So far, the closeformationcontrol problem has been studied using different methods, such as PI controller , sliding mode control , LQR control , MPC control , adaptive control , and robustcontrol . However, all of them were developed under the leader-follower architecture, and there was no cooperation between two UAVs. The efficacy of the existing methods can only be guaranteed for closeformationflight of two or three aircraft. Increase of formation size (number of UAVs) will result in dramatic loss of efficiency and accuracy for the existing methods. To deal with the deficiency of the existing methods in closeformationflight of more than three UAVs, a cooperativecontrol method is proposed in this paper. A bidirectional communication topology is employed. UAVs in closeformation are required to communicate with some of their neighbors. To enhance the robustness against model uncertainties and formation aerodynamic disturbances, the uncertainty and disturbance estimation technique is employed and combined with the proposed cooperativeformation controller. The efficiency of the proposed control algorithm is verified via the closeformation simulation of five aircraft.
robust multisteady state variable linear time-invariant control system. The results show that the swarm of UAVs can successfully form patterns, switching autonomously between them through a simple parameter switch to the potential, satisfying constraints made regarding each UAV. To further improve the model, several assump- tions made regarding the system will be considered in the future. For example, it was assumed that the communication system between the UAVs was ideal. Future work will consider the implementation of a sensing region surrounding each UAV and its in ﬂ uence on the stability of the swarm. In addition, it was assumed that there were no external perturbations acting on each UAVand all sensors were ideal. The development of a control law to take this into consideration will also be investigated.
The swarm behavior has been used in many simulation across many industries. The swarm behavior could have been used in this simulation as well, but the UAVs in this simulation use the formationflight behavior as mentioned above. The formationflight behavior is the a form which the UAVs maintain throughout the run time of the simulation, by which it means is the UAVs remain in the same formation when moving from point a to point b and then moving from point b to a without losing their formation. According to the paper by Luo, Delin , the UAVs need to keep the formation to be effective as well as be capable enough to change the formation in the times of environmental change etc. and that is why a control strategy is needed to be designed for the transformation of the formation.
The coordinated path-following control problem was implicit in the early work in [ 33 ], where the authors built on and extended the single-vehicle “manoeuvre regulation” approach in [ 45 ], and presented a solution to the problem of coordinated operation of an autonomous surface vehicle and an autonomous underwater vehicle. The strategy adopted, however, requires the vehicles to exchange a large amount of information, and cannot be easily generalized to larger teams of vehicles. These drawbacks were later overcome in [ 60 ], which proposes a leader-follower cooperative approach that (almost) decouples the temporal and spatial assignments of the mission. The solution adopted is rooted in the results on path-following control of a single vehicle presented in [ 95 ], and takes advantage of the fact that, with this path-following algorithm, the speed profile of each vehicle becomes an additional degree of freedom that can be exploited for vehicle coordination. Moreover, in this setup, the two vehicles only need to exchange the (scalar) “along-path positions” of their virtual targets, which reduces drastically the amount of information to be exchanged among vehicles when compared to the solution developed in [ 33 ]. Interestingly, an approach similar to the one in [ 60 ] was proposed at approximately the same time in the work in [ 92 ] and [ 93 ], where a nonlinear control design method was presented for formationcontrol of a fleet of ships. The approach relies on the maneuvering methodology developed in [ 94 ], which is then combined with a centralized guidance system that adjusts the speed profile of each vehicle so as to achieve and maintain the desired formation configuration. The maneuvering strategy in [ 94 ] was also exploited in [ 46 ], where a passivity framework is used to solve the problem of vehicle coordination and formation maneuvering.
The LPV flight controllers are synthesized using single quadratic (SQLF) or parameter- dependent (PDLF) Lyapunov functions where the synthesis problems involve solving the linear matrix inequality (LMI) constraints that can be efficiently solved using standard software. To synthesize an LPV autopilot of a Jindivik UAV, the lon- gitudinal and lateral LPV models are required in which they are derived from a six degree-of-fredoom (6-DOF) nonlinear model of the vehicle using Jacobian lin- earization. However, the derived LPV models are nonlinearly dependent on the time-varying parameters, i.e. speed and altitude. To obtain a finite number of LMIs and avoid the gridding parameter technique, the Tensor-Product (TP) model trans- formation is applied to transform the nonlinearly parameter-dependent LPV model into a TP-type convex polytopic model form. Hence, the gain-scheduled output feedback H ∞ control technique can be applied to the resulting TP convex polytopic
UnmannedAerialVehicles (UAVs) were originally deployed as target drones for combat pilot training but have evolved over time to provide valuable roles in in- telligence, surveillance and reconnaissance for both civilian and military operations. Historically, UAVs were built and operated in ways to avoid interacting with their environment’s at all costs, affording them the ability to quickly and efficiently travel large distances. The ability for aerialvehicles to manipulate or carry objects that they encounter could greatly expand the types of missions achievable by unmannedaerial systems. High degree of freedom (DOF) robots with dexterous arms could lead to transformative applications such as material handling for infrastructure re- pair, law enforcement, disaster response, casualty extraction, and personal assistance leading to a paradigm shift in the way UAVs are deployed as shown in Figure 1.1. Such aerial manipulation systems are referred to as Mobile Manipulating UnmannedAerialVehicles (MM-UAV).
