Journal articles: 'Intelligent UAV'

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Zhou, Bao Yu, and Li Tian. "Designing Highly Intelligent Unmanned Aerial Vehicle-Borne Software." Applied Mechanics and Materials 241-244 (December 2012): 2722–27. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2722.

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The existing software borne by an unmanned aerial vehicle (UAV) is mostly made up of functional modules with a simple design for meeting its functional requirements, thus being unable for the UAV to meet the requirements for its software to be intelligent, integrated and credible. To meet the requirements for the new-type UAV to be functionally varied, highly autonomous and intelligent, we construct the intelligent database and the network-adaptive and real-time middleware so as to greatly enhance the intelligence of the UAV-borne software and the autonomous flight capability of the UAV, paving a firm foundation for enhancing the functional diversity, autonomy and intelligence of the future advanced UAV.

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Wang, Changyu, Weili Yu, Jinrong Lu, Fusheng Zhu, Lihua Fan, and Shengping Li. "UAV-Based Physical-Layer Intelligent Technologies for 5G-Enabled Internet of Things: A Survey." Wireless Communications and Mobile Computing 2022 (January 28, 2022): 1–5. http://dx.doi.org/10.1155/2022/4351518.

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In recent years, the utilization and application of unmanned aerial vehicle (UAV) have attracted much attention, both from academy and industry. UAVs have been widely used in many practical communication scenarios, due to its high flexibility, high mobility, and low cost. Therefore, this paper addresses the key technologies of UAV communication and reviews the current research status, from various aspects including UAV communication transmission, UAV formation control and networking, UAV resource allocation, and intelligent communication from artificial intelligence algorithms. Then, artificial intelligence is introduced into multiple aspects of UAV communication, including channel transmission, control and networking, and resource scheduling, to organically integrate artificial intelligence into UAV communication, which can help reduce the complexity of communication algorithms and improve system spectrum efficiency. This paper will help improve the efficiency of UAV communication and promote the development of UAV-related industries.

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Yin, Wenqiang, Ran An, and Qun Zhao. "Intelligent Recognition of UAV Pilot Training Actions Based on Dynamic Bayesian Network." Journal of Physics: Conference Series 2281, no. 1 (June 1, 2022): 012014. http://dx.doi.org/10.1088/1742-6596/2281/1/012014.

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Abstract The recognition of UAV pilot training actions using artificial intelligence methods can effectively improve the intelligence level of UAV pilot training ability assessment. Based on the flight characteristics of UAV systems, this paper constructs an intelligent recognition model of flight training actions based on dynamic Bayesian network. Combined with the training tasks, the model recognition effect was verified. The results show that the model can accurately recognize different flight phases and typical training actions of UAV, have the advantages of high recognition accuracy and good real-time performance, and effectively improve the training effect of pilots.

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Voloshyn, Denys, and Serhii Bulba. "Intelligent UAV Spoofing Detection Method." Advanced Information Systems 6, no. 1 (April 6, 2022): 88–96. http://dx.doi.org/10.20998/2522-9052.2022.1.15.

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The paper presents an intelligent method for detecting UAV spoofing. A distinctive feature of the method is the use of subtrajectory calculation technology based on visual odometry subtrajectories and GPS positions in a sliding window, taking into account the intelligent estimation of the optical flow and the formation of UAV “Ego-movement” descriptors. In the course of the study, an analysis and comparative studies of a wide range of UAV spoofing methods were carried out, the most frequently recommended and practically used methods were identified. The conclusion is made about the relevance of the problems of GPS spoofing. The analysis of methods of protection against UAV GPS spoofing has been carried out. Promising directions for intelligent detection of UAV spoofing using methods and means of visual odometry are identified. In the course of studying methods for fixing input data, an approach was proposed for estimating the optical flow using a sliding window. At the same time, the need for intelligent processing of input data is argued. The estimation of the optical flow and the formation of descriptors was carried out using recurrent convolutional neural networks. As a result, a block diagram of the UAV spoofing detection method was developed. This allowed us to study the developed method. The results of the experiment for two spoofing scenarios showed the efficiency of estimating the positions of at least two of the three indicators under the conditions of using sliding windows of size 15 or more, with a time delay of half the window size.

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Qu, Haoming, Hongkui Yan, Bo Sun, and Weijia Niu. "The study of Simulation Intelligent Analysis System of UAV Control On the basis of Computer Big Data Technology." Highlights in Science, Engineering and Technology 12 (August 26, 2022): 181–86. http://dx.doi.org/10.54097/hset.v12i.1452.

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With the advent of the era of computer big data, the application of big data is increasing. Computer big data technology is suitable for the analysis of UAV simulation intelligent system, which can clarify its practicability. After the intelligent analysis system is put into use, the accuracy of UAV control is improved. The UAV flight control system is the core part of UAV, and the performance of UAV depends on the design of its flight control system to a great degree. In this paper, the intelligent analysis system of UAV control simulation is studied. The accuracy of massive data set algorithm with adaptive selection and adjustment strategy and the complex causal relationship between data in the intelligent analysis system of UAV control simulation under computer big data technology are studied. The research results show that the global optimization of the whole process can be ensured by combining the structural characteristics and algorithms of data-to-data association algorithm, and the data is more than 58.96%. Therefore, the intelligent analysis system of airborne software simulation, which provides guarantee for the research and development of UAV control through testing, has a certain market application space and prospect.

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Ya-Ping Li, Ya-Ping Li, and Shang-Cai Chi Ya-Ping Li. "Research on Community Intelligent Logistics UAV Scheduling Based on Intelligent Optimization Algorithm." 電腦學刊 33, no. 6 (December 2022): 061–71. http://dx.doi.org/10.53106/199115992022123306005.

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"The last mile" is a key problem that needs to be solved urgently by major e-commerce companies, and also an important means to improve their competitiveness in e-commerce activities. In this paper, aiming at the problems existing in the current community intelligent logistics, from the perspective of the entire logistics system, combined with the actual distribution of logistics sites and distribution methods, we studied the scheduling problem of UAVs using different strategies when returning to the site after distribution. First, the UAV distribution time model is established, and then the model is solved with the minimum completion time of goods distribution as the goal. Finally, the feasibility of the algorithm is verified through simulation experiments, and the optimal scheduling strategy of UAV is given in combination with the actual operation, thus guiding e-commerce to build a reasonable intelligent logistics system.

