An unmanned air vehicle (UAV) GPS location and navigation system

PurposeThis paper aims to present a comprehensive review in major research areas of unmanned air vehicles (UAVs) navigation, i.e. three degree-of-freedom (3D) path planning, routing algorithm and routing protocols. The paper is further aimed to provide a meaningful comparison among these algorithms and methods and also intend to find the best ones for a particular application.Design/methodology/approachThe major UAV navigation research areas are further classified into different categories based on methods and models. Each category is discussed in detail with updated research work done in that very domain. Performance evaluation criteria are defined separately for each category. Based on these criteria and research challenges, research questions are also proposed in this work and answered in discussion according to the presented literature review.FindingsThe research has found that conventional and node-based algorithms are a popular choice for path planning. Similarly, the graph-based methods are preferred for route planning and hybrid routing protocols are proved better in providing performance. The research has also found promising areas for future research directions, i.e. critical link method for UAV path planning and queuing theory as a routing algorithm for large UAV networks.Originality/valueThe proposed work is a first attempt to provide a comprehensive study on all research aspects of UAV navigation. In addition, a comparison of these methods, algorithms and techniques based on standard performance criteria is also presented the very first time.

The UAVs (Unmanned Air Vehicles) have great potentials in different civilian applications, such as oil pipeline surveillance, precision farming, forest fire fighting (yearly), search and rescue, boarder patrol, etc. The related industries of UAVs can create billions of dollars for each year. However, the road block of adopting UAVs is that it is against FAA (Federal Aviation Administration) and ATC (Air Traffic Control) regulations. In this paper, we have reviewed the latest technologies and researches on UAV navigation and obstacle avoidance. We have purposed a system design of Jittering Mosaic Image Processing (JMIP) with stereo vision and optical flow to fulfill the functionalities of autonomous UAVs.

Unmanned air vehicles (UAVs) have received tremendous importance because of their civil, military, industrial, agricultural and security etc. applications. As compared to terrestrial robots, UAVs have to face stronger aerodynamic as well as gravitational forces which effect the dynamics of these flying structures. The control and estimation of unavailable plant states in UAVs is a challenging task due to their nonlinear dynamics in the presence of external uncertainties like air load and noise effects. Practically, external aerodynamic disturbances and sensor noise can alter the desired performance of the aerial robots leading to instability and loss of control of UAVs. Information of all plant states is important for better control and navigation of UAVs. In this paper it is assumed that all plant states are not available, therefore, nonlinear observer design techniques are used to estimate unavailable states of quadrotor plant with least error. The designed nonlinear observer's results are compared in simulations in the presence of external disturbance and sensor noise. Sliding mode observers implemented in this paper exhibit the property of finite time convergence of error dynamics both with and without disturbances and noise.

Hierarchical and Hybrid Model Predictive Control of Quadcopter Air Vehicles

Nowadays, Unmanned Air Vehicles (UAVs) have become more and more important. These vehicles are employed in many applications from military operations to civilian tasks. Under situations where global positioning system (GPS) and inertial navigation system (INS) do not function, or as an additional sensor, vision systems can be used. In recent years omnidirectional camera usage has experienced a remarkable increase. In many fields innovative research has been done by this camera which captures 360° view in a single frame. However, employment of omnidirectional cameras in UAVs is very new. In this paper, we present the use of catadioptric systems in UAVs. The parallel lines that exist on many structures in an urban environment are used by the algorithm for the estimation of the attitude of the UAV. After explanation of the algorithm, the UAV modelling and control will be presented. Various simulations have been done to present the effectiveness of the estimation algorithms as well as the UAV controllers. Finally, we will present the experiments and the results of the estimation and control algorithms on a real model helicopter.

