paper includes simulations and experimental results in the CATEC testbed with multiple UAVs.