Seamless connectivity with 5G enabled unmanned aerial vehicles base station using machine programming approach

2021 ◽  
Author(s):  
Dilip Mandloi ◽  
Rajeev Arya
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Base Station ◽  
Programming Approach ◽  
Seamless Connectivity ◽  
Aerial Vehicles ◽  
Machine Programming

scholarly journals A Dynamic Topology Construction Algorithm for Self-Organizing Wireless Sensor Networks

2021 ◽  
Author(s):  
Waltenegus Dargie
Keyword(s):  
Wireless Sensor Networks ◽  
Environmental Factors ◽  
Unmanned Aerial Vehicles ◽  
Base Station ◽  
Wireless Sensor ◽  
Topology Construction ◽  
Construction Algorithm ◽  
Aerial Vehicles ◽  
Dynamic Topology ◽  
Self Organizing

<div>Self-organizing protocols and algorithms require knowledge of the underlying topology of the network. The topology can be represented by a graph or an adjacency matrix. In most practical cases, establishing the topology prior to a deployment is not possible because the exact placement of nodes and the existence of a reliable link between any two individual nodes cannot guaranteed. Therefore, this task has to be carried out after deployment. If the network is stand-alone and certain aspects are fixed (such as the identity of the base station, the size of the network, etc.), the task is achievable. If, however, the network has to interact with other systems -- such as Unmanned Aerial Vehicles (UAVs) or mobile robots -- whose operation is affected by environmental factors, the task can be difficult to achieve. In this paper we propose a dynamic topology construction algorithm, assuming that the network is a part of a joint deployment and does not have a fixed based station.</div>

scholarly journals Control System Architecture for Automatic Recovery of Fixed-Wing Unmanned Aerial Vehicles in a Moving Arrest System

2021 ◽  
Vol 103 (4) ◽  
Author(s):  
Kristoffer Gryte ◽  
Martin L. Sollie ◽  
Tor Arne Johansen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Low Cost ◽  
Field Testing ◽  
Satellite System ◽  
Base Station ◽  
Research Literature ◽  
Aerial Vehicles ◽  
Global Navigation Satellite ◽  
The Right ◽  
Automatic Recovery

AbstractAutomatic recovery is an important step in enabling fully autonomous missions using fixed-wing unmanned aerial vehicles (UAVs) operating from ships or other moving platforms. However, automatic recovery in moving arrest systems is only briefly studied in the research literature, and is not yet an option when using low-cost, commercial off-the-shelf (COTS) autopilots. Acknowledging the reliability and low cost of COTS avionics, this paper adds recovery functionality as a modular extension based on non-intrusive additions to an autopilot with very general assumptions on its interface. This is achieved by line-of-sight guidance, which sends an augmented desired position to the autopilot, to ensure line-following along a virtual runway that guides the UAV into the arrest system. The translation and rotation of this line is determined by the pose of the arrest system, determined using two Global Navigation Satellite System (GNSS) receivers, where one is configured as a Real-Time Kinematic (RTK) base station. The relative position of the UAV and arrest system is also precisely estimated using RTK GNSS. Through extensive field testing, on two different fixed-wing UAVs, the system has shown its performance and reliability; 43 recovery attempts in a stationary net hit 0.01 ± 0.25m to the right and 0.07 ± 0.20m below the target in calm conditions. Further, 15 recoveries in a barge-mounted, ship-towed net hit 0.06 ± 0.53m to the right and 0.98 ± 0.27m below the target in winds up to 4 m/s. The remaining error is largely systematic, caused by communication delays, and could be reduced with more integral effect or through direct compensation.