This paper describes the latest efforts to develop an Automated UAV Mission System (AUMS) for small vertical takeoff and landing (VTOL) unmanned air vehicles (UAVs). In certain applications such as force protection, perimeter security, and urban surveillance a VTOL UAV can provide far greater utility than fixed-wing UAVs or ground-based sensors. The VTOL UAV can operate much closer to an object of interest and can provide a hover-and-stare capability to keep its sensors trained on an object, while the fixed wing UAV would be forced into a higher altitude loitering pattern where its sensors would be subject to intermittent blockage by obstacles and terrain. The most significant disadvantage of a VTOL UAV when compared to a fixed-wing UAV is its reduced flight endurance. AUMS addresses this disadvantage by providing forward staging, refueling, and recovery capabilities for the VTOL UAV through a host unmanned ground vehicle (UGV), which serves as a launch/recovery platform and service station. The UGV has sufficient payload capacity to carry UAV fuel for multiple launch, recovery, and refuel iterations. The UGV also provides a highly mobile means of forward deploying a small UAV into hazardous areas unsafe for personnel, such as chemically or biologically contaminated areas. Teaming small UAVs with large UGVs can decrease risk to personnel and expand mission capabilities and effectiveness. There are numerous technical challenges being addressed by these development efforts. Among the challenges is the development and integration of a precision landing system compact and light enough to allow it to be mounted on a small VTOL UAV while providing repeatable landing accuracy to safely land on the AUMS. Another challenge is the design of a UGV-transportable, expandable, self-centering landing pad that contains hardware and safety devices for automatically refueling the UAV. A third challenge is making the design flexible enough to accommodate different types of VTOL UAVs, such as the AAI iSTAR ducted-fan vehicle and small helicopter UAVs. Finally, a common command-and-control architecture which supports the UAV, UGV, and AUMS must be developed and interfaced with these systems to allow fully autonomous collaborative behaviors. Funded by the Joint Robotics Program, AUMS is part of a joint effort with the Air Force Research Laboratory and the Army Missile Research Development and Engineering Command. The objective is to develop and demonstrate UGV-UAV teaming concepts and work with the warfighter to ensure that future upgrades are focused on operational requirements. This paper describes the latest achievements in AUMS development and some of the military program and first responder situations that could benefit from this system.

In this research, time-optimal open-loop and closed-loop guidance laws for MAVs, such as Samarai, were investigated. Open-loop optimal solutions were achieved with a new technique,based on using mathematical methods, such as calculus of variation and the GA-PSO optimization algorithm. This novel method could overcome difficulties of the usual optimal control methods. The TS approach developed closed-loop guidance that dealt with the novel neural-fuzzy training algorithm based on open-loop optimal trajectories to achieve closed-loop guidance. By feed forwarding the optimal control as a nominal optimum trajectory plus deviation, closed-loop optimal control can be generated. On the other hand, (TS) fuzzy logic needs storing nominal optimal trajectories to train the system. As discussed, the responses of the optimal closed-loop guidance were achieved, and the robustness of closed-loop policies against noises and/or disturbances was studied. However, it was difficult to obtain closed-loop solutions when the other methods, such as dynamic programming or analytical methods, were considered. The case mentioned is an important innovative idea for autonomous MAVs, whose results are important for further developments of Samarai monocopter. The results of this work demonstrated that the closed-loop optimal responses based on TS fuzzy logic methods can damp disturbances for minimum time criterion. However, the result of fuzzy logic is perfectly suitable, where higher noises were applied. Therefore, it can be concluded that fuzzy logic was robust and efficient in eliminating perturbations. It is proposed that aerospace engineers, such as primary trajectory designers, use TS fuzzy training guidance because its robustness was guaranteed by this work, especially for MAVs, such as Samarai.

An Unmanned Air Vehicle (UAV) is an unmanned, remotely controlled, small air vehicle. It has an important role in anti-surface warfare. This implies over-the-horizon detection, classification, targeting and battle damage assessment. To perform these tasks several UAVs are needed to assist or interchange with each other. An important problem is to determine how many UAVs are needed in this respect. The answer depends on the characteristics of the UAV and its mission. The UAV availability problem is very complex and the usual method to solve such a problem is simulation. A disadvantage of simulation is that it can be very time-consuming. Hence it is not very suitable for sensitivity analysis. Moreover, since simulation gives mere approximations and is not very generic, theoretical insights are hardly gained. In this paper we show how such a complex problem can still be tackled analytically by using a basic model from reliability theory, viz., a 1-out-of-n system with cold standby, ample repair facility and general life time and repair distributions.

For Unmanned Air Vehicles (UAV) to operate autonomously in unstructured and GPS-denied environments, requisite information about the surroundings with adequate detailing needs to be generated and Simultaneous Localization and Mapping (SLAM) is among the most preferred protocols for fulfilling these needs. This study presents a SLAM-based linear optimal control approach for UAV navigation with limited sensor resources. The suggested protocol utilizes 1-Point RANSAC and an Extended Kalman Filter (EKF) for SLAM from a 6 degree-of-freedom motion monocular image sequence. Output of this research effort includes the estimated camera motion and a sparse map of salient point features through sensory representation. In this study we present a unique combination of 1-Point RANSAC (Random Sample Consensus) in conjunction with EKF with innate focus on reducing of the computational complexity. Contemporary studies by UAV research groups have successfully demonstrated the usefulness of algorithms based on 1-Point RANSAC and also provided comparison of algorithmic results with those of visual odometry. The present study further extends the scope by evaluating the algorithm with data generated from an input device mounted on a custom developed UAV. Employment of SLAM mission for mapping the areas that are prone to mining and land exploration is among the important outcomes of the proposed study.

