Improving the Productivity of UAV Operations Based on Granular Computing and Fuzzy Sets

The number of Unmanned Aerial Vehicles (UAVs) flying around is increasing year after year due to their popularity. This new scenario considerably increases the risk of collision between UAVs, which makes the development of protocols to avoid this issue an emerging topic. This work focuses on the development of a collision avoidance protocol between UAVs performing planned missions. Our proposal is based on the periodic submission of state information, and future position estimates of the UAV, allowing the detection of collision risks. Collision avoidance is achieved by forcing UAVs to stop when they are flying critically near each other, and establishing the priority of the UAV passing by the critical area first; this decision is based on a per-UAV unique identifier. We prove that our solution is robust enough to avoid collisions in all the situations tested; in addition, the overhead introduced by our protocol in terms of additional UAV flight time is low.

In this paper we consider the problem of searching for an arbitrarily smart and fast evader in a large environment with a team of unmanned aerial vehicles (UAVs) while providing guarantees of detection. Our emphasis is on the fast execution of efficient search strategies that minimize the number of UAVs and the search time. We present the first approach for computing fast guaranteed search strategies utilizing additional searchers to speed up the execution time and thereby enabling large scale UAV search. In order to scale to very large environments when using UAVs one would either have to overcome the energy limitations of UAVs or pay the cost of utilizing additional UAVs to speed up the search. Our approach is based on coordinating UAVs on sweep lines, covered by the UAV sensors, that move simultaneously through an environment. We present some simulation results that show a significant reduction in execution time when using multiple UAVs and a demonstration of a real system with three AR.Drone 2.0.

The importance of local intervention capability for the assertion of Canadian Sovereignty in the Northwest Passage is recognized. However, Canada lacks the ability to deploy, on demand, assets to search a wide area for rescue or surveillance purposes in the North. This fact motivated our investigation of the feasibility of a rapid intervention system based on a carrier-scouts design in which a number of unmanned aerial vehicles (UAVs) would be transported, air launched and recovered by a carrier aircraft. These UAVs would have the ability to self-organize in formations that correspond to the task at hand. When searching for a target, they would fly in a linear pattern so that the search area swept per hour and the probability of detecting the target would be considerably increased. A 1973 report by the Tactical Combat Aircraft Programs of the Boeing Aerospace Company for the US Air Force [15] and a 2007 thesis by Chalamont [6] indicate that airborne launch and recovery of many UAVs from a carrier aircraft is feasible and requires only already existing technology. We propose here a solution to the remaining problem of managing simultaneously the many UAVs that are required by the vastness of the areas to be surveyed, with a minimum number of human controllers and communications. Namely, we present algorithms for the self-organization of the UAVs in the required formations. These allow for surveillance operations during which close-up images would be acquired of activities in a region of interest, and searching an area for assets in distress and providing a visual presence for such. We reach the conclusion that our proposed local intervention system with flocking UAVs is feasible and would provide a valuable asset for asserting Canadian Sovereignty in the North.

With the rapid development of aviation technology, especially in intelligent situation awareness, autonomous flight control and electric propulsion, the number of unmanned aerial vehicles (UAVs) has grown explosively, bringing challenges to low-altitude flight safety. Autonomous UAV detect and avoid (DAA) technology is a key future direction. Firstly, this paper focuses on UAV multi-sensor data fusion, proposing an asynchronous trajectory fusion method via comprehensive weighting that accounts for multiple influencing factors. Secondly, for trajectory tracking of ownship and intruders, improved Kalman filter (KF) and enhanced Sage-Husa adaptive unscented Kalman filter (AUKF) algorithms are used for vertical and horizontal tracking to enhance accuracy. Then, a hybrid method combining detect and avoid alerting logic for unmanned aircraft systems (DAIDALUS) with Markov decision process (MDP) is proposed to solve the UAV collision avoidance decision-making problem. It calculates collision avoidance strategies via the state space, takes them as MDP’s action space, sets up reward functions and state transition probabilities to build an MDP model, and explores the influence of discount factors. Finally, through experimental tests, results show the proposed UAV detection and avoidance decision method has a 27.2% efficiency increase compared to standalone DAIDALUS, the loss of well clear (LoWC) rate is only 5.8%, and the nearest distance between the intruder and the ownship is 343 m, meeting the collision avoidance requirements for UAV flight.