One of the primary design features that incorporates the aircraft into the system is the ability to route the existing transmit (Tx) and receive (Rx) signals from the remote control unit through the HC12. Tx and Rx signals are in the form of pulse width modulated (PWM) outputs for each control surface of the aircraft (elevator, rudder and aileron depending on the aircraft). Essentially, coding for this element of the project was completed after coding was developed for servo motor control and accelerometer PWM decoding (see sections 4.2.1 and 4.3.1 respectively). Tx and Rx routing is basically a combination of these two elements of coding. Basically, the PWM high/low transitions need to be identified and conveyed to the servo outputs. There is no need to record timing information as with the servo control and accelerometer decoding methods as the output required (servo pulses) is identical to the input received (Tx/Rx pulses). Figure 6.1 summarises the
The Regression method was chosen to be used as it was seen to be the easiest to implement, it requires less computational time to process and is more suited for real-time implementation. These properties were found to be sufficient to justify its choice over the previous methods. The regression method, however, is based on the assumption that the measurements that are used to make up the regressor matrix are error free, and thus do not contain noise or biases. This is however is never the case when implementing the method using actual flight data. The estimated parameters, as obtained from the regression method, are shown to be highly dependent on the quality of the measurements.
ROBAIN DE KEYSER received the M.Sc. degree in electromechanical engineering and the Ph.D. degree in control engineering from Ghent Univer- sity, Ghent, Belgium, in 1974 and 1980, respec- tively. He is currently an Emeritus Professor of control engineering with the Faculty of Engineer- ing, Ghent University. He has authored or coau- thored more than 300 publications in journals, books, and conference proceedings. The research is application-driven, with many pilot implementa- tions in technical and nontechnical systems, amongst others in the chemical, steel, marine, mechatronic, semiconductor, power electronics, and biomedi- cal spheres. His current research interests include model predictive control, auto-tuning and adaptive control, modeling and simulation, and system identification. He has acted as an External Review Expert in several European Commission Research Programs and is one of the pioneers who produced the original concepts of predictive control during the 1980s.
On the complexity front, there are many unanswered questions. Our PSPACE-hardness proof crucially depends on the freedom to set flight times and relative deadlines. In , it is claimed that the Euclidean version of the problem, i.e., in which targets can be realised as points in a two-dimensional plane (with discretised distances between points), is NP-complete (with a single UAV). In the view of our result, we would like to investigate whether this claim is indeed true. It is conceivable that the techniques used in the well-known NP-hardness proof of euclidean tsp  might be useful in this regard, but we were unfortunately unable to leverage them in the case at hand.
Object detection has been studied extensively over decades. Most of the promising detec- tors are able to detect objects of interest in clear images, such images are usually captured from ground-based cameras. With the rapid development of technology, UnmannedAerialVehicles (UAVs) equipped with cameras have been increasingly deployed in many industrial applications, opening up a new frontier of computer vision applications in security surveillance, peacekeeping, agriculture, deliveries, aerial photography, disaster assistance [1, 2, 3, 4], etc. One of the core fea- tures of the UAV-based applications is to detect object of interests (pedestrians or vehicles). While it is in high demand, object detection from UAVs is insufficiently investigated. In the meantime, the large mobility of UAV-mounted cameras bring in greater challenges than traditional object de- tection (using surveillance or other ground-based cameras). Some of the UAV-specific nuisances are enumerated below.
