Unmanned Aerial Vehicle Approach Research Articles

An Efficient Detection Approach for Unmanned Aerial Vehicle (UAV) Small Targets Based on Group Convolution

To solve the problem that small drones in the sky are easily confused with background objects and difficult to detect, according to the characteristics of irregular movement, small size, and changeable shape of drones, using a regional target recognition algorithm, the structure characteristics of Group Convolution (GC) in Resnext50 are absorbed. The optimized GC-faster-RCNN is obtained by improving the Fast-RCNN algorithm and the following methods are performed. First, a clustering method is used to analyze the dataset, and appropriate prior bounding box types are obtained. Second, the Resnext50 is used to replace the original feature extraction network, and the improved channel attention mechanism is integrated into its network output to enhance its feature map information. Then, we calculate its effective receptive field according to the Feature Pyramid Network (FPN) structure and redesign the prior bounding box of the corresponding size to construct a multi-scale detection network for small targets. Experiments show that the algorithm has a recognition accuracy of up to 94.8% under 1080 P image quality, and a recognition speed of 8FPS, which can effectively detect the positions of 1–5 small UAVs in a picture. This method provides an effective positioning detection for low-altitude UAVs.

Modified model free dynamic programming :an augmented approach for unmanned aerial vehicle

Modified model free dynamic programming :an augmented approach for unmanned aerial vehicle

Design and Implementation of a Constrained Model Predictive Control Approach for Unmanned Aerial Vehicles

This paper designs and implements a robust and Time-varying Constrained Model Predictive Controller (TCMPC) for the translational and attitude control of a real quadrotor. The proposed approach considers online optimization to find solutions through the hard and soft constraints. All the controller parameters were derived from the experimental test setup and took into consideration the various restrictions and physical constraints associated with the hand-made quadrotor. The proposed controller can possibly linearize and discretize the nonlinear dynamic model of the quadrotor at every sampling time if all constraints and physical restrictions are considered. The performance of the proposed approach was assessed using both a simulation study and a practical implementation. The simulation study considered a quadrotor hovering mode in the presence of wind gusts and encompassed a comparison analysis with a well-tuned Proportional-Integral-Derivative (PID) controller, an Advanced Error model predictive control (AEMPC), and an Efficient MPC (EMPC) approach.. For the real-time implementation, an online optimization algorithm was used and tested on the high clock processor ARM A53 on a new attitude test setup. The experimental results, showed that the proposed controller outperformed the unconstrained MPC, the well-tuned PID controller, and EMPC, especially in terms of rejecting the external wind disturbances. The proposed method real-time TCMPC) approach has the advantages of greater robustness and is not heavily dependent upon the accurate dynamics of the model.

Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests

European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests. Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras: Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests.

Control Based Motion Planning Exploiting Calculus of Variations and Rational Functions: A Formal Approach

This paper presents a control-based trajectory generation approach for unmanned aerial vehicles (UAVs) under dynamic constraints. It exploits the concept of optimal control to find closed-form differential equations that satisfy any arbitrary dynamic limitation mapped into kinematic constraints. Pontryagin’s Minimum Principle applies to derive a set of differential equations in which the dynamic environment is considered in the constrained Hamiltonian function. In particular, we aim to minimize the L2-norm of the control input avoiding dynamic obstacles, given initial and final boundary conditions. Lastly, this paper proposes a novel interpolation algorithm based on rational functions, referred to as rational recursive smooth trajectory (RRST) method. The method generates an analytic expression that approximates the control inputs, for which no closed-form solutions are in general attainable.

A hybrid air-sea cooperative approach combined with a swarm trajectory planning method

AbstractThis work addresses the issue of ocean monitoring and clean-up of polluted zones, as well as the notion of trajectory planning and fault tolerance for semi-autonomous unmanned vehicles. A hybrid approach for unmanned aerial vehicles (UAVs) is introduced to monitor the ocean region and cooperate with swarm of unmanned surface vehicles (USVs) to clean dirty zones. The paper proposes two solutions that apply to trajectory planning from the base of life to the dirty zone for swarm USVs. The first solution is performed by a modified Genetic Algorithm (GA), and the second uses a modified Ant Algorithm (AA). The proposed solutions were both implemented in the simulation with different scenarios for the dirty zone. This approach detects and reduces the pollution level in ocean zones while taking into account the problem of fault tolerance related to unmanned cleaning vehicles.