To enable safe and efficient operation of a large number of Unmanned Aerial Vehicles (UAVs) as envisioned in future commercial operations, a novel networked-based air traffic control paradigm is needed to provide for autonomous route optimization and cooperation to enable deconfliction. This system would need to ensure deconfliction from nearby manned and unmanned operations and would need the capability to respond to unexpected close-proximity aircraft, known as interlopers. This network-based control paradigm will require that operators be able to oversee a network of cooperative and autonomous UAVs capable of maneuvering at low altitudes. Such a network would need new algorithms and increased autonomy to best support cooperative deconfliction and high level supervisory control, and one that also allows operators the ability to both understand local and global behavior of a network of UAVs. To this end, we propose a paradigm-shifting multi-layer distributed air traffic control concept focused on low altitude, high density UAV operations. This new architecture would include a group of analysts that perform a global analysis of the UAV network at a strategic level and more tactical, local controllers manipulating the UAV network, who are advised by the analysts. The proposed operational support system would include methods for the automatic identification of issues within the network, as well as a method for the automatic distribution of effective tasks to the most suitable personnel. This architecture would be a combination of current air traffic control techniques as well as those troubleshooting techniques found in other domains like network and cloud computing management.

With the development of unmanned platforms, there are large numbers of unmanned aerial vehicles(UAVs), due to UAVs’ new features such as lightweight, high speed mobility and heterogeneous, UAVs are facing great challenges of data security. As a key means to protect data security, cryptography is of great significance. Traditional mature and complete cryptography schemes need to be adjusted to meet the new features of UAVs. We start with the security challenges faced by UAV architecture, then analyze the cryptographic technologies that can be applied. By comparing with existing researching schemes, we shows the advantages of different cryptographic technologies in the security protection of unmanned platforms and looks forward to the future.

UAV (Unmanned aerial vehicle) has become an optimal platform for surveying and mapping applications. One of the most important performances of UAV is flight speed. It is a key factor to impact surveying safety and efficiency within the limited flight endurance. Researchers have not treated UAV's flight speed in much detail, only focus on the image motion in order to limit the maximum flight speed in path. In this paper, an optimal flight speed estimation method is proposed, which is according to the multicopter flight mechanics and aerial photogrammetry theories. Through this method, the energy consumption characteristics of surveying UAV in the field operation can be obtained. A safe and efficient flight speed also can be determined. To verify the feasibility of the method, different test scenarios are carried out in the SITL (Software in the loop) simulation and obtained promising results.

In this paper, a novel framework for power-efficient, cluster- based machine-to-machine (M2M) communications is proposed. In the studied model, a number of unmanned aerial vehicles (UAVs) are used as aerial base stations to collect data from the cluster heads (CHs) of a set of M2M clusters. To minimize the CHs' transmit power while satisfying the rate requirements of M2M devices, an optimal scheduling and resource allocation mechanism for CH-UAV communications is proposed. First, using the queue rate stability concept, the minimum number of UAVs as well as the dwelling time that each UAV must spend for servicing the CHs are computed. Next, the optimal resource allocation for the CH-UAV communication links is determined such that M2M devices rate requirements are satisfied with a minimum transmit power. Simulation results show that, as the packet transmission probability of machines increases, the minimum number of UAVs required to guarantee the queue rate stability of CHs will also significantly increase. Our results also show that, compared to a case with pre-deployed terrestrial base stations, the average transmit power of CHs will decrease by 68% when UAVs are used.