considered in  is uncontrollable, even though it is over-actuated compared to a quadrotor he- licopter. Thus, in order to minimize flight performance degradation in the case of motor failure, an octorotor helicopter is a better choice for real applications. Motivated by this, the author mounted extra four motors under the original ones on an existing quadrotor helicopter available at the au- thor’s lab, respectively. Compared to the octorotor helicopter used in  and , the one used in this chapter is more compact, and more suitable for applications in urban and indoor environ- ments. In fact, due to payload and better flight performance requirements for different engineering applications, more and more hexarotor and octorotor helicopters are available on the small UAVs market. Such a development and application trend also provides natural needs and platforms for developing and implementing FTC strategies on these UAVs towards satisfaction of strict safety and reliability demands by US Federal Aviation Administration (FAA) or other country’s licensing & certificating authorities for practical and commercial uses of developed UAVs. With the increase of available redundant actuators, the problem of allocating them to achieve the desired forces and moments becomes non-unique and far more complex. Such redundancy has called for effective control allocation schemes to distribute the required control forces and moments over the available actuators. In particular, in the case of actuator fault/failure, an effective control re-allocation of the remaining healthy actuators is needed to achieve acceptable performance.
measurement called estimatedFlightTime. This measurement is calculated within the FC and relates to the expected amount of time the drone has left to fly safely before depleting its battery pack. The measurements were often inaccurate either by displaying unrealistic total flight times or negative flight times with a half capacity battery pack. This behavior often leads the FC into triggering an internal fail-safe measurement that disables the ability to arm the drone, and is only solvable by rebooting the FC. Several attempts to track the origin of the issue were inconclusive, since several sensors were used, and over time all sensors showed that same behavior. We consider this a cumulative error within the estimation function implemented in dRonin, since other aspects of the sensor metrics, like current voltage and tension seem accurate; this situation leads to the disable of the internal fail-safe trigger of the FC.
In recent years, there has been a surge in research on small unmannedaerialvehicles (UAVs) in news production and news audience engagement. Most of this research has focused on legal, ethical, and regulatory implications of UAVs in newsgathering, while paying less atten- tion to the journalists’ perspectives. To fill this gap in the academic literature, this article explores the ethical principles that guide journalists who use UAVs, how they have worked within these ethical principles, and how they can serve as disruptive innovators. Semi- structured interviews with 13 UAV early adopters reveal that legal and regulatory restraints on UAVs facilitated the emergence of a new form of norm entrepreneur inside journalistic institu- tions. These individuals were able to experiment on the fringes of acceptable practice. In so doing, they seeded their organizations with the skill set and institutional capacity to engage constructively with the use of UAVs once constraints were lifted.
Even though their implementation details may vary drasti- cally, all autopilots share similar functionalities. First, they are all meant to provide some level of autonomous flight. To achieve that, they typically implement a cascade of modules for estimating and controlling angle rate, angle, velocity, and position. Some autopilots accept external references in any of the controllers, but the most common and useful controls for high-level users are velocity, posi- tion, and yaw controls. Another common concept for autop- ilots is the flight mode. Depending on the current state, the task that is being executed, or the set of controllers that are handling the flight, the autopilot declares to be in a defined flight mode. Typically, each mode provides some level of control to the radio control (RC) human pilot (the so-called safety pilot), and at least one of them allows for autono- mous control from an external computer. We generalize and refer to the first set as manual modes, and the last one as auto mode. Moreover, in order to provide complete autonomous flights, autopilots usually implement addi- tional basic maneuvers, such as takeoff and landing.