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Baktayan, Asrar Ahmed, and Ibrahim Ahmed Al-Baltah. "A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions." Sustainable Engineering and Innovation 4, no. 2 (December 16, 2022): 156–90. http://dx.doi.org/10.37868/sei.v4i2.id179.

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The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network.

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Huang, Zishan. "UAV Intelligent Control Based on Machine Vision and Multiagent Decision-Making." Advances in Multimedia 2022 (May 27, 2022): 1–11. http://dx.doi.org/10.1155/2022/8908122.

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In order to improve the effect of UAV intelligent control, this paper will improve machine vision technology. Moreover, this paper adds scale information on the basis of the LSD algorithm, uses the multiline segment standard to merge these candidate line segments for intelligent recognition, and uses the LSD detection algorithm to improve the operating efficiency of the UAV control system and reduce the computational complexity. In addition, this paper combines machine vision technology and multiagent decision-making technology for UAV intelligent control and builds an intelligent control system, which uses intelligent machine vision technology for recognition and multiagent decision-making technology for motion control. The research results show that the UAV intelligent control system based on machine vision and multiagent decision-making proposed in this paper can achieve reliable control of UAVs and improve the work efficiency of UAVs.

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Park, Ki-Won, Hyeon Min Kim, and Oh-Soon Shin. "A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications." Energies 15, no. 14 (July 15, 2022): 5143. http://dx.doi.org/10.3390/en15145143.

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Both the unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS) are attracting growing attention as enabling technologies for future wireless networks. In particular, IRS-assisted UAV communication, which incorporates IRSs into UAV communications, is emerging to overcome the limitations and problems of UAV communications and improve the system performance. This article aims to provide a comprehensive survey on IRS-assisted UAV communications. We first present six representative scenarios that integrate IRSs and UAVs according to the installation point of IRSs and the role of UAVs. Then, we introduce and discuss the technical features of the state-of-the-art relevant works on IRS-assisted UAV communications systems from the perspective of the main performance criteria, i.e., spectral efficiency, energy efficiency, security, etc. We also introduce machine learning algorithms adopted in the previous works. Finally, we highlight technical issues and research challenges that need to be addressed to realize IRS-assisted UAV communications systems.

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Zhang, Mingze, Mohammed EI-Hajjar, and Soon Xin Ng. "Intelligent Caching in UAV-Aided Networks." IEEE Transactions on Vehicular Technology 71, no. 1 (January 2022): 739–52. http://dx.doi.org/10.1109/tvt.2021.3125396.

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Zhou, Huan, Yintong Li, and Tong Han. "Anticollision Decision and Control of UAV Swarm Based on Intelligent Cognitive Game." Computational Intelligence and Neuroscience 2022 (August 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/6398039.

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UAV swarm anticollision system is very important to improve the flight safety of the whole swarm formation, while the existing system design methods are still insufficient in realizing autonomous and cooperative anticollision. Based on the cognitive game theory, an intelligent decision-making and control method for UAV swarm anticollision is designed. Firstly, by using the idea of swarm intelligence, basic flight behaviors of UAV swarm are defined as five basic flight rules, such as cohesion, following, self-guidance, dispersion, and alliance. Further, the cognitive security domain of UAV swarm is constructed by setting the overall anticollision rules of the swarm and the anticollision rules of individual members. On this basis, the anticollision problem of UAV swarm is transformed into a game problem involving two parties, and the solution method of decision and control strategy set is proposed. Finally, the stability of anticollision decision and control method is proved through eigenvalue theory. The simulation results show that the method proposed in this paper can effectively realize the autonomous cooperative anticollision of UAV swarm and also has good algorithm real-time solution ability while ensuring flight safety.

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Yue, Guang, and Yutian Pan. "Intelligent Inspection of Marine Disasters Based on UAV Intelligent Vision." Journal of Coastal Research 93, sp1 (September 23, 2019): 410. http://dx.doi.org/10.2112/si93-054.1.

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Wei, Yu, Ying Tang, Tian Zou, Xue Li, Wei Zhao, Honggen Zhou, and Jinfeng Liu. "The Design of Intelligent Spray Painting System for Ship Panel Based on UAV." Journal of Physics: Conference Series 2200, no. 1 (February 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2200/1/012007.

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Abstract This paper proposes a new form of using UAVs to carry spray painting process equipment to spray outer plates of ships, which solves the paining points of poor practicality, poor versatility and low intelligence of existing robotic spray painting and has received wide attention from experts in the industry. It includes two types of hardware equipment and three software systems, among which the hardware equipment includes intelligent spray painting platform and material supply platform, and the software system includes intelligent planning system of spray painting path, intelligent spray painting stability control system and “double platform” dynamic joint control system. Through experimental verification, the intelligent spray painting system of ship outer plate based on UAV proposed in this paper realizes intelligent spray painting of ship outer plate, improves safety, economy and efficiency of ship outer plate spray painting, and further promotes intelligent transformation of ship construction and high-quality development of ship industry.

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Liu, Jia, Jianjian Xiang, Yongjun Jin, Renhua Liu, Jining Yan, and Lizhe Wang. "Boost Precision Agriculture with Unmanned Aerial Vehicle Remote Sensing and Edge Intelligence: A Survey." Remote Sensing 13, no. 21 (October 30, 2021): 4387. http://dx.doi.org/10.3390/rs13214387.

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In recent years unmanned aerial vehicles (UAVs) have emerged as a popular and cost-effective technology to capture high spatial and temporal resolution remote sensing (RS) images for a wide range of precision agriculture applications, which can help reduce costs and environmental impacts by providing detailed agricultural information to optimize field practices. Furthermore, deep learning (DL) has been successfully applied in agricultural applications such as weed detection, crop pest and disease detection, etc. as an intelligent tool. However, most DL-based methods place high computation, memory and network demands on resources. Cloud computing can increase processing efficiency with high scalability and low cost, but results in high latency and great pressure on the network bandwidth. The emerging of edge intelligence, although still in the early stages, provides a promising solution for artificial intelligence (AI) applications on intelligent edge devices at the edge of the network close to data sources. These devices are with built-in processors enabling onboard analytics or AI (e.g., UAVs and Internet of Things gateways). Therefore, in this paper, a comprehensive survey on the latest developments of precision agriculture with UAV RS and edge intelligence is conducted for the first time. The major insights observed are as follows: (a) in terms of UAV systems, small or light, fixed-wing or industrial rotor-wing UAVs are widely used in precision agriculture; (b) sensors on UAVs can provide multi-source datasets, and there are only a few public UAV dataset for intelligent precision agriculture, mainly from RGB sensors and a few from multispectral and hyperspectral sensors; (c) DL-based UAV RS methods can be categorized into classification, object detection and segmentation tasks, and convolutional neural network and recurrent neural network are the mostly common used network architectures; (d) cloud computing is a common solution to UAV RS data processing, while edge computing brings the computing close to data sources; (e) edge intelligence is the convergence of artificial intelligence and edge computing, in which model compression especially parameter pruning and quantization is the most important and widely used technique at present, and typical edge resources include central processing units, graphics processing units and field programmable gate arrays.