Towards a Predictive Analysis of UAV-Based Flying Base Station Decisions

2020 ◽  
Vol 16 (2) ◽  
pp. 20-52 ◽  
Author(s):  
Hamid Garmani ◽  
Driss Ait Omar ◽  
Mohamed El Amrani ◽  
Mohamed Baslam ◽  
Mostafa Jourhmane
Keyword(s):  
Nash Equilibrium ◽  
Unmanned Aerial Vehicles ◽  
Base Station ◽  
Base Stations ◽  
Practical Significance ◽  
Game Model ◽  
Activity Scheduling ◽  
Area Of Interest ◽  
Aerial Vehicles ◽  
Fairness Concern

The use of unmanned aerial vehicles (UAVs) as a communication platform is of great practical significance in the wireless communications field. This paper studies the activity scheduling of unmanned aerial vehicles acting as aerial base stations in an area of interest for a specific period. Specifically, competition among multiple UAVs is explored, and a game model for the competition is developed. The Nash equilibrium of the game model is then analyzed. Based on the analysis, an algorithm for Nash equilibrium computation is proposed. Then, a game model with fairness concern is established, and its equilibrium price is also analyzed. In addition, numerical examples are conducted to determine the factors that affect the strategies (price, quality of service, and beaconing duration) of the UAV and to show how the expected profits of UAVs change with that fairness concern point. The authors believe that this research paper will shed light on the application of UAV as a flying base station.

Minimizing the number of wireless charging PAD for unmanned aerial vehicle–based wireless rechargeable sensor networks

2021 ◽  
Vol 17 (12) ◽  
pp. 155014772110559
Author(s):  
Yingjue Chen ◽  
Yingnan Gu ◽  
Panfeng Li ◽  
Feng Lin
Keyword(s):  
Sensor Networks ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Large Scale ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Charging ◽  
Aerial Vehicles ◽  
Wireless Rechargeable Sensor Networks ◽  
Aerial Vehicle

In wireless rechargeable sensor networks, most researchers address energy scarcity by introducing one or multiple ground mobile vehicles to recharge energy-hungry sensor nodes. The charging efficiency is limited by the moving speed of ground chargers and rough environments, especially in large-scale or challenging scenarios. To address the limitations, researchers consider replacing ground mobile chargers with lightweight unmanned aerial vehicles to support large-scale scenarios because of the unmanned aerial vehicle moving at a higher speed without geographical limitation. Moreover, multiple automatic landing wireless charging PADs are deployed to recharge unmanned aerial vehicles automatically. In this work, we investigate the problem of introducing the minimal number of PADs in unmanned aerial vehicle–based wireless rechargeable sensor networks. We propose a novel PAD deployment scheme named clustering-with-double-constraints and disks-shift-combining that can adapt to arbitrary locations of the base station, arbitrary geographic distributions of sensor nodes, and arbitrary sizes of network areas. In the proposed scheme, we first obtain an initial PAD deployment solution by clustering nodes in geographic locations. Then, we propose a center shift combining algorithm to optimize this solution by shifting the location of PADs and attempting to merge the adjacent PADs. The simulation results show that compared to existing algorithms, our scheme can charge the network with fewer PADs.

scholarly journals Hybrid Direction of Arrival Precoding for Multiple Unmanned Aerial Vehicles Aided Non-Orthogonal Multiple Access in 6G Networks

2022 ◽  
Vol 12 (2) ◽  
pp. 895
Author(s):  
Laura Pierucci
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Multiple Access ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Base Station ◽  
Flying Height ◽  
Maximization Problem ◽  
Aerial Vehicles ◽  
Sum Rate ◽  
Hybrid Beamforming