The increase in the number of Unmanned Aerial Vehicles (UAVs) for liquid transport tasks, such as agricultural spraying, results in significant stability challenges. The free movement of the liquid, known as sloshing, generates unpredictable forces that destabilize the vehicle and increase collision risks. This study treats this problem by developing and validating a nonlinear control strategy to ensure precise trajectory tracking while actively suppressing liquid sloshing. The coupled dynamics of the system are modeled using the Euler–Lagrange formalism by representing the UAV as a planar vertical take-off and landing (PVTOL) aircraft and the liquid sloshing dynamics as an equivalent pendulum model. The stability of the entire closed-loop system is proven using Lyapunov’s direct method. The analytical results are validated through numerical simulations in MATLAB/Simulink, which demonstrate excellent tracking of desired altitude and horizontal trajectories. Crucially, the simulations confirm that the controller effectively attenuates the sloshing oscillations, offering a robust solution to enhance the safety and operational performance of UAVs in liquid transport applications.

This article establishes a new multiunmanned aerial vehicle (multi-UAV)-enabled mobile edge computing (MEC) system, where a number of unmanned aerial vehicles (UAVs) are deployed as flying edge clouds for large-scale mobile users. In this system, we need to optimize the deployment of UAVs, by considering their number and locations. At the same time, to provide good services for all mobile users, it is necessary to optimize task scheduling. Specifically, for each mobile user, we need to determine whether its task is executed locally or on a UAV (i.e., offloading decision), and how many resources should be allocated (i.e., resource allocation). This article presents a two-layer optimization method for jointly optimizing the deployment of UAVs and task scheduling, with the aim of minimizing system energy consumption. By analyzing this system, we obtain the following property: the number of UAVs should be as small as possible under the condition that all tasks can be completed. Based on this property, in the upper layer, we propose a differential evolution algorithm with an elimination operator to optimize the deployment of UAVs, in which each individual represents a UAV's location and the entire population represents an entire deployment of UAVs. During the evolution, we first determine the maximum number of UAVs. Subsequently, the elimination operator gradually reduces the number of UAVs until at least one task cannot be executed under delay constraints. This process achieves an adaptive adjustment of the number of UAVs. In the lower layer, based on the given deployment of UAVs, we transform the task scheduling into a 0-1 integer programming problem. Due to the large-scale characteristic of this 0-1 integer programming problem, we propose an efficient greedy algorithm to obtain the near-optimal solution with much less time. The effectiveness of the proposed two-layer optimization method and the established multi-UAV-enabled MEC system is demonstrated on ten instances with up to 1000 mobile users.

This paper addresses the trackability for a fixed-wing unmanned aerial vehicle (UAV) to track a dynamic target. To achieve the tracking task, the UAV needs to fulfill two objectives, i.e., keep synchronous motion with the target, and minimize the relative distance between itself and the target. Thus, to ensure the successful path planning, the UAV dynamics and sensor coverage range must be taken into consideration. By adoption of a geometric approach, systematic path planning algorithms are developed based on the ratios between the speeds of UAV and moving target. Meanwhile, a sufficient condition on sensor range is derived for the target to be non-escape from the UAV field of view (FOV). Finally, an application example is presented to demonstrate the efficacy of the developed algorithms.

Abstract. The Unmanned Aerial Vehicle (UAV) is an emerging technology being adapted for a wide range of applications. Real-time monitoring is essential to enhance the effectiveness of UAV applications. Sensor networks are networks constructed from various sensor nodes. International standard such as OGC's SOS (Sensor Observation Service) makes it possible to share sensor data with other systems as well as to provide accessibility to globally distributed users. In this paper, we propose a system combining UAV technology and sensor network technology to use an UAV as a mobile node of sensor network so that the sensor data from UAV is published and shared real-time. A UAV can extend the observation range of a sensor network to remote areas where it is usually difficult to access such as disaster area. We constructed a UAV system using remote-controlled helicopter and various sensors such as GPS, gyrocompass, laser range finder, Digital camera and Thermometer. Furthermore, we extended the Sensor Observation Service (SOS) and Sensor Service Grid (SSG) to support mobile sensor nodes. Then, we conducted experiments of flying the helicopter over an area of the interest. During the flight, the system measured environmental data using its sensors and captured images of the ground. The data was sent to a SOS node as the ground station via Wi-Fi which was published using SSG to give real- time access to globally distributed users.

