UAV Trajectory Research Articles

Combined MPC and Dynamic Neural Network-Based UAVs Trajectory Tracking Control

Combined MPC and Dynamic Neural Network-Based UAVs Trajectory Tracking Control

FlyingLoRa: Towards energy efficient data collection in UAV-assisted LoRa networks

Energy efficiency is an increasingly crucial consideration due to the battery-powered end devices in LoRa networks. With the adoption of chirp spread spectrum modulation, end devices far apart from the gateway have to use a high spreading factor and transmit power to send uplink packets, which causes longer transmission time and higher energy consumption compared to the end devices near the gateway, and further causes unfairness issues on energy efficiency. To tackle this problem, we investigate a novel energy-efficient data collection scheme named FlyingLoRa, where an unmanned aerial vehicle carrying one gateway is dispatched to harvest packets from end devices. The goal of FlyingLoRa is to minimize the energy consumption of end devices for packet transmission by jointly optimizing the 3D UAV trajectory, scheduling strategies, and transmission parameters of end devices. We formulate our design as a mixed-integer non-convex optimization problem and propose an efficient iterative algorithm to find a sub-optimal solution. The proposed approach is numerically simulated and the results show that FlyingLoRa improves energy efficiency by 16.13× on average compared with the existing fixed gateway schemes. In addition, we realize FlyingLoRa in real scenarios and evaluate its performance by presenting the actual packet reception rate (PRR) and transmission energy consumption, which shows its effectiveness.

Delay-Tolerant UAV-Assisted Communication: Online Trajectory Design and User Association

UAV-assisted communication is a promising paradigm to enhance the performance of wireless networks. In this paper, we consider the network randomness in the delay-tolerant UAV-assisted communication systems. We aim to optimize both the UAV trajectory and user association in an online manner to achieve a finite queueing delay at the users' buffers with the minimum energy consumption of the UAVs. We first formulate our problem as a stochastic dynamic programming problem that integrates the future expected energy consumption and delay into its objective function and constraints, respectively. To deal with the inaccurate knowledge of future user demand and mobility, we apply the Lyapunov optimization technique and convert it into a series of online joint UAV trajectory and user association optimization problems. However, each online problem is a non-convex non-linear integer programming problem, which is very challenging to solve in general. As such, we first eliminate the coupling relationship of user association variables in each online problem. By exploiting the special graph structure of the problem, we can decompose the joint problem into inner optimization and outer optimization problems. Then, we propose the online UAV mobility and user association selection (UMUAS) scheme, which includes two steps to solve the inner and outer problems. Simulation results show that our online UMUAS scheme achieves the near-optimal performance of the energy-delay tradeoff, which also has a 44.2% improvement over the greedy scheme and a 51.3% improvement over the interference-free scheme. Moreover, it achieves the highest energy efficiency among the three benchmark schemes.

UAV Trajectory Optimization for Joint Relay Communication and Image Surveillance

This work examines the use of image surveillance UAVs for relay communication between ground users and a remote base station (BS). UAVs take aerial images of the surveillance region and forward them to the BS while serving the uplink transmission demands of ground users. We first consider the single-UAV scenario and jointly determine the UAV’s trajectory, task assignment, user association, and rate allocation by maximizing the sum-log-throughput of the users subject to constraints on the surveillance coverage, image transmission requirements, and relay capacity. The resulting mixed-integer nonlinear programming problem is solved by an inexact block coordinate descent (BCD) algorithm where we inherit ideas from the exact penalty method for mathematical programming with equilibrium constraints to relax the integer constraints and the successive convex approximation approach to address the non-convexity of the trajectory optimization problem. Then, we extend the proposed framework to the case with multiple UAVs that are dispatched to cover a wide surveillance region. The UAVs may complete both relay and surveillance tasks more efficiently through cooperation and proper task allocation for UAVs. A similar BCD algorithm is adopted to solve the problem. Numerical simulations are provided to demonstrate the effectiveness of the proposed scheme over several baseline methods.