In the era of Declining Defense Dollars, the cost of sophisticated aircraft and highly trained personnel has heightened interest in Unmanned Air Vehicles (UAVs). The obvious lure is the lower vehicle cost (no crew station and crew support systems) and reduced needs for highly skilled air crews. Reconnaissance (commonly called recce) aircraft and their missions are among the commonly sighted applications for UAVs. Today's UAV recce aircraft (such as the Predator) are the genesis of much more sophisticated UAVs of the future. The evolution of the UAV will not be constrained to recce aircraft, but the recce mission will be significant for UAVs. The recce hole has historically been that of a battlefield data collector for post mission review and planning. In the electronic battlefield of the future, that role will be expanded. Envisioned mission for future recce aircraft include real-time scout, target location and fire coordination, battle damage assessment, and large area surveillance. Associated with many of these new roles is the need to store or assess much higher volumes of data. The higher volume data requirements are the result of higher resolution sensors (the Advanced Helicopter Pilotage infrared sensor has a data rate of near 1.2 Gigabits per second) and multi-sensor applications (the Multi-Sensor Aided Targeting program considered infrared, TV, and radar). The evolution of the UAV recce role, and associated increased data storage needs (from higher data rates and increased coverage requirements), requires the development of new data storage equipment. One solution to the increased storage needs is solid-state memory. As solid-state memories become faster, smaller, and cheaper they will enable the UAV recce mission capability to expand. Because of the speed of the memory, it will be possible to buffer and assess (identify the existence of targets or other points of interest) data before committing to consumption of limited storage assets. Faster memory search times and random memory access will permit comparisons of iterative imagery to identify changes (moving-target-indicator or new/missing objects) in real-time. This has three positive effects: (1) Real-time assessment of enemy troops and weapon strengths (coupled with directional data) for planning or fire coordination. (2) Real-time battle damage assessment to accelerate determination of the allocation of friendly assets. (3) Selective storage of imagery (only that which contains points-of-interest) reduces the amount of data storage leading to increased area coverage and mission times. Orbital Sciences Corporation/Fairchild Defense is completing the development of a High-Speed Solid State Recorder which is a critical element of the Enhanced Recce Management System we are developing. The High-Speed Solid State Recorder will be a major part of defining the mission for future UAV recce aircraft.

The problem of cooperative obstacle avoidance by a group of unmanned ground vehicles (UGVs) and unmanned air vehicles (UAVs) during a typical search mission is addressed in this paper. The group of UAVs and UGVs are performing a search operation in the designated area. All the UAVs and UGVs are equipped with a vision sensor/LIDAR to identify the possible obstacles in the search space. Due to their operation on the ground, UGVs are more likely to encounter obstacles. The obstacle avoidance for UGV under the event of sensor failure is done with environment information from the nearest UAV. The UAV plans its trajectory according to the UGV’s expected future trajectory, leading towards the base station. The UGV replans its trajectory to avoid obstacles after obtaining the information from its nearest UAV. A simulation study is performed with 10 UAVs and five UGVs performing a search mission in 1[Formula: see text]km[Formula: see text][Formula: see text][Formula: see text]1[Formula: see text]km area. The proposed obstacle avoidance method is experimentally validated in the outdoor environment with an autonomous UAV equipped with a camera and an autonomous UGV navigating based on GPS localization and environment information from the UAV.

In this chapter, preliminary sizing and performance calculations of unmanned air vehicles (UAV), with relation to its propulsion system, are explained. Starting with the mission profile of the air vehicle, which is one of the important design drivers, the UAV is sized for the requirements. The requirements in design consist of a set of target values (e.g., payload amount to be carried, range, endurance time, cost, etc.). Additionally, the parameters within mission profile such as cruising altitude and speed of aircraft affect engine type, power level required, fuel quantity, and therefore general dimensions and the gross weight of the aircraft. It is often an iterative process to size the air vehicle and engine together. UAV is designed in a loop of calculations in which sizing, flight performance, and engine performance are done for each phase of the mission or flight profile to satisfy the overall design mission and requirements.