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Cheng, Xuchang. "Research on the Application of Computer Vision Technology in Power System UAV Line Inspection." E3S Web of Conferences 358 (2022): 01030. http://dx.doi.org/10.1051/e3sconf/202235801030.

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The stability of transmission lines is one of the important factors to ensure stable power supply. In this paper, a set of UAV intelligent power line inspection flight control system is designed according to the special flight requirements of UAV in power line inspection operation based on the principle of photogrammetry. The key technologies of the system include UAV flight attitude control, high-precision time synchronization between multiple sensors, defect detection and intelligent diagnosis, airborne sensor calibration, wireless communication, and ground data processing. In this paper, a camera and an on-board computer are innovatively used for real-time image processing and pattern recognition for wire feature acquisition. At the same time, this paper proposes an improved Canny edge detection algorithm combined with image segmentation technology to achieve wire feature extraction and identification. The field test results show that the system can complete the remote control of the UAV and realize the intelligent navigation and inspection of the transmission line UAV.

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El-Diwiny, Marwa A., and Abou-Hashema M. El-Sayed. "New Intelligent Control of Auto-self Defensive Unmanned Aerial Vehicle." International Journal of Computers and Communications 15 (November 24, 2021): 23–26. http://dx.doi.org/10.46300/91013.2021.15.5.

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This paper handle a new terminology of the field of the unmanned aerial vehicle is Auto-self defensive UAV, by using new configuration of x-band Doppler surface distribution. The aim of this research is to make UAV can avoid any directed missiles at small time delay before the collision. The intelligent control will process the data of the hypothetical missiles by using supercomputing and send it to the inertial navigation system to correct the path of UAV every sample time against the missile. The goal is to make intelligent UAV that can maneuver the missile and never collide with it.

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VLADAREANU, Victor. "DEVELOPMENT OF INTELLIGENT ALGORITHMS FOR UAV PLANNING AND CONTROL." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 18, no. 1 (June 24, 2016): 221–26. http://dx.doi.org/10.19062/2247-3173.2016.18.1.29.

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Yue, Longfei, Rennong Yang, Ying Zhang, Lixin Yu, and Zhuangzhuang Wang. "Deep Reinforcement Learning for UAV Intelligent Mission Planning." Complexity 2022 (March 31, 2022): 1–13. http://dx.doi.org/10.1155/2022/3551508.

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Rapid and precise air operation mission planning is a key technology in unmanned aerial vehicles (UAVs) autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning (DRL) is proposed to solve the shortcomings of the traditional intelligent optimization algorithm, such as relying on simple, static, low-dimensional scenarios, and poor scalability. Specifically, the suppression of enemy air defense (SEAD) mission planning is described as a sequential decision-making problem and formalized as a Markov decision process (MDP). Then, the SEAD intelligent planning model based on the proximal policy optimization (PPO) algorithm is established and a general intelligent planning architecture is proposed. Furthermore, three policy training tricks, i.e., domain randomization, maximizing policy entropy, and underlying network parameter sharing, are introduced to improve the learning performance and generalizability of PPO. Experiments results show that the model in this work is efficient and stable, and can be adapted to the unknown continuous high-dimensional environment. It can be concluded that the UAV intelligent mission planning model based on DRL has powerful intelligent planning performance, and provides a new idea for researching UAV autonomy.

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Guan, Hongcan, Xiliang Sun, Yanjun Su, Tianyu Hu, Haitao Wang, Heping Wang, Chigang Peng, and Qinghua Guo. "UAV-lidar aids automatic intelligent powerline inspection." International Journal of Electrical Power & Energy Systems 130 (September 2021): 106987. http://dx.doi.org/10.1016/j.ijepes.2021.106987.

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Singh, Simran, Abhaykumar Kumbhar, İsmail Güvenç, and Mihail L. Sichitiu. "Intelligent Interference Management in UAV-Based HetNets." Telecom 2, no. 4 (November 24, 2021): 472–88. http://dx.doi.org/10.3390/telecom2040027.

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Unmanned aerial vehicles (UAVs) can play a key role in meeting certain demands of cellular networks. UAVs can be used not only as user equipment (UE) in cellular networks but also as mobile base stations (BSs) wherein they can either augment conventional BSs by adapting their position to serve the changing traffic and connectivity demands or temporarily replace BSs that are damaged due to natural disasters. The flexibility of UAVs allows them to provide coverage to UEs in hot-spots, at cell-edges, in coverage holes, or regions with scarce cellular infrastructure. In this work, we study how UAV locations and other cellular parameters may be optimized in such scenarios to maximize the spectral efficiency (SE) of the network. We compare the performance of machine learning (ML) techniques with conventional optimization approaches. We found that, on an average, a double deep Q learning approach can achieve 93.46% of the optimal median SE and 95.83% of the optimal mean SE. A simple greedy approach, which tunes the parameters of each BS and UAV independently, performed very well in all the cases that we tested. These computationally efficient approaches can be utilized to enhance the network performance in existing cellular networks.

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黄, 心雨. "Intelligent UAV Path Planning in Emergency Rescue." Journal of Aerospace Science and Technology 09, no. 02 (2021): 44–50. http://dx.doi.org/10.12677/jast.2021.92005.

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Chao, Lijun, Zhi Xiong, Jianye Liu, Chuang Yang, and Yudi Chen. "A brain-inspired localization system for the UAV based on navigation cells." Aircraft Engineering and Aerospace Technology 93, no. 7 (August 9, 2021): 1221–28. http://dx.doi.org/10.1108/aeat-09-2020-0194.

