Machine vision has been extensively researched in the field of unmanned aerial vehicles (UAV) recently. However, the ability of Sense and Avoid (SAA) largely limited by environmental visibility, which brings hazards to flight safety in low illumination or nighttime conditions. In order to solve this critical problem, an approach of image enhancement is proposed in this paper to improve image qualities in low illumination conditions. Considering the complementarity of visible and infrared images, a visible and infrared image fusion method based on convolutional sparse representation (CSR) is a promising solution to improve the SAA ability of UAVs. Firstly, the source image is decomposed into a texture layer and structure layer since infrared images are good at characterising structural information, and visible images have richer texture information. Both the structure and the texture layers are transformed into the sparse convolutional domain through the CSR mechanism, and then CSR coefficient mapping are fused via activity level assessment. Finally, the image is synthesised through the reconstruction results of the fusion texture and structure layers. In the experimental simulation section, a series of visible and infrared registered images including aerial targets are adopted to evaluate the proposed algorithm. Experimental results demonstrates that the proposed method increases image qualities in low illumination conditions effectively and can enhance the object details, which has better performance than traditional methods.

Aerodynamic characterisation from flight testing is an integral subroutine for evaluating a new flight vehicle’s aerodynamic performance, stability and controllability. The estimation of aerodynamic parameters from flight test data has extensively been explored, in the past, using estimation methods such as the equation error method, output error method and filter error method. However, in the current era, non-gradient-based estimation techniques are gaining attention from researchers due to their inherent data-driven optimisation capability to find the global best solution. In this paper, a novel non-gradient-based estimation method is proposed for the aerodynamic characterisation of unmanned aerial vehicles from flight data, which relies on the maximum likelihood method augmented with particle swarm optimisation. Flight data sets of a wing-alone unmanned aerial vehicle are used to demonstrate the capabilities of the proposed method in estimating aerodynamic derivatives. Estimates from the proposed method are corroborated with the wind tunnel test and output error method results. It has been observed that simulated flight vehicle responses using estimated parameters are in good agreement with measured data in most of the manoeuvers considered. Confidence in the estimates of linear and nonlinear aerodynamic parameters is well established with the lower limit of Cramer-Rao bounds, which are minimal. The proposed method also demonstrates good predictability of the quasi-steady stall aerodynamic model by estimating stall characteristic parameters such as aerofoil static stall characteristics parameter, hysteresis time constant and breakpoint. The overall performance of the proposed estimation method is on par with the output error method and is validated with the proof-of-match exercise.

The need for innovative solutions to enable aerial platforms to fly faster, higher, and longer continues to remain a primary focus for airframe designers. This paper outlines work undertaken to apply a morphing wing warping technology onto a generic Unmanned Aerial Vehicle to deliver enhanced flight performance, efficiency and control capabilities. The prototype employs wings of novel construction which provide both near resistance-free compliance in twist as well as adequate structural stiffness to resist applied loads; all while preserving an aerodynamically smooth surface. Used in combination with developed and integrated closed-loop feedback control architecture, a real-time, non-linear, span-wise wing twist adjustment capability required for optimised flight under differing operating conditions and flight requirements, is demonstrated. Experimental results obtained from a wind tunnel test program show up to a 72% increase in lift to drag ratio under certain conditions compared to a fixed baseline providing some confidence that the combination could be used to realise a step change in flight performance.

The major component for the effective functioning of airborne unmanned aerial vehicles (UAVs) is an antenna. The unified integration of an antenna with UAVs without disturbing the other components of UAVs, compact size, thin, and wideband antennas are preferred. Based on these prerequisites, a low-profile and a wideband antenna is presented for autonomous UAVs for C-band applications. The antenna is designed on an extremely thin substrate of size 0.01λ0 without compromising the antenna's broad bandwidth. The antenna structure comprises a corrugated U-shaped patch, a few parasitic patches along with the partial ground. Initially, only a single mode was excited using a U-shaped patch, then further on introducing a few more parasitic patches along with corrugations in a U-shaped patch other modes were also coupled leading to enhancement in the bandwidth of the antenna. The proposed antenna along with a 2 × 2 quad-port multiple input, multiple output (MIMO) antenna is designed and fabricated. The measured results are found to be in good agreement with the simulated results. A broad bandwidth of 74.5% (4.1–9.0 GHz) with 5.02 dBi peak gain at 6.6 GHz is exhibited by the fabricated MIMO antenna. Thus, the designed antenna proves to be an effective candidate for UAV-based C-band applications.