UAV trajectory planning based on an improved sparrow optimization algorithm with multi-strategy integration

Real-time monitoring of urban high-altitude data is an important goal in the construction and development of smart cities today. However, with the development of modern cities, the monitoring space becomes complicated and narrow because of the different building heights and no-fly zones, which makes UAV trajectory planning more difficult. In this paper, a multi-strategy sparrow search algorithm (MSSA) is proposed to solve the UAV trajectory planning problem in a three-dimensional environment. The algorithm aims to minimize the flight distance and maximize the use efficiency of the UAV. First, the improved algorithm employed a reverse-learning strategy based on the law of refraction to improve the search range and enhance the optimization performance. Second, we introduced a random step size generated by Levy flight into the position update strategy of the participant. The algorithm accuracy and speed of convergence were improved by the randomness feature. Finally, the algorithm incorporated the Cauchy mutation to improve the scout position, which enhanced its ability to jump out of the local optimum of the algorithm. Sixteen benchmark test functions, Wilcoxon rank sum test, and 30 CEC2014 test function optimization results demonstrated that MSSA had better optimization accuracy, convergence speed, and robustness than the comparison algorithms. In addition, the proposed algorithm was applied to the UAV trajectory planning problem in different complex 3D environments. The results confirmed that the MSSA outperformed the other algorithms in complex 3D trajectory planning problems.

UAV trajectory planning based on bi-directional APF-RRT* algorithm with goal-biased

In recent decades, RRT* algorithm has attracted much attention because of its asymptotic optimization. However, the RRT* algorithm still suffers from slow convergence rate and large randomness of search range. To overcome the shortcomings of this algorithm, this paper proposes UAV trajectory planning based on bi-directional APF-RRT* algorithm with goal-biased. Firstly, goal-biased strategy is used to guide the generation of random sampling points, and two mutually alternating random search trees are established by the bi-directional RRT* algorithm to perform the search, thus increasing the convergence rate of the algorithm. Secondly, the number of iterations is greatly reduced by incorporating an modified artificial potential field method into the bi-directional growth tree. In the process of smoothing the paths, a cubic spline interpolation algorithm is applied to optimize the paths to obtain the best trajectory. The combination of the two algorithms improves the direction of new node generation and reduces the path cost. Finally, the algorithm of this paper is compared with Informed-RRT*, Bi-RRT* and improved P-RRT* algorithms, and it enhances the search performance of the growing tree.

MUTAA: An online trajectory optimization and task scheduling for UAV-aided edge computing

A novel UAV-aided edge computing system is proposed in this work, where UAV-aided edge nodes are dispatched to provide communication and computation assistance for completing tasks generated by ground clients (GCs). We formulate a trajectory design and task allocation problem (TDTAP), aiming at maximizing the sum of completed tasks of GCs by optimizing the proper trajectory for each UAV and scheduling tasks from each GC. It is impossible to solve the TDTAP problem directly in polynomial time since UAVs lack all GCs’ information, e.g., position and amount of tasks. To this end, we put forward an online iterative algorithm named Maximum UAV trajectory and Task Allocation Algorithm (MUTAA) to solve the TDTAP problem by jointly optimizing UAVs’ trajectory and GCs’ task scheduling. Unlike existing algorithms, MUTAA can make real-time decisions for each UAV without acquiring information from all GCs in advance. During each iteration, MUTAA consists of two sub-algorithms: (1) trajectory design algorithm TDA and (2) task allocation strategy TAS. Specifically, the preschedule step is used in TDA to find the proper trajectory for UAVs, and a competitive online algorithm, TAS, is proposed to schedule GCs’ tasks. Theoretical analysis proves that TAS is e/(e−1)-competitive, that is, it processes (e−1)/e (approximately 63%) tasks when compared with the optimal offline solution. Experimental results demonstrate that MUTAA completes 83% data on an average of the optimal offline solution, illustrating that the proposed algorithm MUTAA can be widely used in time-sensitive scenarios.