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Purpose To solve problems of low intelligence and poor robustness of traditional navigation systems, the purpose of this paper is to propose a brain-inspired localization method of the unmanned aerial vehicle (UAV). Design/methodology/approach First, the yaw angle of the UAV is obtained by modeling head direction cells with one-dimension continuous attractor neural network (1 D-CANN) and then inputs into 3D grid cells. After that, the motion information of the UAV is encoded as the firing of 3 D grid cells using 3 D-CANN. Finally, the current position of the UAV can be decoded from the neuron firing through the period-adic method. Findings Simulation results suggest that continuous yaw and position information can be generated from the conjunctive model of head direction cells and grid cells. Originality/value The proposed period-adic cell decoding method can provide a UAV with the 3 D position, which is more intelligent and robust than traditional navigation methods.

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An, Qing, Qiqi Hu, Ruoli Tang, and Lang Rao. "Intelligent Scheduling Methodology for UAV Swarm Remote Sensing in Distributed Photovoltaic Array Maintenance." Sensors 22, no. 12 (June 13, 2022): 4467. http://dx.doi.org/10.3390/s22124467.

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In recent years, the unmanned aerial vehicle (UAV) remote sensing technology has been widely used in the planning, design and maintenance of urban distributed photovoltaic arrays (UDPA). However, the existing studies rarely concern the UAV swarm scheduling problem when applied to remoting sensing in UDPA maintenance. In this study, a novel scheduling model and algorithm for UAV swarm remote sensing in UDPA maintenance are developed. Firstly, the UAV swarm scheduling tasks in UDPA maintenance are described as a large-scale global optimization (LSGO) problem, in which the constraints are defined as penalty functions. Secondly, an adaptive multiple variable-grouping optimization strategy including adaptive random grouping, UAV grouping and task grouping is developed. Finally, a novel evolutionary algorithm, namely cooperatively coevolving particle swarm optimization with adaptive multiple variable-grouping and context vector crossover/mutation strategies (CCPSO-mg-cvcm), is developed in order to effectively optimize the aforementioned UAV swarm scheduling model. The results of the case study show that the developed CCPSO-mg-cvcm significantly outperforms the existing algorithms, and the UAV swarm remote sensing in large-scale UDPA maintenance can be optimally scheduled by the developed methodology.

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Xuefeng Chen, Xuefeng Chen, Wan Tang Xuefeng Chen, Ximin Yang Wan Tang, Lingyun Zhou Ximin Yang, and Liuhuan Li Lingyun Zhou. "PSO-VFA: A Hybrid Intelligent Algorithm for Coverage Optimization of UAV-Mounted Base Stations." 網際網路技術學刊 23, no. 3 (May 2022): 487–95. http://dx.doi.org/10.53106/160792642022052303007.

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When the number of outdoor wireless users surges and fixed base stations (BSs) can hardly accommodate high-load communication traffic, unmanned aerial vehicles (UAVs) carrying BSs can provide wireless communication services, and the location deployment of the UAV-mounted BSs directly influences the reliability of network communications. For the target area scenario where the UAVs uniformly cover user nodes, we propose a hybrid intelligent coverage algorithm called PSO-VFA to optimize the coverage of a fixed number of UAV-BSs. The PSO-VFA algorithm consists of two phases employing different intelligent algorithms. First, we adopt a particle swarm optimization (PSO) method for a global search of the coverage areas. Then, for local search, a virtual-repulsive-force-based firefly algorithm (VFA) is proposed in this paper to maximize the user coverage. In the VFA algorithm, the users are treated as the objects attracting the UAVs, and the virtual repulsive force is used for UAV location adjustment. Simulation results show that the proposed PSO-VFA hybrid algorithm has faster convergence and significantly increases the communication coverage of UAV-mounted BSs compared with individual intelligent algorithms such as VFA, PSO, genetic algorithm (GA), and simulated annealing (SA).

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Cheng, Tianhao, Buhong Wang, Zhen Wang, Kunrui Cao, Runze Dong, and Jiang Weng. "Intelligent Reflecting Surface Assisted Secure Transmission in UAV-MIMO Communication Systems." Entropy 24, no. 11 (November 4, 2022): 1605. http://dx.doi.org/10.3390/e24111605.

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This paper studies the intelligent reflecting surface (IRS) assisted secure transmission in unmanned aerial vehicle (UAV) communication systems, where the UAV base station, the legitimate receiver, and the malicious eavesdropper in the system are all equipped with multiple antennas. By deploying an IRS on the facade of a building, the UAV base station can be assisted to realize the secure transmission in this multiple-input multiple-output (MIMO) system. In order to maximize the secrecy rate (SR), the transmit precoding (TPC) matrix, artificial noise (AN) matrix, IRS phase shift matrix, and UAV position are jointly optimized subject to the constraints of transmit power limit, unit modulus of IRS phase shift, and maximum moving distance of UAV. Since the problem is non-convex, an alternating optimization (AO) algorithm is proposed to solve it. Specifically, the TPC matrix and AN covariance matrix are derived by the Lagrange dual method. The alternating direction method of multipliers (ADMM), majorization-minimization (MM), and Riemannian manifold gradient (RCG) algorithms are presented, respectively, to solve the IRS phase shift matrix, and then the performance of the three algorithms is compared. Based on the proportional integral (PI) control theory, a secrecy rate gradient (SRG) algorithm is proposed to iteratively search for the UAV position by following the direction of the secrecy rate gradient. The theoretic analysis and simulation results show that our proposed AO algorithm has a good convergence performance and can increase the SR by 40.5% compared with the method without IRS assistance.

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Huang, Xinyu, Yunzhe Tian, Yifei He, Endong Tong, Wenjia Niu, Chenyang Li, Jiqiang Liu, and Liang Chang. "Exposing Spoofing Attack on Flocking-Based Unmanned Aerial Vehicle Cluster: A Threat to Swarm Intelligence." Security and Communication Networks 2020 (December 9, 2020): 1–15. http://dx.doi.org/10.1155/2020/8889122.

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With the rapid development of wireless communication technology and intelligent mobile devices, unmanned aerial vehicle (UAV) cluster is becoming increasingly popular in both civilian and military applications. Recently, a swarm intelligence-based UAV cluster study, aiming to enable efficient and autonomous collaboration, has drawn lots of interest. However, new security problems may be introduced with such swarm intelligence. In this work, we perform the first detailed security analysis to a kind of flocking-based UAV cluster with 5 policies, an upgrade version of the well-known Boids model. Targeting a realistic threat in a source-to-destination flying task, we design a data spoofing strategy and further perform complete vulnerability analysis. We reveal that such design and implementation are highly vulnerable. After breaking through the authentication of ad hoc on-demand distance vector (AODV) routing protocol by rushing attack, an attacker can masquerade as the first-arrival UAV within a specific scope of destination and generate data spoofing of arrival status to the following UAVs, so as to interfere with their normal flying paths of destination arrival and cause unexpected arrival delays amid urgent tasks. Experiments with detailed analysis from the 5-UAV cluster to the 10-UAV cluster are conducted to show specific feature composition-based attack effect and corresponding average delay. We also discuss promising defense suggestions leveraging the insights from our analysis.