Figure 68 - HSV convention’s color is decided mainly based on hue value. Saturation decides strength of the color and value decides its brightness . ................................................................................... 115 Figure 69 - Color filter process: First image was the original image, second image was converted to HSV color plane and last one was filtering out non-red pixels. .................................................................... 115 Figure 70 - The testing module: camera and light source was placed in front of a white wall facing each other, light source and camera had a distance of d and shining angle of a. .......................................... 117 Figure 71 - The LED flashlight that was used had three LEDs with different colors: red, blue and green. The LED had a solid lighting mode as well as a flashing mode. ............................................................. 118 Figure 72 - The laser light that was used was a level measurement laser. Its light could reach more than 5 feet distance . ............................................................................................................................... 119 Figure 73 - The Aircrack-ng code used to send deauthentication packets to the bullets. These packets contained random, meaningless bits that should not allow the two bullets to connect with one another. ............................................................................................................................................................ 121 Figure 74 - Box architecture. ................................................................................................................ 125 Figure 75 - The latitude, longitude, and altitude was sent to the base station after the red color was detected. The first line in the above picture represents the client code command. This command must be run in order to communicate with the server. The Beagleboard keeps on sending the GPS information as long as the red color is still being detected. ..................................................................................... 129 Figure 76 - The flight simulation as it appears on the ground control software. This operates in real time and tracks the plane’s orientation and altitude. ................................................................................... 131 Figure 77 - Data window on the Mission Planner software. All important data is shown such as roll, pitch, yaw, latitude, and longitude. ............................................................................................................... 132 Figure 78 - Location of the autopilot. The software uses Google Maps to produce an interactive map showing the location of the board and the direction in which it is facing. The current location is at
In land surveying, a number of conventional devices have been used in producing terrain mapping particularly DTM and DSM. There are such as total station , global positioning system (GPS) , light detection ranging radar (LiDAR) [3-4], manned aircraft [5-6], terrestrial laser scanning (TLS)  and remote sensing [8-9]. However, despite have been benefitted many, these approaches suffer from certain limitations particularly in terms of time consumption, usage and costing. The issue is much more serious in the tropical regions which are known persistently covered with clouds especially during monsoon seasons, making it difficult to capture high-quality images even by using remote sensing satellite technology. Meanwhile, GPS survey requires a lot of time to establish high-density points in the study area. This is because GPS survey method measures discrete point on the surface. Therefore, this method is not practical for projects allocated with limited budget and time . Terrain mapping using LiDAR and manned aircraft are very costly but has low ground resolution and limited time frame hence, rather impractical to be used for low altitude and small area surveying. Recently, UAV has been given a great attention in many applications including terrestrial terrain mapping, mainly, due to its low cost and practicality [10-11]. A UAV is commonly integrated with autopilot technology that enables semi or full autonomous navigation and image acquisition capabilities . The image acquisition capabilities enable Earth terrain to be mapped and modelled to produce orthophoto. Orthophoto is an aerial photograph that has been geometrically rectified with appropriate scale and curvature, which has been considered as a vital element in the field of photogrammetry. Besides orthophoto, images acquired from UAV can also be used to generate Digital Terrain Model (DTM), which is the spatial terrain elevations of bare-earth, DTM can be utilized
We started with a budget of £20,000 for the investment in the aircraft systems, accessories, computer hardware, software, training, permits, insurance and the development of a new website for the Survey Drone Ltd which we established as a separate trading entity. We started with one UAV, the DJI Inspire 1, as it has us an all in one flying platform with the capability to switch cameras for different applications from aerial filming through to survey. Since its inception Survey Drone has grown into a successful company in its own right, carrying out large-scale site surveys in countries such as Africa, Spain, Romania and the UK. We also provide aerial photography services for developers wishing to sell potential or future views from developments, and we are also using the GPS technology for mapping potential views in our LVIA work.
3. Combine the first two strategies. First, quickly reroute all agents in conflict with the no-fly zone(s) using sapf. Second, use mapf to solve conflicts between agents. If time runs out during execution of mapf, at least some path exists for every agent. This helps ensure that even if a no-fly zone is introduced very quickly, paths for all agents around the no-fly zone can likely be found in time. On a side note, if this happens, a collision detection and avoidance algorithm such as GRCA [Bareiss and van den Berg, 2015] can be used to overcome agent conflicts during flight. Collision detection and avoidance algorithms differ from pathfinding algorithms in that they do not plan paths, they simply take short evasive maneuvers in real-time to avoid a collision, only considering agents and obstacles in the close vicinity (e.g. 100 meters for a UAV). After the collision has been avoided, the UAV returns to the original path.
In manual flight mode where the inputs are directly given by a pilot mov- ing the control surfaces, feedback control can be used to improve the handling qualities of the aircraft by shaping the closed loop response. This makes the air- craft easier to fly by damping oscillatory terms and stabilizing possibly unstable modes. In an unmanned aircraft on the other hand, inputs are given in terms of desired path, altitude and velocity. We are not directly interested in the dynami- cal response from the control surfaces, but rather how well the aircraft can follow given reference values. Still, looking at and compensating for the stability and damping of these modes and trying to get a good control surface deflection to state response is a good staring approach for a completely automatic controller. Flightcontrol is to a large extent based on cascade control, where the inner loops are successively closed to attain a desired performance. This requires both system knowledge and experience from the control designer when choosing the structure, and so making or changing an existing controller can become a large and time consuming effort. With increasingly complex flight systems, modern control techniques are becoming more popular, with methods like eigenstructure assignment, LQR, and robustcontrol being among some of the techniques that have been used in aircraft control systems.