Improving Dual-UAV Aided Ground-UAV Bi-Directional Communication Security: Joint UAV Trajectory and Transmit Power Optimization

This paper investigates a dual-unmanned aerial vehicle (UAV) aided communication system to improve the security of the communication between ground devices and UAVs. Different from the existing works which ignored ground devices mobility and just considered one-way communication security between ground devices and UAVs, we allow the devices to be mobile and consider bi-directional ground-UAV communication security. Specifically, one UAV server communicates with mobile ground devices, and the other UAV jammer is invoked to confuse eavesdroppers. Our objective is to maximize the worst-case average secrecy rate by the joint optimization of UAV trajectory and sender transmit power. To achieve it, we first formulate the worst-case average secrecy rate maximization problem as a constrained Markov decision process (CMDP) under the constraints of UAV flight space, flight speed, energy capacity, anti-collision, and peak transmit power. Then, we design a Deep Deterministic Policy Gradient (DDPG) based algorithm to solve the CMDP. Experiment results demonstrate that our joint optimization scheme can enhance the communication security in terms of the secrecy rate in both UAV-to-ground (U2G) case and ground-to-UAV (G2U) case. Besides, it is observed that UAV trajectory and sender transmit power have different impacts on the communication security in U2G case and G2U case.

End-to-end UAV obstacle avoidance decision based on deep reinforcement learning

Aiming at the problem that the traditional UAV obstacle avoidance algorithm needs to build offline three-dimensional maps, discontinuous speed control and limited speed direction selection, we study the end-to-end obstacle avoidance decision method of UAV continuous action output based on DDPG(deep deterministic policy gradient) deep reinforcement learning algorithm. Firstly, an end-to-end decision control model based on DDPG algorithm is established. The model can output continuous control variables, namely UAV obstacle avoidance actions, according to the continuous state information perceived. Secondly, the training verification is carried out on the platform of UE4 + Airsim. The results show that the model can realize the end-to-end UAV obstacle avoidance decision. Finally, the 3DVFH(three dimensional vector field histogram) obstacle avoidance algorithm model with the same data source is compared and analyzed. The experiment shows that DDPG algorithm has better optimization effect on the obstacle avoidance trajectory of UAV.

Toward Energy-Efficient UAV-Assisted Wireless Networks Using an Artificial Intelligence Approach

This article studies the application of artificial intelligence (AI) approach in UAV-assisted wireless networks to cope with a large number of parameters impacting energy-efficiency in the sixth generation wireless network. In order to improve the energy efficiency for UAV-assisted wireless networks, we focus on the following three aspects: the UAVs trajectory planning; caching, computing, and communication resource allocation of UAVs; and 3D hovering location decision of UAVs. We discuss each aspect and reveal the corresponding optimization problem of energy efficiency. We also explore several promising deep-learning-based AI methods, which include pointer network, federated deep learning, and multi-agent deep deterministic policy gradient, to solve these optimization problems. Through case studies, we verify the superiority of the proposed AI methods to save UAVs' energy and decrease the system delay.

Array design for mm-wave UAV-assisted cooperative communications with simultaneous wireless information and power transmission

High performance UAV-assisted communications system using simultaneous wireless information and power transmission (SWIPT) in mm-wave band is presented. UAV is a moving relay powered from a ground source through a power-splitting mechanism. In mm-wave band we utilize antenna array to increase the antenna gain while keeping array size small and practical. The radiation pattern of the UAV antenna is continuously adjusted to peak towards the source and destination. Two array geometries, a line and a cross, are designed for UAV antenna. We achieve near optimal pattern, utilizing innovative low power switches instead of phase shifters which are high power consuming components and using them here defies the purpose. We maximize the end-to-end cooperative throughput by optimizing the UAV power profile, power-splitting ratio profile, antenna weights (0, 1), and UAV trajectory for amplify-and-forward (AF) protocol. We consider two cases. Case1: UAV transmits and receives data simultaneously along two predefined trajectories. The antenna weights for line and cross arrays are optimized utilizing genetic algorithm. The power profile and, power-splitting ratio profile are also optimized using the penalty method. Case2: UAV accumulates the data and power along an optimal trajectory until it reaches the vicinity of target, when it transmits data at high bit rate. Here we define the optimization of parameters mentioned in Case1, while at an optimal point along the trajectory, as sub-problem1, and finding the next optimal point as sub-problem2. Sub-problem1 is solved using the genetic algorithm and dual decomposition method. Sub-problem2 is then solved using successive concave optimization. The overall problem, i.e. cooperative throughput, is solved by reciprocal iteration over the two sub problems. The simulation results show the proposed mm-wave band cross array antenna and switches can overcome the high frequency propagation losses, hence, achieving higher power harvest and data rates. The achieved higher data throughput outperforms the conventional single antenna low frequency systems.