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ud Din, Adnan Fayyaz, Imran Mir, Faiza Gul, Suleman Mir, Nasir Saeed, Turke Althobaiti, Syed Manzar Abbas, and Laith Abualigah. "Deep Reinforcement Learning for Integrated Non-Linear Control of Autonomous UAVs." Processes 10, no. 7 (July 1, 2022): 1307. http://dx.doi.org/10.3390/pr10071307.

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In this research, an intelligent control architecture for an experimental Unmanned Aerial Vehicle (UAV) bearing unconventional inverted V-tail design, is presented. To handle UAV’s inherent control complexities, while keeping them computationally acceptable, a variant of distinct Deep Reinforcement Learning (DRL) algorithm, namely Deep Deterministic Policy Gradient (DDPG) is proposed. Conventional DDPG algorithm after being modified in its learning architecture becomes capable of intelligently handling the continuous state and control space domains besides controlling the platform in its entire flight regime. Nonlinear simulations were then performed to analyze UAV performance under different environmental and launch conditions. The effectiveness of the proposed strategy is further demonstrated by comparing the results with the linear controller for the same UAV whose feedback loop gains are optimized by employing technique of optimal control theory. Results indicate the significance of the proposed control architecture and its inherent capability to adapt dynamically to the changing environment, thereby making it of significant utility to airborne UAV applications.

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Al Ridhawi, Ismaeel, Ouns Bouachir, Moayad Aloqaily, and Azzedine Boukerche. "Design Guidelines for Cooperative UAV-supported Services and Applications." ACM Computing Surveys 54, no. 9 (December 31, 2022): 1–35. http://dx.doi.org/10.1145/3467964.

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Internet of Things (IoT) systems have advanced greatly in the past few years, especially with the support of Machine Learning (ML) and Artificial Intelligence (AI) solutions. Numerous AI-supported IoT devices are playing a significant role in providing complex and user-specific smart city services. Given the multitude of heterogeneous wireless networks, the plethora of computer and storage architectures and paradigms, and the abundance of mobile and vehicular IoT devices, true smart city experiences are only attainable through a cooperative intelligent and secure IoT framework. This article provides an extensive study on different cooperative systems and envisions a cooperative solution that supports the integration and collaboration among both centralized and distributed systems, in which intelligent AI-supported IoT devices such as smart UAVs provide support in the data collection, processing and service provisioning process. Moreover, secure and collaborative decentralized solutions such as Blockchain are considered in the service provisioning process to enable enhanced privacy and authentication features for IoT applications. As such, user-specific complex services and applications within smart city environments will be delivered and made available in a timely, secure, and efficient manner.

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Hua, Meng, Luxi Yang, Qingqing Wu, Cunhua Pan, Chunguo Li, and A. Lee Swindlehurst. "UAV-Assisted Intelligent Reflecting Surface Symbiotic Radio System." IEEE Transactions on Wireless Communications 20, no. 9 (September 2021): 5769–85. http://dx.doi.org/10.1109/twc.2021.3070014.

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Bensaid, Alaa, Adil Sayouti, and Hicham Medromi. "DEVELOPMENT OF INTELLIGENT HYBRID ARCHITECTURE FOR AUTONOMOUS UAV." International Journal of Advanced Research 8, no. 02 (February 29, 2020): 276–88. http://dx.doi.org/10.21474/ijar01/10464.

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Luo, Wei, Ze Zhang, Ping Fu, Guosheng Wei, Dongliang Wang, Xuqing Li, Quanqin Shao, et al. "Intelligent Grazing UAV Based on Airborne Depth Reasoning." Remote Sensing 14, no. 17 (August 25, 2022): 4188. http://dx.doi.org/10.3390/rs14174188.

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The existing precision grazing technology helps to improve the utilization rate of livestock to pasture, but it is still at the level of “collectivization” and cannot provide more accurate grazing management and control. (1) Background: In recent years, with the rapid development of agent-related technologies such as deep learning, visual navigation and tracking, more and more lightweight edge computing cell target detection algorithms have been proposed. (2) Methods: In this study, the improved YOLOv5 detector combined with the extended dataset realized the accurate identification and location of domestic cattle; with the help of the kernel correlation filter (KCF) automatic tracking framework, the long-term cyclic convolution network (LRCN) was used to analyze the texture characteristics of animal fur and effectively distinguish the individual cattle. (3) Results: The intelligent UAV equipped with an AGX Xavier high-performance computing unit ran the above algorithm through edge computing and effectively realized the individual identification and positioning of cattle during the actual flight. (4) Conclusion: The UAV platform based on airborne depth reasoning is expected to help the development of smart ecological animal husbandry and provide better precision services for herdsmen.

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Shang, Bodong, Rubayet Shafin, and Lingjia Liu. "UAV Swarm-Enabled Aerial Reconfigurable Intelligent Surface (SARIS)." IEEE Wireless Communications 28, no. 5 (October 2021): 156–63. http://dx.doi.org/10.1109/mwc.010.2000526.

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Mu, Xidong, Yuanwei Liu, Li Guo, Jiaru Lin, and H. Vincent Poor. "Intelligent Reflecting Surface Enhanced Multi-UAV NOMA Networks." IEEE Journal on Selected Areas in Communications 39, no. 10 (October 2021): 3051–66. http://dx.doi.org/10.1109/jsac.2021.3088679.

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Li, Yijiu, Cheng Yin, Tan Do-Duy, Antonino Masaracchia, and Trung Q. Duong. "Aerial Reconfigurable Intelligent Surface-Enabled URLLC UAV Systems." IEEE Access 9 (2021): 140248–57. http://dx.doi.org/10.1109/access.2021.3119268.

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Moussid, Mostafa, Adil Sayouti, and Hicham Medromi. "DEVELOPMENT OF INTELLIGENT HYBRID ARCHITECTURE FOR AUTONOMOUS UAV." International Journal of Advanced Research 5, no. 2 (February 28, 2017): 638–53. http://dx.doi.org/10.21474/ijar01/3196.