A QoS-Oriented Trajectory Optimization in Swarming Unmanned-Aerial-Vehicles Communications

This letter aims to present a novel approach for unmanned aerial vehicles (UAV)’ path planning with respect to certain quality of service requirements. More specifically, we study the max-min fairness problem in an air-to-ground communication system where multiple UAVs and multiple ground stations exist. We jointly optimize the UAVs trajectories and power allocation as well as the user scheduling. To this end, we propose an effective iterative algorithm that relies on the successive convex approximation and the block coordinate descent techniques.

Optimized Path-Planning in Continuous Spaces for Unmanned Aerial Vehicles Using Meta-Heuristics

This work presents a novel path-planning approach for Unmanned Aerial Vehicles (UAVs) in continuous 3D environments. This proposal aims to minimize the path length while avoiding collisions through the suitable adjusting of control points (the points that take the UAV from a start position to a target location). The above is stated as a constrained global optimization problem. This problem considers the overall length of the path as the single objective function. Regarding the problem constraints, they are related to the collision of the obstacles with the 3D shape of a path. The assignment of the path shape is also proposed in this work to streamline the planning process. Due to the optimization problem features (high nonlinearity, multimodality, non-differentiability, and the lack of an initial guess solution), a constraints-handling mechanism is used in meta-heuristics to find suitable optimized paths. Also, an enhanced path-search mechanism is included in these algorithms to deal with complex planning scenarios. The enhanced mechanism incorporates a path computed by a variant of the A-Star method (the Pruned A-Star) in the first set of candidate solutions of the meta-heuristics. The proposed approach is tested through six complex scenarios. Moreover, the performance of three well-known meta-heuristics, Differential Evolution (DE), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA), is studied to find a potential candidate to solve the path-planning problem. In this way, the paths found by DE show outstanding performance. The paths obtained by the Pruned A-Star technique are adopted as a point of comparison to determine the advantages and drawbacks of the proposal.

Robust path planning for avoiding obstacles using time-environment dynamic map

Small unmanned aerial vehicles are widely used in urban space because of its flexibility and maneuverability. However, there are full of dynamic obstacles and immobile obstacles which will affect safe flying in urban space. In this paper, a novel integrated path planning approach for unmanned aerial vehicles is presented, which is consisted of three steps. First, a time-environment dynamic map is constructed to represent obstacles by introducing time axis. Second, unmanned aerial vehicles’ flyable paths are explored based on breadth-first algorithm. Third, a path planning method using A* algorithm and local trace-back model is designed in order to discover sub-optimal feasible path rapidly in unmanned aerial vehicles’ field of view. Finally, the simulation results have illustrated that the proposed method can ensure unmanned aerial vehicles’ autonomous path planning safely and effectively in urban space crowded with obstacles.

Robust direction of arrival estimation approach for unmanned aerial vehicles at low signal‐to‐noise ratios

The conventional methods used to estimate the direction of arrival (DOA) of linear frequency modulated (LFM) signals at low signal-to-noise ratios (SNRs), such as the echoes reflected by a small unmanned aerial vehicle (UAV), demonstrate major performance deterioration. In order to eliminate the problem and achieve highly accurate DOA estimation for low-SNR echoes, this paper proposes a novel estimation approach that applies two accumulative methods based on fractional Fourier transform (FrFT). According to the findings of this paper, the algorithm directly accumulates the FrFT result of each echo when it is not possible to determine in advance the speed of the target. When the radial velocity of the small UAV has been estimated ahead of time, the algorithm performs phase compensation to enhance the accumulative effect. Through coherent integration, the algorithm then extracts all the peaks of the target echo waveform, which are then used for the construction of a fractional autocorrelation matrix. Thereafter, multiple signal classification is employed for DOA estimation. Furthermore, with the proposed algorithm, the DOAs of multi-target echoes can be estimated accurately. The effectiveness of the proposed algorithm was verified using Monte-Carlo simulation trials, and the root-mean-square-error of DOA estimation was close to the Cramer-Rao bound.