In this paper, we propose a Real-Time Immersive Surveillance System with a reduced number of Unmanned Aerial Vehicles (UAVs) required to cover any inaccessible area for video surveillance. To start with, we have developed a twin servo based camera assembly which is capable to move in horizontal and vertical axis (Pan and Tilt) providing us flexibility to change our field of view without physically moving the UAV. The movable assembly for sensor can be controlled in two ways - Auto mode and Movement Based on accelerometer data of mobile phone. The Real Time video feed can be viewed on Big screens as well as on HMDs (Head Mounted Displays) using mobile phones. Furthermore, a holistic view of this area can be created using feed from multiple drones, which provides an immersive experience. The maximum distance between GCS and Anchor UAV have been obtained experimentally based on acceptable packet loss and Quality-of-Experience (QoE) for video streaming. In order to minimise the number of drones to cover the entire area, we have utilised the capability of this movable camera setup. To calculate the area covered in various position of sensor, the concept of Field of View (FOV) of the on-board camera has been used. We have also discussed the necessary limitations of the extent of camera movement and the camera setup.

Recently, the use of unmanned aerial vehicles for both reconnaissance and fire strikes has been increasing. The control of such vehicles and the transmission of telemetry information are carried out using a combined command and telemetry radio link and frequency hopping spread spectrum technology. In such conditions, the task of developing automatic algorithms for detecting and separating radio links for portable radio monitoring devices becomes especially relevant. The aim of this work is to extend the capabilities of portable radio monitoring devices to detect unmanned aerial vehicles’ radio signals in sophisticated electronic environment by automating signal processing. Formally, the research task is to detect and separate several frequency hopping spread spectrum signals in same frequency band. To solve this task, it is first necessary to detect the frequency channels and estimate frequency and, if necessary, temporal and modulation parameters of the signals in these channels. Estimates of signal parameters values are recorded in associative arrays. The paper proposes a scheme of the algorithm for automatic detection and separation of signals with frequency hopping spread spectrum, as well as the structure of the array with the results of frequency band analysis. In the study of developed algorithm, we considered the practically important option of separating radio links with frequency hopping spread spectrum by the spectral width of the frequency element and the value of the signal-to-noise ratio in the frequency channel, since these features can be quite easily distinguished in the frequency domain. The radio links separation was performed by cluster analysis of scatter plots of parameter pairs estimates using a Gaussian mixture model. The developed algorithm can be implemented in existing and prospective portable radio monitoring devices to automatically detect and determine the number of unmanned aerial vehicles by their radio links signals, in particular, under conditions of a priori uncertainty about the values of signal parameters.

This paper proposes a new evolutionary planner to determine the trajectories of several Unmanned Aerial Vehicles (UAVs) and the scan direction of their cameras for minimizing the expected detection time of a nondeterministically moving target of uncertain initial location. To achieve this, the planner can reorient the UAVs cameras and modify the UAVs heading, speed, and height with the purpose of making the UAV reach and the camera observe faster the areas with high probability of target presence. Besides, the planner uses a digital elevation model of the search region to capture its influence on the camera likelihood (changing the footprint dimensions and the probability of detection) and to help the operator to construct the initial belief of target presence and target motion model. The planner also lets the operator include intelligence information in the initial target belief and motion model, in order to let him/her model real-world scenarios systematically. All these characteristics let the planner adapt the UAV trajectories and sensor poses to the requirements of minimum time search operations over real-world scenarios, as the results of the paper, obtained over 3 scenarios built with the modeling aid-tools of the planner, show.