Unmanned aerial vehicles (UAV) have attracted increasing attention in acting as a relay for effectively improving the coverage and data rate of wireless systems, and according to this vision, they will be integrated in the future sixth generation (6G) cellular network. Non-orthogonal multiple access (NOMA) and mmWave band are planned to support ubiquitous connectivity towards a massive number of users in the 6G and Internet of Things (IOT) contexts. Unfortunately, the wireless terrestrial link between the end-users and the base station (BS) can suffer severe blockage conditions. Instead, UAV relaying can establish a line-of-sight (LoS) connection with high probability due to its flying height. The present paper focuses on a multi-UAV network which supports an uplink (UL) NOMA cellular system. In particular, by operating in the mmWave band, hybrid beamforming architecture is adopted. The MUltiple SIgnal Classification (MUSIC) spectral estimation method is considered at the hybrid beamforming to detect the different direction of arrival (DoA) of each UAV. We newly design the sum-rate maximization problem of the UAV-aided NOMA 6G network specifically for the uplink mmWave transmission. Numerical results point out the better behavior obtained by the use of UAV relays and the MUSIC DoA estimation in the Hybrid mmWave beamforming in terms of achievable sum-rate in comparison to UL NOMA connections without the help of a UAV network.

scholarly journals Real-Time Autonomous Obstacle Avoidance For Unmanned Aerial Vehicles

2021 ◽  
Author(s):  
Min Prasad Adhikari
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Convex Programming ◽  
Obstacle Avoidance ◽  
Kinematic Model ◽  
Trajectory Generation ◽  
Computation Time ◽  
Programming Approach ◽  
Sequential Convex Programming ◽  
Aerial Vehicles

<div>In this dissertation, methods for real-time trajectory generation and autonomous obstacle avoidance for fixed-wing and quad-rotor unmanned aerial vehicles (UAV) are studied. A key challenge for such trajectory generation is the high computation time required to plan a new path to safely maneuver around obstacles instantaneously. Therefore, methods for rapid generation of obstacle avoidance trajectory are explored. The high computation time is a result of the computationally intensive algorithms used to generate trajectories for real-time object avoidance. Recent studies have shown that custom solvers have been developed that are able to solve the problem with a lower computation time however these designs are limited to small sized problems or are proprietary. Additionally, for a swarm problem, which is an area of high interest, as the number of agents increases the problem size increases and in turn creates further computational challenges. A solution to these challenges will allow for UAVs to be used in autonomous missions robust to environmental uncertainties.</div><div><br></div><div>In this study, a trajectory generation problem posed as an optimal control problem is solved using a sequential convex programming approach; a nonlinear programming algorithm, for which custom solver is used. First, a method for feasible trajectory generation for fast-paced obstacle-rich environments is presented for the case of fixed-wing UAVs. Next, a problem of trajectory generation for fixed-wing and quad-rotor UAVs is defined such that starting from an initial state a UAV moves forward along the direction of flight while avoiding obstacles and remaining close to a reference path. The problem is solved within the framework of finite-horizon model predictive control. Finally, the problem of trajectory generation is extended to a swarm of quad-rotors where each UAV in a swarm has a reference path to fly along. Utilizing a centralized approach, a swarm scenario with moving targets is studied in two different cases in an attempt to lower the solution time; the first, solve the entire swarm problem at once, and the second, solve iteratively for a UAV in the swarm while considering trajectories of other UAVs as fixed.</div><div><br></div><div>Results show that a feasible trajectory for a fixed-wing UAV can be obtained within tens of milliseconds. Moreover, the obtained feasible trajectories can be used as initial guesses to the optimal solvers to speed up the solution of optimal trajectories. The methods explored demonstrated the ability for rapid feasible trajectory generation allowing for safe obstacle avoidance, which may be used in the case an optimal trajectory solution is not available. A comparative study between a dynamic and a kinematic model shows that the dynamic model provides better trajectories including aggressive trajectories around obstacles compared to the kinematic counterpart for fixed-wing UAVs, despite having approximately the same computational demands. Whereas, for the case of quad-rotor UAVs, the kinematic model takes almost half the solution time than with a reduced dynamic model, despite having approximately the similar range of values for the cost function. When extended to a swarm, solving the problem for each UAV is four to seven times computationally cheaper than solving the swarm as a whole. With the improved computation time for trajectory generation for a swarm of quad-rotors using centralized approach, the problem is now reasonably scalable, which opens up the possibility to increase the number of agents in a swarm using high-end computing machines for real-time applications. Overall, a custom solver jointly with a sequential convex programming approach solves an optimization problem in a low computation time.</div>

scholarly journals Real-Time Autonomous Obstacle Avoidance For Unmanned Aerial Vehicles