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Oubbati, Omar Sami, Abderrahmane Lakas, Fen Zhou, Mesut Güneş, Nasreddine Lagraa, and Mohamed Bachir Yagoubi. "Intelligent UAV-assisted routing protocol for urban VANETs." Computer Communications 107 (July 2017): 93–111. http://dx.doi.org/10.1016/j.comcom.2017.04.001.

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Lu, Hao, Minghe Mao, and Jianjun Sun. "Space Deployment Algorithm for UAV-IRS-Based Systems Using a Ck++ Optimizer." Wireless Communications and Mobile Computing 2022 (September 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/7916305.

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With the popularity of 5G mobile communication services, the number of users has increased dramatically, as well as the nonuniformity of user density distribution; many users are in nonideal channel conditions, so unmanned aerial vehicle (UAV) as a passive relay equipment platform has gradually entered people’s vision. Intelligent reflective surfaces (IRSs) capable of reconfiguring electromagnetic absorption and reflection properties in real-time are offering unprecedented opportunities to enhance wireless communication experience in challenging environments. In this paper, we start from the point of minimizing the energy consumption and nodes of UAV passive relay; the paper proposes to install intelligent reflecting surface (IRS) on UAV as a new passive relay and establish the communication coverage model of UAV-IRS. Then, the main relationship between the coverage radius and hover height of UAV-IRS is verified. In view of different distribution densities of target users, a CK++ (cyclic k -means++) is proposed to solve the spatial deployment problem of UAV-IRS, where the optimal solution of the system is obtained through cyclic clustering. And the algorithm is verified to effectively improve the performance of urban mobile communication and user communication service quality through numerical stimulation.

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Pu, Huangzhong, Ziyang Zhen, and Daobo Wang. "Modified shuffled frog leaping algorithm for optimization of UAV flight controller." International Journal of Intelligent Computing and Cybernetics 4, no. 1 (March 29, 2011): 25–39. http://dx.doi.org/10.1108/17563781111115778.

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PurposeAttitude control of unmanned aerial vehicle (UAV) is the purposeful manipulation of controllable external forces to establish a desired attitude, which is inner‐loop of the autonomous flight control system. In the practical applications, classical control methods such as proportional‐integral‐derivative control are usually selected because of simple and high reliability. However, it is usually difficult to select or optimize the control parameters. The purpose of this paper is to investigate an intelligent algorithm based classical controller of UAV.Design/methodology/approachAmong the many intelligent algorithms, shuffled frog leaping algorithm (SFLA) combines the benefits of the genetic‐based memetic algorithm as well as social behavior based particle swarm optimization. SFLA is a population based meta‐heuristic intelligent optimization method inspired by natural memetics. In order to improve the performance of SFLA, a different dividing method of the memeplexes is presented to make their performance balance; moreover, an evolution mechanism of the best frog is introduced to make the algorithm jump out the local optimum. The modified SFLA is applied to the tuning of the proportional coefficients of pitching and rolling channels of UAV flight control system.FindingsSimulation of a UAV control system in which the nonlinear model is obtained by the wind tunnel experiment show the rapid dynamic response and high control precision by using the modified SFLA optimized attitude controller, which is better than that of the original SFLA and particle swarm optimization method.Originality/valueA modification scheme is presented to improve the global searching capability of SFLA. The modified SFLA based intelligent determination method of the UAV flight controller parameters is proposed, in order to improve the attitude control performance of UAV.

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Jia, Feng, and Yang Song. "UAV Automation Control System Based on an Intelligent Sensor Network." Journal of Sensors 2022 (August 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/7143194.

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With the widespread use of UAVs, it is gradually difficult for single UAV to meet the needs of increasingly complex scenarios. At the same time, the problems of low autonomy and high dependence on control stations in central UAV cluster networks are gradually highlighted. In this paper, we analyze the theoretical conditions of network topology establishment and network connectivity, design a series of UAV distributed cluster automation control algorithm frameworks, and achieve certain research results, taking the distributed clusters of flight self-organizing networks as the background and using mathematical tools such as algebraic graph theory and random geometry to build a vibration sensor array model based on multiple intelligent sensor management. Based on this, a distributed connectivity maintenance algorithm based on the importance of nodes is designed to realize the “self-healing” of the flight’s self-organizing network. This study also improves the Mavlink flight control communication protocol customization and Zigbee wireless networking mode design to solve the UAV swarm communication link collision problem. Compared with the existing distributed spectrum estimation-based node importance algorithm, the proposed algorithm further analyzes the topological changes caused by the removal of associated edges by failed nodes and the reconstruction of new associated edges between neighboring nodes, so that the theoretical results are closer to the actual topological dynamics of the flight self-organizing network.

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Han, Changwan, Hyeongjun Jeon, Junghyun Oh, and Heungjae Lee. "Dynamic Order Picking Method for Multi-UAV System in Intelligent Warehouse." Remote Sensing 14, no. 23 (December 1, 2022): 6106. http://dx.doi.org/10.3390/rs14236106.

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For the logistics environment, multi-UAV algorithms have been studied for the purpose of order picking in warehouses. However, modern order picking adopts static order picking methods that struggle to cope with increasing volumes of goods because the algorithms receive orders for a certain period of time and pick only those orders. In this paper, by using the modified interventionist method and dynamic path planning, we aim to assign orders received in real-time to multi-UAVs in the warehouse, and to determine the order picking sequence and path of each UAV. The halting and correcting strategy is proposed to assign orders to UAVs in consideration of the similarity between the UAV’s picking list and the orders. A UAV starts picking orders by using the ant colony optimization algorithm for the orders initially assigned. For additional orders, the UAV modifies the picking sequence and UAV’s path in real time by using the k-opt-based algorithm. We evaluated the proposed method by changing the parameters in a simulation of a general warehouse layout. The results show that the proposed method not only reduces completion time compared to the previous algorithm but also reduces UAV’s travel distance and the collapsed time.

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Wang, Gicheol, Byoung-Sun Lee, Jae Young Ahn, and Gihwan Cho. "A UAV-Aided Cluster Head Election Framework and Applying Such to Security-Driven Cluster Head Election Schemes: A Survey." Security and Communication Networks 2018 (June 19, 2018): 1–17. http://dx.doi.org/10.1155/2018/6475927.