Great efforts are devoted for integrating Renewable Energy Sources (RES) on the propulsion system of Unmanned Air Vehicles (UAVs). This is applicable to small UAVs, having a horizon to expand to large UAVs within the next decades. For the conventional propulsion systems to be replaced, the required power needs of the aircraft should first be examined. The required power depends on the UAV's flight stage from takeoff to landing, whereas its calculation varies with regard to the propulsion system type, which is separated into two main categories, the propelled‐driven engines and the jet engines. Two obstacles are arising. First, the parameters needed for the power estimation are commercially sensitive and second the analysis alters with respect to the engine's type. This paper focuses on the mathematical analysis of the required power for both the main propulsion systems, presented extensively for each flight phase, proposing, at the same time, a parameter estimation method which is applicable to any UAV type. The main purpose of this paper is to act as a guide for the calculation of any type UAV's required power at any flight stage. To validate the analysis, the MQ‐9 Reaper/Predator B, a High Altitude Long Endurance (HALE) turboprop UAV produced by General Atomics is analysed. Its unknown parameters are estimated and based on a selected flight profile, the required power in terms of horsepower from takeoff to landing is assessed, for two scenarios regarding the aircraft's initial weight. In the first scenario the UAV has the maximum gross takeoff weight, whereas in the second scenario it does not carry any payload. The estimated required shaft power per flight phase for each scenario is then marked into the TPE331‐10 turboprop engine characteristic curves.

The work presented concerns the application of value driven engineering design to the Sophomore Design Project of an Unmanned Air Vehicle (UAV) conceptual/preliminary design. In this Sophomore Design Project, offered to the Aviation Program students, student teams performed the conceptual/preliminary stages of design of a small UAV (below 5 kilograms) based on a given set of requirements and constructed the selected UAV design in balsa wood. The student taking this course were divided in groups of 4-5 persons and worked to propose, plan and manufactured an aircraft design while they were supervised by a faculty/lab instructor/lab engineer. The student teams were evaluated on their ability to coordinate efforts to propose the aircraft design criteria, aircraft components, resources, implementation schedule, estimate cost and finally construct the optimal UAV design. A large number of UAV configurations and geometries were considered and evaluated in the Multi-Disciplinary Design Optimization (MDO) in order to search the widest possible design space. Moreover, the designed system’s operational capabilities and acquisition cost, based on the UAV user’s needs and priorities, were employed in the value model to compare the different design alternatives and select the optimal. Following the selection of the flying wing as optimal design, manufacturing of the prototype in balsa wood was performed by the students, with the two student groups constructing this UAV.

Structural health and usage monitoring of unmanned air vehicles is of increasing interest not only for the evaluation of flight loads and the use of data in order to estimate the remaining lifetime of the structure but also for a fast and reliable assessment of structural impacts due to unexpected events that can produce significant loads on the structure, such as hard landings and gusts. This is especially important with composite material structures where damage is quite often barely visible. In-flight load and usage monitoring enables fast turnaround times and increases the availability of the unmanned air vehicle. The Spanish National Institute for Aerospace Technology has been developing, for over two decades now, unmanned air vehicles for surveillance, reconnaissance, and aerial targets. At the moment, a health and usage monitoring system for the Spanish National Institute for Aerospace Technology’s medium altitude and long endurance unmanned air vehicle called MILANO, based on fiber Bragg grating sensors that measure strain and temperature, is being developed. The sensors are integrated in the outer and central wing box and the central and rear fuselage. The health and usage monitoring system is based on the experience gained during flight test activities of the tactical unmanned air vehicle SIVA, an all-composite material unmanned air vehicle developed and assembled by Spanish National Institute for Aerospace Technology. One SIVA is instrumented with 15 fiber Bragg grating sensors that measure both strain and temperature in different structural points of interest, such as the outer wing, the wing attachment, the front and rear fuselage, a stabilizer, and the landing gear. The use of optical sensors instead of electrical sensors has the advantages of lower weight penalty, longer durability, and total immunity to electromagnetic interferences. Structural deformations of the unmanned air vehicle have been measured during flight and used to calculate structural flight loads. Temperature-induced thermal strains have been compensated for. This article explains the results and data evaluation of the performed flight tests, describes the equipment used and the sensor applications, and presents the health and usage monitoring of Spanish National Institute for Aerospace Technology’s tactical and medium altitude and long endurance unmanned air vehicles.