Vision-based adaptive variable rate spraying approach for unmanned aerial vehicles

Vision-based adaptive variable rate spraying approach for unmanned aerial vehicles

Embedded Mission Decision-Making based on Dynamic Decision Networks in SoPC Platform

This paper tackles a Bayesian Decision Making approach for unmanned aerial vehicle (UAV) mission that allows UAV to quickly react to unexpected events under dynamic environments. From online observations and the mission state-ment, the proposed approach is designed by means of Dynamic Bayesian Networks (DBN) arising from the safety or performance failures analysis. After proposing a DBN model, a probabilistic approach based on Multiple-Criteria Decision-Making (MCDM) is then applied to find the best configuration reaching a balance between performance and energy consumption, thus decide which tasks will be implemented as SW and which as HW execution units, regarding the mission requirement. The proposal UAV mission decision-making is three-pronged, providing: (1) real time image pre-processing of sensor observations; (2) temporal and probabilistic approach based on Bayesian Networks to continuously update the mission plan during the flight; and (3) low-power hardware and software implementations for online and real time embedded Decision Making using Xilinx System on Programmable Chip (SoPC) platform. The proposed approach is then validated with a practical case UAV mission planning using the proposed dynamic decision-maker implemented on embedded system based on a hybrid device.

Attitude estimation for cooperating UAVs based on tight integration of GNSS and vision measurements

This paper presents a cooperative navigation approach for Unmanned Aerial Vehicles (UAVs) that allows robust and accurate attitude determination for a chief vehicle flying in formation with other deputy UAVs. The proposed method is based on a tightly coupled Extended Kalman Filter (EKF) that exploits the spatial diversity of measurements coming from Global Navigation Satellite Systems (GNSS) receivers and a vision system, which are integrated with inertial and magnetic sensor data. The focus is set on outdoor environments and the innovative idea is to extend attitude estimation approaches based on multiple GNSS antennas, to a multi-vehicle system where differential-GNSS and vision-based UAV-to-UAV tracking are exploited to build a virtual additional navigation sensor. Concept and processing architecture are described with emphasis on the EKF measurement update phase which is applicable for any number of cooperating deputies, and for different GNSS processing architectures. Performance of the proposed method is assessed through experimental tests involving two multi-rotors and two fixed ground antennas, one of which is used as Ground Control Point for pointing accuracy analysis. Results show the potential of the developed approach in terms of accuracy and capability to provide drift-free estimates, in real time or in post processing scenarios.

A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles

Dynamic path planning is one of the key procedures for unmanned aerial vehicles (UAV) to successfully fulfill the diversified missions. In this paper, we propose a new algorithm for path planning based on ant colony optimization (ACO) and artificial potential field. In the proposed algorithm, both dynamic threats and static obstacles are taken into account to generate an artificial field representing the environment for collision free path planning. To enhance the path searching efficiency, a coordinate transformation is applied to move the origin of the map to the starting point of the path and in line with the source-destination direction. Cost functions are established to represent the dynamically changing threats, and the cost value is considered as a scalar value of mobile threats which are vectors actually. In the process of searching for an optimal moving direction for UAV, the cost values of path, mobile threats, and total cost are optimized using ant optimization algorithm. The experimental results demonstrated the performance of the new proposed algorithm, which showed that a smoother planning path with the lowest cost for UAVs can be obtained through our algorithm.

Hybrid real-time stereo visual odometry for unmanned aerial vehicles

A visual localization approach for unmanned aerial vehicles (UAVs) based on the hybrid real-time stereo visual odometry (VO) is presented. The hybrid VO initializes the semidirect visual odometry (SVO) with depth obtained from the stereo camera, which runs with the monocular scheme at 30 Hz. Meanwhile, a feature-based stereo VO runs in a parallel thread to improve the reliability. Considering the robustness of the feature-based VO, we not only are able to estimate the pose of the UAV with the same rate of the incoming images but also to recover the pose of the UAV when the SVO fails. We demonstrate the accuracy and the reliability of the hybrid VO based on a public benchmark dataset and a dataset recorded on our experimental platform. In addition, the autonomous hovering experiment verifies that the estimated result is fast and accurate enough to control the position of the UAV.

Filtered Medial Surface Based Approach for 3D Collision-Free Path Planning Problem

This paper introduces an original 3D path planning approach for Unmanned Aerial Vehicle (UAV) applications. More specifically, the core idea is to generate a smooth and collision-free path with respect to the vehicle dimension. Given a 3D grid representation of the environment, the Generalized Voronoi Graph (GVG) is first approximated using a filtered medial surface (FMS) algorithm on the corresponding navigable space. Based on an efficient pruning criterion, the produced FMS excludes GVG portions corresponding to narrow passages unfitting safe UAV navigation constraints, and thus it defines a set of guaranteed safe trajectories within the environment. Given a set of starting and destination coordinates, an adapted A-star algorithm is then applied to compute the shortest path on the FMS. Finally, an optimization process ensures the smoothness of the final path by fitting a set of 3D Bézier curves to the initial path. For a comparative study, the A-star algorithm is applied directly on the input environment representation and relevant comparative criteria are defined to assert the proposed approach using simulation results.