2021 ◽  
Author(s):  
Min Prasad Adhikari
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Convex Programming ◽  
Obstacle Avoidance ◽  
Kinematic Model ◽  
Trajectory Generation ◽  
Computation Time ◽  
Programming Approach ◽  
Sequential Convex Programming ◽  
Aerial Vehicles

<div>In this dissertation, methods for real-time trajectory generation and autonomous obstacle avoidance for fixed-wing and quad-rotor unmanned aerial vehicles (UAV) are studied. A key challenge for such trajectory generation is the high computation time required to plan a new path to safely maneuver around obstacles instantaneously. Therefore, methods for rapid generation of obstacle avoidance trajectory are explored. The high computation time is a result of the computationally intensive algorithms used to generate trajectories for real-time object avoidance. Recent studies have shown that custom solvers have been developed that are able to solve the problem with a lower computation time however these designs are limited to small sized problems or are proprietary. Additionally, for a swarm problem, which is an area of high interest, as the number of agents increases the problem size increases and in turn creates further computational challenges. A solution to these challenges will allow for UAVs to be used in autonomous missions robust to environmental uncertainties.</div><div><br></div><div>In this study, a trajectory generation problem posed as an optimal control problem is solved using a sequential convex programming approach; a nonlinear programming algorithm, for which custom solver is used. First, a method for feasible trajectory generation for fast-paced obstacle-rich environments is presented for the case of fixed-wing UAVs. Next, a problem of trajectory generation for fixed-wing and quad-rotor UAVs is defined such that starting from an initial state a UAV moves forward along the direction of flight while avoiding obstacles and remaining close to a reference path. The problem is solved within the framework of finite-horizon model predictive control. Finally, the problem of trajectory generation is extended to a swarm of quad-rotors where each UAV in a swarm has a reference path to fly along. Utilizing a centralized approach, a swarm scenario with moving targets is studied in two different cases in an attempt to lower the solution time; the first, solve the entire swarm problem at once, and the second, solve iteratively for a UAV in the swarm while considering trajectories of other UAVs as fixed.</div><div><br></div><div>Results show that a feasible trajectory for a fixed-wing UAV can be obtained within tens of milliseconds. Moreover, the obtained feasible trajectories can be used as initial guesses to the optimal solvers to speed up the solution of optimal trajectories. The methods explored demonstrated the ability for rapid feasible trajectory generation allowing for safe obstacle avoidance, which may be used in the case an optimal trajectory solution is not available. A comparative study between a dynamic and a kinematic model shows that the dynamic model provides better trajectories including aggressive trajectories around obstacles compared to the kinematic counterpart for fixed-wing UAVs, despite having approximately the same computational demands. Whereas, for the case of quad-rotor UAVs, the kinematic model takes almost half the solution time than with a reduced dynamic model, despite having approximately the similar range of values for the cost function. When extended to a swarm, solving the problem for each UAV is four to seven times computationally cheaper than solving the swarm as a whole. With the improved computation time for trajectory generation for a swarm of quad-rotors using centralized approach, the problem is now reasonably scalable, which opens up the possibility to increase the number of agents in a swarm using high-end computing machines for real-time applications. Overall, a custom solver jointly with a sequential convex programming approach solves an optimization problem in a low computation time.</div>

scholarly journals Optimization of Base Location and Patrol Routes for Unmanned Aerial Vehicles in Border Intelligence, Surveillance and Reconnaissance

2018 ◽  
Author(s):  
Yao Liu ◽  
Zhong Liu ◽  
Jianmai Shi ◽  
Guohua Wu ◽  
Chao Chen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Base Station ◽  
Base Stations ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Solution Approach ◽  
Location Routing ◽  
Aerial Vehicles