Energy Consumption and Sustainable Services in Intelligent Reflecting Surface and Unmanned Aerial Vehicles-Assisted MEC System for Large-Scale Internet of Things Devices

The aim of this article is to save the energy consumption and tasks’ completion time of multi-unmanned aerial vehicle (UAV) by jointly optimizing the trajectories of UAVs and passive phase shifts of intelligent reflecting surface (IRS)s. By applying this approach, the system will lead to complex optimization problem as it has several optimization subproblems like optimization of deployment of stop points (SPs), the optimization of association among Internet of Things devices (IoTDs), SPs, and UAVs, the passive phase shifts of IRSs, and the trajectories planning of UAVs. A trajectory planning and passive beamforming algorithm with variable population size (TPPBA-VP) is proposed to tackle the complex optimization problem via four phases taking into accounts all the above optimization concerns. Simulation results demonstrate that the proposed TPPBA-VP is superior than the compared algorithms in terms of reducing the sum of the energy consumption and tasks’ completion time of UAVs.

Integrated planning of UAV 3D trajectory based on ant colony algorithm

To address the problem of UAV trajectory planning optimality, a dynamic multi-objective optimal path model is established based on satisfying the basic conditions of UAV flight constraints, taking into account the time window restricted area and radio interference threat, etc. comprehensively. Based on the characteristics of the complex environment, an ant colony algorithm with reduced adaptive step size is constructed based on the theory of ant colony algorithm to quickly plan the best safe UAV trajectory in the complex dynamic environment. Simulation results show that the model can meet the complex airspace conditions of UAV missions, while the algorithm can find shorter trajectories and shorten the search time.

UAV-Assisted Data Collection for Dynamic and Heterogeneous Wireless Sensor Networks

To consider the heterogeneity of sensor nodes (SNs) and further protect the SNs with low residual energy, in this letter, we propose an energy-saving UAV-assisted data collection framework for a heterogeneous wireless sensor network (WSN), where the working mode of each SN is dynamically determined by its residual energy and a certain energy threshold. Our aim is to minimize the WSN’s energy consumption by jointly optimizing the energy threshold, SNs’ working modes and cluster head (CH) selection, while ensuring successful data transmission and satisfying UAV trajectory length constraint. To address it, we propose a one-dimensional search method to find the optimal energy threshold, and leverage a sequential rank-one constraint relaxation algorithm to select the CHs. Numerical results justify that the performance of our proposed approach is more energy-saving than the existing benchmarks.

Algorithm of ant colony optimization (ACO) for 3D variation traveling salesman problem

The article discusses the solution of the spatial traveling salesman problem (TSP 3D variation) using Ant Colony Optimization (ACO). The traveling salesman problem considers n bridges and a matrix of pairwise distances between them. It is necessary to find such an order of visiting cities so that the total distance traveled was minimal, each city was visited exactly once and the salesman returned to the city from which he began his route. In the TSP 3D variation problem, each “city” has 3 coordinates x, y, z. The analysis of the main methods of solving, in particular, the metaheuristic algorithms to which ACO belongs, is performed. At each iteration of these methods, a new solution of the problem is built, which is based not on one, but several solutions of the population. The ACO uses an idea that is based on collecting statistical information about the best solutions. The program code is implemented in MATLAB. During computational experiments, various network topologies were randomly generated, and the number of iterations at which the optimal cycle was achieved was recorded. The execution time of the code for the TSP 3D task is almost the same as the execution time of TSP 2D. The results can be used for spatial tasks of the salesman (TSP 3D-variation), which arise in the process of 3D printing, planning UAV trajectories (UAV) in mountain conditions or multi-story urban development, road planning in multi-story buildings.