This article presents the research status and development trends of Vertical Take-off and Landing hybrid Unmanned Aerial Vehicles. In this research, a special emphasis is laid on the design philosophies, analysis techniques, dynamic modeling, and control laws of hybrid VTOL UAVs. It studies and compares various design configurations of hybrid VTOL UAVs, based on key design features such as aerodynamic performance, flight stability, structural strength, propulsive power, avionics systems, flight controls, autonomy, ease of fabrication and flight transition mechanisms. The benefits and shortcomings of each design configuration are expressed in detail. A selection problem is formulated in a fuzzy environment and the Multi-Attribute Decision-Making technique is employed. Ongoing research projects in the field are discussed and a novel design of tail sitter hybrid VTOL UAV is presented by the authors. This work serves as a useful guide for the prospective explorers of this challenging field of research.

This research focuses on a collision-free real-time motion planning system for unmanned aerial vehicles (UAVs) in complex three-dimensional (3D) dynamic environments based on generalized potential force functions. The UAV must survive in such a complex heterogeneous environment while tracking a dynamic target and avoiding multiple stationary or dynamic obstacles, especially at low hover flying conditions. The system framework consists of two parts. The first part is the target tracking part employing a generalized extended attractive potential force into 3D space. In contrast, the second part is the obstacle avoidance part employing a generalized extended repulsive potential force into 3D space. These forces depend on the relative position and relative velocity between the UAV and respective obstacles. As a result, the UAV is attracted to a moving or stationary target and repulsed away from moving or static obstacles simultaneously in 3D space. Accordingly, it changes its altitude and projected planner position concurrently. A real-time implementation for the system is conducted in the SPACE laboratory to perform motion planning in 3D space. The system performance is validated in real-time experiments using three platforms: two parrot bebop drones and one turtlebot robot. The pose information of the vehicles is estimated using six Vicon cameras during real-time flights. The demonstrated results show the motion planning system’s effectiveness. Also, we propose a successful mathematical solution of the local minima problem associated with the potential field method in both stationary and dynamic environments.

South Texas is home to a high diversity of species due to its location at the confluence of subtropical, desert, and coastal ecoregions. Historical overgrazing of South Texas rangelands transformed the savanna and prairie to a landscape dominated by woody plants and shrubs interspersed with low seral grass species and bare ground. During the first half of the 20th century, exotic grass species, coupled with the application of industrial agricultural practices appeared to be the future of forage production in South Texas and elsewhere. Several of these exotic species, namely King Ranch bluestem [Bothriochloa ischaemum (L.) Keng], Kleberg bluestem [Dichanthium annulatum (Forssk.) Stapf], Angelton bluestem [Dichanthium aristatum (Poir.) C.E. Hubbard], buffelgrass [Pennisetum ciliare (L.) Link], guineagrass [Urochloa maxima (Jacq.) R. Webster], Lehmann lovegrass (Eragrostis lehmanniana Nees), and Bermudagrass [Cynodon dactylon (L.) Pers.], have escaped pasture cultivation. Additionally, the native grass tanglehead [Heteropogon contortus (L.) P. Beauv. ex Roem. & Schult.] has begun displaying invasive behaviors. The monoculture growth habit of these species simplifies vegetation structure, reduces biodiversity, and decreases habitat for many species of wildlife. These grasses also alter natural fire regimes and nutrient cycling. This landscape-level transformation of vegetation composition and structure requires monitoring to quantify and assess the spatial and temporal distributions of invasive species as a basis to inform management practices. Current advances in remote sensing technologies, such as very high spatial resolution coupled with daily satellite imagery and unmanned aerial vehicles, are providing tools for invasive vegetation monitoring. We provide a synthesis of the natural history of these grasses, including their introductions, an overview of remote sensing applications in South Texas, and recommendations for future management practices.

This paper introduces an intuitive and safe command approach for a quadrotor, where inertial and muscular gestures are used for semi-autonomous flight. A bracelet composed of gyroscopes, accelerometers, and electromyographic sensors is used to detect user gestures, then an algorithm is proposed to interpret the signals as flight commands. The main goal is to provide a wearable, easy-to-handle human–machine interface for users a to safely command this kind of vehicles, even for inexpert operators. Safety measures are incorporated in the scheme to further enhance the user’s experience. Experimental tests are performed to validate the proposal.

This paper considers actuator redundancy management for a redundant multirotor Unmanned Aerial Vehicle (UAV) under actuators failures. Different approaches are proposed: using robust control (passive fault tolerance), and reconfigurable control (active fault tolerance). The robust controller is designed using high-order super-twisting sliding mode techniques, and handles the failures without requiring information from a Fault Detection scheme. The Active Fault-Tolerant Control (AFTC) is achieved through redistributing the control signals among the healthy actuators using reconfigurable multiplexing and pseudo-inverse control allocation. The Fault Detection and Isolation problem is also considered by proposing model-based and model-free modules. The proposed techniques are all implemented on a coaxial octorotor UAV. Different experiments with different scenarios were conducted for the validation of the proposed strategies. Finally, advantages, disadvantages, application considerations and limitations of each method are examined through quantitative and qualitative studies.