The location routing problem of unmanned aerial vehicles (UAV) in border patrol for intelligence, surveillance and reconnaissance is investigated, where the location of UAV base stations and the UAV flying routes for visiting the targets in border area are jointly optimized. The capacity of the base station and the endurance of the UAV are considered. A binary integer programming model is developed to formulate the problem, and two heuristic algorithms combined with local search strategies are designed for solving the problem. The experiment design for simulating the distribution of stations and targets in border is proposed for generating random test instances. Also, an example based on the Sino-Vietnamese border is presented to illustrate the problem and the solution approach. The performance of the two algorithms are analyzed and compared through randomly generated instances.

scholarly journals Multiple Unmanned Aerial Vehicles Deployment and User Pairing for Non-Orthogonal Multiple Access Schemes

2020 ◽  
Author(s):  
Jie Wang ◽  
Miao Liu ◽  
Jinlong Sun ◽  
Guan Gui ◽  
Haris Gacanin ◽  
...  
Keyword(s):  
Wireless Communications ◽  
Unmanned Aerial Vehicles ◽  
Message Passing ◽  
Multiple Access ◽  
Base Station ◽  
Spectrum Efficiency ◽  
User Pairing ◽  
Aerial Vehicles ◽  
Multiple Uavs ◽  
Multiple Unmanned Aerial Vehicles

Non-orthogonal multiple access (NOMA) significantly improves the connectivity opportunities and enhances the spectrum efficiency (SE) in the fifth generation and beyond (B5G) wireless communications. Meanwhile, emerging B5G services demand of higher SE in the NOMA based wireless communications. However, traditional ground-to-ground (G2G) communications are hard to satisfy these demands, especially for the cellular uplinks. To solve these challenges, this paper proposes a multiple unmanned aerial vehicles (UAVs) aided uplink NOMA method. In detail, multiple hovering UAVs relay data for a part of ground users (GUs) and share the sub-channels with the left GUs that communicate with the base station (BS) directly. Furthermore, this paper proposes a K-means clustering based UAV deployment and location based user pairing scheme to optimize the transceiver association for the multiple UAVs aided NOMA uplinks. Finally, a sum power minimization based resource allocation problem is formulated with the lowest quality of service (QoS) constraints. We solve it with the message-passing algorithm and evaluate the superior performances of the proposed scheduling and paring schemes on SE and energy efficiency (EE). Extensive experiments are conducted to compare the performances of the proposed schemes with those of the single UAV aided NOMA uplinks, G2G based NOMA uplinks, and the proposed multiple UAVs aided uplinks with a random UAV deployment. Simulation results demonstrate that the proposed multiple UAVs deployment and user pairing based NOMA scheme significantly improves the EE and the SE of the cellular uplinks at the cost of only a little relaying power consumption of the UAVs.

scholarly journals Optimization of Base Location and Patrol Routes for Unmanned Aerial Vehicles in Border Intelligence, Surveillance and Reconnaissance

2019 ◽  
Author(s):  
Yao Liu ◽  
Zhong Liu ◽  
Jianmai Shi ◽  
Guohua Wu ◽  
Chao Chen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Base Station ◽  
Base Stations ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Solution Approach ◽  
Location Routing ◽  
Aerial Vehicles

The location routing problem of unmanned aerial vehicles (UAV) in border patrol for intelligence, surveillance and reconnaissance is investigated, where the location of UAV base stations and the UAV flying routes for visiting the targets in border area are jointly optimized. The capacity of the base station and the endurance of the UAV are considered. A binary integer programming model is developed to formulate the problem, and two heuristic algorithms combined with local search strategies are designed for solving the problem. The experiment design for simulating the distribution of stations and targets in border is proposed for generating random test instances. Also, an example based on the Sino-Vietnamese border is presented to illustrate the problem and the solution approach. The performance of the two algorithms are analyzed and compared through randomly generated instances.

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