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UAS (Unmanned Aerial Systems) are now drawing a lot of attention from academic and research fields as well as the general public. The UAS is expected to provide many promising applications such as intelligent transportation system, disaster management, search and rescue, public safety, smart delivery, wild species monitoring, and wireless service area extension. More specifically, as a part of the wireless service extension, we deal with the information dissemination and collection using a UAV in this paper. In this application, because the UAV communicates with each CH (Cluster Head) to collect data from sensor nodes or to disseminate information to the sensor nodes, well-behaved and qualified nodes should be elected as CHs and their integrity should be preserved. Even though a UAV makes the information dissemination and collection process efficient in a WSN, we can make the UAV help the election of new CHs to mitigate the threat of compromised CHs. To this aim, we first propose a UAV-aided CH election framework where a UAV delivers the critical information collected from sensors to the sink, and the sink reselects a set of well-behaved and qualified CHs considering the information. Then, we classify the existing security-driven CH election schemes into several categories and explain the principle of each category and its representative schemes. For each representative scheme, we also explain how to adapt it into the UAV-aided CH election framework. Next, we identify some desirable security properties that a CH election scheme should provide and reveal the security level that each representative scheme reaches for the desirable security properties. Next, we compare communication and computation overhead of the security-driven CH election schemes in terms of the big O notation. In conclusion, we reveal what we have learned from this survey work and provide a future work item.

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Yang, Jian, and Xuejun Huang. "Intelligent Planning Modeling and Optimization of UAV Cluster Based on Multi-Objective Optimization Algorithm." Electronics 11, no. 24 (December 19, 2022): 4238. http://dx.doi.org/10.3390/electronics11244238.

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As a flight tool integrating carrier and reconnaissance, unmanned aerial vehicles (UAVs) are applied in various fields. In recent years, mission planning and path optimization have become the most important research focuses in the field of UAVs. With the continuous maturity of artificial intelligence technology, various search algorithms have been applied in the field of unmanned aerial vehicles. However, these algorithms have certain defects, which lead to problems, such as large search volume and low efficiency in task planning, and cannot meet the requirements of path planning. The objective optimization algorithm has a good performance in solving optimization problems. In this paper, the intelligent planning model of UAV cluster was established based on multi-objective optimization algorithm, and its path is optimized. In the aspect of modeling, this paper studied and analyzed online task planning, search rules and cluster formation control using an agent-based intelligent modeling method. For mission planning and optimization, it combined multi-objective optimization algorithm to build the model from three aspects of mission allocation, route planning and planning evaluation. The final simulation results showed that the UAV cluster intelligent planning modeling method and path optimization method based on multi-objective optimization algorithm met the requirements of route design and improved the path search efficiency with 2.26% task completion satisfaction.

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Chi, Wensheng, Hai Wang, Wenjun Xie, Peng Zhang, and Le Ru. "Research on Distributed Cooperative Intelligent Spectrum Sensing of UAV Cluster." Wireless Communications and Mobile Computing 2022 (July 13, 2022): 1–15. http://dx.doi.org/10.1155/2022/1981533.

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Spectrum sensing is important to improving the survivability of unmanned aerial vehicles (UAVs) in complex electromagnetic environments. At a low signal-to-noise ratio (SNR), a UAV cluster has more prominent advantages in cluster distributed cooperative sensing than a single UAV. Aiming at this urgent need, joint optimal design is carried out for adaptive spectrum sensing algorithm and distributed estimation algorithm to realize the distributed cooperative intelligent spectrum sensing of UAV cluster. In this paper, the adaptive theory is analyzed first, and the performance of the conventional energy-aware sensing method and the least mean square (LMS) spectrum sensing algorithm is compared. In a complex electromagnetic environment, it is proposed to replan the real-time network by deleting erroneous data nodes in order to eliminate parameter estimation deviations caused by data errors. Under the condition of ensuring detection probability, the fast spectrum sensing algorithm based on SNR estimation is optimized by adaptively selecting and setting the SNR threshold to solve the problem of complex and slow calculation. The superiority of distributed spectrum estimation algorithm without erroneous data nodes is verified at a low SNR, showing that the algorithm has a good steady-state error curve and avoids the impact of data errors on detection results. In addition, the effectiveness of optimizing the fast spectrum sensing algorithm by selecting and setting the SNR threshold is verified to improve the distributed cooperative intelligent spectrum sensing rate of UAV cluster.

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Luo, Yuxie, Jia Song, Kai Zhao, and Yang Liu. "UAV-Cooperative Penetration Dynamic-Tracking Interceptor Method Based on DDPG." Applied Sciences 12, no. 3 (February 3, 2022): 1618. http://dx.doi.org/10.3390/app12031618.

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The multi-UAV system has stronger robustness and better stability in combat. Therefore, the collaborative penetration of UAVs has been extensively studied in recent years. Compared with general static combat scenes, the dynamic tracking and interception of equipment penetration are more difficult to achieve. To realize the coordinated penetration of the dynamic-tracking interceptor by the multi-UAV system, the intelligent UAV model is established by using the deep deterministic policy-gradient algorithm, and the reward function is constructed using the cooperative parameters of multiple UAVs to guide the UAV to proceed with collaborative penetration. The simulation experiment proved that the UAV finally evaded the dynamic-tracking interceptor, and multiple UAVs reached the target at the same time, realizing the time coordination of the multi-UAV system.

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Li, Ping, Yong Hong Hu, and Mei Xia Du. "Design of Intelligent Temperature Controller for UAV Airborne Terminal." Applied Mechanics and Materials 336-338 (July 2013): 1250–55. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1250.

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Introduce a multichannel intelligent temperature controller to ensure terminal set of UAV (unmanned aerial vehicle) work under proper temperature. Hardware circuit is implemented through central unit microcontroller ADuC812 extend basic collecting and driving circuit. Heating algorithm employs fuzzy algorithm with prediction model and integral effectiveness, which overcomes the problem that traditional PID algorithm presents barely satisfactory in time delay system [1]. Simulations show this design well meets requirements of UAV airborne terminal computer, possesses good dynamic performance and smaller steady-state error, and avoids oscillation phenomenon. Practical application approves that the measurable temperature range is from-50°C to +50°C with accuracy +/-0.1°C, and static error of temperature control is less than or equal to +/-2°C, which satisfies application demands.

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Fu, Zhangjie, Jingnan Yu, Guowu Xie, Yiming Chen, and Yuanhang Mao. "A Heuristic Evolutionary Algorithm of UAV Path Planning." Wireless Communications and Mobile Computing 2018 (September 9, 2018): 1–11. http://dx.doi.org/10.1155/2018/2851964.