The range of small, electrically powered UAVs is still limited by the mass specific energy of batteries. This paper investigates the idea that, in cases where multiple aircraft must transit to the same location, savings in mass or an extension of achievable range are possible when they join wingtip-to-wingtip. The viability of joined flight is investigated by quantifying the relative magnitude of savings resulting from increased aerodynamic efficiency and that of penalties due to the increased structural and component weights. Through a parametric analysis the level of savings achievable is found to be greatly dependent on the proportion of the flight spent in a joined configuration and aircraft design parameters such as wing loading, aspect ratio and the added weight of the joining mechanism. A custom, multidisciplinary UAV sizing algorithm is presented and utilised to design several sample aircraft, featuring two different joining mechanism architectures. The results verify the findings of the parametric study and indicate that mass savings are possible only for moderate to low aspect ratios, with semi-permanent magnetic joining mechanism performing better than rigid structural ones, even when the joined fight segment accounts for only 30% of the total airborne time.

This paper deals with the problem of fault-tolerant control (FTC) for redundant multirotor unmanned aerial vehicles (UAVs) subject to actuators failures. A fuzzy logic approach is used to solve the constrained control allocation problem by adjusting the components of the multiplexing vector once a motor failure is detected. This fuzzy logic allocation problem is tuned using the Bacterial Foraging Algorithm (BFA), a powerful bio-inspired optimisation technique. The effectiveness of this approach is illustrated through real experimental application to a hexarotor UAV, where up to two motors failures are considered.

A novel statistical model is presented to quantify situation awareness in the operation of small civilian Unmanned Aircraft Systems (UAS). Today, the vast majority of small Unmanned Aircraft Systems (UAS) operation takes place under Visual Line of Sight (VLOS) of a human operator, who is wholly responsible for the safety of the flight. As operation begins to move to Beyond Visual Line of Sight (BVLOS), it is likely that this responsibility will become shared between operator and the increasingly autonomous UAS itself. Before we seek to quantify the safety of such a system, it is beneficial to analyse the safety of existing Visual Line of Sight (VLOS) operations to provide a target level of safety. Prior to considering any on-board decision making, it is essential to ensure that the artificial situation awareness system of a UAS in Beyond Visual Line of Sight (BVLOS) is at least as good as awareness of a human operator. The paper provides a probabilistic theory and model for the high-level abstractions of situation awareness to guide future assessment of BVLOS operations.

Protected areas across the range of the African savannah elephant Loxodonta africana are increasingly being surrounded and isolated by agriculture and human settlements. Conflicts between people and crop-raiding elephants regularly lead to direct reprisals and diminish community support for conservation. We report on field trials in northern Tanzania that employed a new, humane way for wildlife managers to move elephants away from conflict zones, from distances of > 100 m, thereby enhancing the safety of wildlife managers, farmers and elephants. We deployed 10 unmanned aerial vehicles (drones) piloted by five trained teams of wildlife managers in the Tarangire–Manyara and Serengeti ecosystems. Game Scouts deployed the drones opportunistically during crop-raiding events at the peak of the maize ripening period in 2015 and 2016. In 100% of trials (n = 51) elephants responded to the presence of a drone by departing rapidly from crop fields (n = 38) and settlements (n = 13). The cost of five teams responsible for 617 km2 in Tarangire–Manyara was estimated to be USD 15,520 for 1 year, and all drones remained operational for the duration of the study. The initial success of this tool warrants further testing of the utility of small unmanned aerial vehicles as part of the toolbox for wildlife managers and communities dealing with high levels of conflict with wildlife.

A new algorithm, called rapidly exploring random tree of trees (RRTOT) is proposed, that aims to address the challenge of planning for autonomous structural inspection. Given a representation of a structure, a visibility model of an onboard sensor, an initial robot configuration and constraints, RRTOT computes inspection paths that provide full coverage. Sampling based techniques and a meta-tree structure consisting of multiple RRT* trees are employed to find admissible paths with decreasing cost. Using this approach, RRTOT does not suffer from the limitations of strategies that separate the inspection path planning problem into that of finding the minimum set of observation points and only afterwards compute the best possible path among them. Analysis is provided on the capability of RRTOT to find admissible solutions that, in the limit case, approach the optimal one. The algorithm is evaluated in both simulation and experimental studies. An unmanned rotorcraft equipped with a vision sensor was utilized as the experimental platform and validation of the achieved inspection properties was performed using 3D reconstruction techniques.