UAV Path Prediction for CD&R to Manned Aircraft in a Confined Airspace for Cooperative Mission

In this paper, a new path prediction approach for unmanned aerial vehicles (UAVs) for conflict detection and resolution (CD&R) to manned aircraft in cooperative mission in a confined airspace is proposed. Path prediction algorithm is established to estimate UAV flight trajectory to predict conflict threat to manned aircraft in time advances (front-end process of CD&R system). A hybrid fusion model is formulated based on three different trajectory prediction conditions considering scenarios in geographical conditions to aid the generation of appropriate resolution advisory of conflict alert. It offers a more precise CD&R system for manned and unmanned aircraft in cooperative rescue missions.

Vine vigour modulates bunch microclimate and affects the composition of grape and wine flavonoids: an unmanned aerial vehicle approach in a Sangiovese vineyard in Tuscany

Background and Aims This study focused on the impact of vine vigour on Sangiovese grapes and wines, applying a high-resolution remote sensing technique by an unmanned aerial vehicle (UAV) platform to identify vigour at the single vine level within a vineyard. Methods and Results A factorial field experiment was established combining two vigour classes (low and high) and two bunch positions within the canopy (north-facing and south-facing). The remote sensing technique by the UAV platform identified the heterogeneity in terms of vigour within the vineyard. Vine vigour influenced significantly the composition of anthocyanins, flavonols and flavan-3-ol monomers of the grapes and wines. Conclusions The work confirmed that UAV-based remote sensing enabled the successful evaluation of vine vigour at high resolution and that vigour influences the flavonoid monomeric compounds as a function of bunch position within the canopy. Significance of the Study Understanding the impact of spatial variability in a vineyard on grape composition should assist the viticulturist and winemaker toward the adoption of adequate viticultural management operations capable of adding value to their wines.

Potential Odor Intensity Grid Based UAV Path Planning Algorithm with Particle Swarm Optimization Approach

This paper proposes a potential odor intensity grid based optimization approach for unmanned aerial vehicle (UAV) path planning with particle swarm optimization (PSO) technique. Odor intensity is created to color the area in the searching space with highest probability where candidate particles may locate. A potential grid construction operator is designed for standard PSO based on different levels of odor intensity. The potential grid construction operator generates two potential location grids with highest odor intensity. Then the middle point will be seen as the final position in current particle dimension. The global optimum solution will be solved as the average. In addition, solution boundaries of searching space in each particle dimension are restricted based on properties of threats in the flying field to avoid prematurity. Objective function is redesigned by taking minimum direction angle to destination into account and a sampling method is introduced. A paired samplest-test is made and an index called straight line rate (SLR) is used to evaluate the length of planned path. Experiments are made with other three heuristic evolutionary algorithms. The results demonstrate that the proposed method is capable of generating higher quality paths efficiently for UAV than any other tested optimization techniques.

A Lyapunov-based three-axis attitude intelligent control approach for unmanned aerial vehicle

A novel Lyapunov-based three-axis attitude intelligent control approach via allocation scheme is considered in the proposed research to deal with kinematics and dynamics regarding the unmanned aerial vehicle systems. There is a consensus among experts of this field that the new outcomes in the present complicated systems modeling and control are highly appreciated with respect to state-of-the-art. The control scheme presented here is organized in line with a new integration of the linear-nonlinear control approaches, as long as the angular velocities in the three axes of the system are accurately dealt with in the inner closed loop control. And the corresponding rotation angles are dealt with in the outer closed loop control. It should be noted that the linear control in the present outer loop is first designed through proportional based linear quadratic regulator (PD based LQR) approach under optimum coefficients, while the nonlinear control in the corresponding inner loop is then realized through Lyapunov-based approach in the presence of uncertainties and disturbances. In order to complete the inner closed loop control, there is a pulse-width pulse-frequency (PWPF) modulator to be able to handle on-off thrusters. Furthermore, the number of these on-off thrusters may be increased with respect to the investigated control efforts to provide the overall accurate performance of the system, where the control allocation scheme is realized in the proposed strategy. It may be shown that the dynamics and kinematics of the unmanned aerial vehicle systems have to be investigated through the quaternion matrix and its corresponding vector to avoid presenting singularity of the results. At the end, the investigated outcomes are presented in comparison with a number of potential benchmarks to verify the approach performance.