무선 전력 전송을 통한 IoT 기기 에너지 공급을 위한 UAV Trajectory 및 Beamwidth 최적화

본 논문에서는 다수의 임무 지역 내에 많은 Internet-of-Things (IoT) 기기가 배치되어 있고 자체 임무를 수행하기 위해 에너지가 필요한 환경에서, unmanned aerial vehicle (UAV)의 무선 전력 전송 기술을 통해 IoT 기기에 필요한 에너지를 공급하는 알고리즘을 개발한다. 확률적 line-of-sight (LoS) 기반 air-to-ground (A2G) 무선 채널모델 하에서 모든 IoT 기기에 필요한 에너지를 공급하는데 필요한 총 소요 시간이 최소화될 수 있도록 UAV의 beamwidth와 비행경로 (trajectory)를 최적화하는 것을 목표로 한다. 이를 위해, 먼저 각 임무 지역에서의 에너지 공급을 위한 UAV의 최적의 에너지 공급 위치를 구하고, 이어서 이를 이용하여 최적의 UAV 비행경로를 찾는 dynamic programming (DP) 기반의 알고리즘을 제안한다.

Mass and 3D centroid test and error analysis of small UAV

Mass and center of mass are important mass characteristic parameters, and these parameters have a direct impact on the space motion attitude, motion trajectory, and flight stability of the rotor UAV. In this paper, the multi-point weighing method is used to measure the mass and three-dimensional center of mass of the rotary-wing UAV, the measurement principle is deeply analyzed and the system error is deeply studied. It can provide theoretical guidance for the development of test equipment for the mass and three-dimensional center of mass of the rotor UAV.

Fair Downlink Communications for RIS-UAV Enabled Mobile Vehicles

This letter considers the sub-6GHz downlink communications from base station (BS) to mobile vehicles (MVs), where reconfigurable intelligent surface enabled unmanned aerial vehicle (RIS-UAV) serves as a mobile passive relay. To achieve communication fairness, we aim to maximize the minimum throughput among all the MVs via jointly optimizing RIS passive beamforming, MV scheduling, UAV trajectory and power allocation. The formulated problem is a mixed-integer non-convex optimization problem which is intractable to solve. Hence, the original problem is divided into three subproblems, joint passive beamforming and MV scheduling optimization, power allocation optimization, and trajectory optimization, which can be solved by the successive convex approximation (SCA) method. An alternating iterative optimization algorithm of the three subproblems is proposed to obtain the suboptimal solutions. Numerical results demonstrate the remarkable throughput gain of the proposed algorithm against other benchmarks.

Trajectory Optimization and Power Allocation Scheme Based on DRL in Energy Efficient UAV‐Aided Communication Networks

With flexibility, convenience and mobility, unmanned aerial vehicles (UAVS) can provide wireless communication networks with lower costs, easier deployment, higher network scalability and larger coverage. This paper proposes the deep deterministic policy gradient algorithm to jointly optimize the power allocation and flight trajectory of UAV with constrained effective energy to maximize the downlink throughput to ground users. To validate the proposed algorithm, we compare with the random algorithm, Q-learning algorithm and deep Q network algorithm. The simulation results show that the proposed algorithm can effectively improve the communication quality and significantly extend the service time of UAV. In addition, the downlink throughput increases with the number of ground users.

A Secure Downlink Transmission Scheme for a UAV-Assisted Edge Network

As an extension of centralized cloud services in a space-air-ground integrated network, an unmanned aerial vehicle (UAV-)-enabled edge network brings computation as close ground terminals of need as possible. In this scenario, the UAV is considered a mobile base station (MBS) to achieve data forwarding and processing from cloud servers to terminals. Note that downlink signals from the MBS to ground terminals are vulnerable to passive wiretapping by a malicious node. We formulate an average secrecy rate maximization problem to tackle the wiretapping issue by simultaneously transmitting confidential information and artificial noise (AN). We decompose the problem into two subproblems, including the UAV transmit power allocation subproblem and the UAV trajectory subproblem. Then, we adopt the successive convex approximation scheme and the alternating optimization method to develop our iterative algorithm to achieve secure transmission. Simulation results demonstrate that the proposed scheme is significantly better than other benchmarks in UAV-enabled downlink communication secrecy performance.