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With the rapid development of the network and the informatization of society, how to improve the accuracy of information is an urgent problem to be solved. The existing method is to use an intelligent robot to carry sensors to collect data and transmit the data to the server in real time. Many intelligent robots have emerged in life; the UAV (unmanned aerial vehicle) is one of them. With the popularization of UAV applications, the security of UAV has also been exposed. In addition to some human factors, there is a major factor in the UAV’s endurance. UAVs will face a problem of short battery life when performing flying missions. In order to solve this problem, the existing method is to plan the path of UAV flight. In order to find the optimal path for a UAV flight, we propose three cost functions: path security cost, length cost, and smoothness cost. The path security cost is used to determine whether the path is feasible; the length cost and smoothness cost of the path directly affect the cost of the energy consumption of the UAV flight. We proposed a heuristic evolutionary algorithm that designed several evolutionary operations: substitution operations, crossover operations, mutation operations, length operations, and smoothness operations. Through these operations to enhance our build path effect. Under the analysis of experimental results, we proved that our solution is feasible.

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Inder, Silva, and Shi. "Learning Control Policies of Driverless Vehicles from UAV Video Streams in Complex Urban Environments." Remote Sensing 11, no. 23 (November 20, 2019): 2723. http://dx.doi.org/10.3390/rs11232723.

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The way we drive, and the transport of today are going through radical changes. Intelligent mobility envisions to improve the efficiency of traditional transportation through advanced digital technologies, such as robotics, artificial intelligence and Internet of Things. Central to the development of intelligent mobility technology is the emergence of connected autonomous vehicles (CAVs) where vehicles are capable of navigating environments autonomously. For this to be achieved, autonomous vehicles must be safe, trusted by passengers, and other drivers. However, it is practically impossible to train autonomous vehicles with all the possible traffic conditions that they may encounter. The work in this paper presents an alternative solution of using infrastructure to aid CAVs to learn driving policies, specifically for complex junctions, which require local experience and knowledge to handle. The proposal is to learn safe driving policies through data-driven imitation learning of human-driven vehicles at a junction utilizing data captured from surveillance devices about vehicle movements at the junction. The proposed framework is demonstrated by processing video datasets captured from uncrewed aerial vehicles (UAVs) from three intersections around Europe which contain vehicle trajectories. An imitation learning algorithm based on long short-term memory (LSTM) neural network is proposed to learn and predict safe trajectories of vehicles. The proposed framework can be used for many purposes in intelligent mobility, such as augmenting the intelligent control algorithms in driverless vehicles, benchmarking driver behavior for insurance purposes, and for providing insights to city planning.

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Chen, Bo, Chunsheng Hua, Decai Li Yuqing He, and Jianda Han. "Intelligent Human–UAV Interaction System with Joint Cross-Validation over Action–Gesture Recognition and Scene Understanding." Applied Sciences 9, no. 16 (August 9, 2019): 3277. http://dx.doi.org/10.3390/app9163277.

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We propose an intelligent human–unmanned aerial vehicle (UAV) interaction system, in which, instead of using the conventional remote controller, the UAV flight actions are controlled by a deep learning-based action–gesture joint detection system. The Resnet-based scene-understanding algorithm is introduced into the proposed system to enable the UAV to adjust its flight strategy automatically, according to the flying conditions. Meanwhile, both the deep learning-based action detection and multi-feature cascade gesture recognition methods are employed by a cross-validation process to create the corresponding flight action. The effectiveness and efficiency of the proposed system are confirmed by its application to controlling the flight action of a real flying UAV for more than 3 h.

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Moholkar, Utkarsh R., Dipti D. Patil, Vinod Kumar, and Archana Patil. "Deep Learning Approach for Unmanned Aerial Vehicle Landing." International Journal of Innovative Technology and Exploring Engineering 11, no. 10 (September 30, 2022): 1–4. http://dx.doi.org/10.35940/ijitee.j9263.09111022.

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It is one of the biggest challenges to land an unmanned aerial vehicle (UAV). Landing it by making its own decisions is almost impossible even if progress has been made in developing deep learning algorithms, which are doing a great job in the Artificial Intelligence sector. But these algorithms require a large amount of data to get optimum results. For a Type-I civilization collecting data while landing UAV on another planet is not feasible. But there is one hack all the required data can be collected by creating a simulation that is cost-effective, time-saving, and safe too. This is a small step toward making an Intelligent UAV that can make its own decisions while landing on a surface other than Earth's surface. Therefore, the simulation has been created inside gaming engine from which the required training data can be collected. And by using that training data, deep neural networks are trained. Also deployed those trained models into the simulation and checked their performance

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Zhao, Xiaoru, Rennong Yang, Ying Zhang, Mengda Yan, and Longfei Yue. "Deep Reinforcement Learning for Intelligent Dual-UAV Reconnaissance Mission Planning." Electronics 11, no. 13 (June 28, 2022): 2031. http://dx.doi.org/10.3390/electronics11132031.

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The reconnaissance of high-value targets is prerequisite for effective operations. The recent appreciation of deep reinforcement learning (DRL) arises from its success in navigation problems, but due to the competitiveness and complexity of the military field, the applications of DRL in the military field are still unsatisfactory. In this paper, an end-to-end DRL-based intelligent reconnaissance mission planning is proposed for dual unmanned aerial vehicle (dual UAV) cooperative reconnaissance missions under high-threat and dense situations. Comprehensive consideration is given to specific mission properties and parameter requirements through the whole modelling. Firstly, the reconnaissance mission is described as a Markov decision process (MDP), and the mission planning model based on DRL is established. Secondly, the environment and UAV motion parameters are standardized to input the neural network, aiming to deduce the difficulty of algorithm convergence. According to the concrete requirements of non-reconnaissance by radars, dual-UAV cooperation and wandering reconnaissance in the mission, four reward functions with weights are designed to enhance agent understanding to the mission. To avoid sparse reward, the clip function is used to control the reward value range. Finally, considering the continuous action space of reconnaissance mission planning, the widely applicable proximal policy optimization (PPO) algorithm is used in this paper. The simulation is carried out by combining offline training and online planning. By changing the location and number of ground detection areas, from 1 to 4, the model with PPO can maintain 20% of reconnaissance proportion and a 90% mission complete rate and help the reconnaissance UAV to complete efficient path planning. It can adapt to unknown continuous high-dimensional environmental changes, is generalizable, and reflects strong intelligent planning performance.

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Journal articles on the topic 'Intelligent UAV'

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