Abstract
Unmanned aerial vehicles (UAVs) are a valuable source of data for a wide range of real-time applications, due to their functionality, availability, adaptability, and maneuverability. Working as mobile sensors, they can provide a cost-effective solution for extremely complex tasks, such as inspection, air-to-ground communications, search and rescue, surveillance, among others. Nevertheless, the robots needs to navigate in quite distinct environments and in different dynamism levels, usually facing unpredicted situations, very often using limited sensing and computing capabilities. A large number of solutions to this problem has been featured by the scientific community in the last years, some of them based on machine-learning (ML) methods. Due to its great capability to deal with big data and complexity, as well as its speedy and high-accuracy processing, the ML framework has been used to improve existing technologies and control techniques. In this context, its adoption in several UAV navigation strategies is expected to provide solutions for various problems where UAVs are used in real-time applications. Thus, in order to contextualize the most recent advances, this work provides a detailed survey of relevant researches in which ML techniques have been used in UAV navigation to improve some functional aspects, such as energy-efficiency, communication, execution time, resource management, obstacle avoidance, and path planning.
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The datasets analyzed during the current work are available from the corresponding author on reasonable request.
Notes
GPS signals could be weak or totally lost in scenarios like urban areas, low altitude operations or indoor flights, for instance.
Abbreviations
- A3C:
-
Asynchronous advantage actor-critic
- AI:
-
Artificial intelligence
- ANN:
-
Artificial Neural Network
- BIF:
-
Bayesian information filter
- CNN:
-
Convolutional Neural Network
- DDPGfD:
-
Deep deterministic policy gradient from demonstrations
- DDPG:
-
Deep deterministic policy gradient
- DL:
-
Deep learning
- DRL:
-
Deep reinforcement learning
- EKF:
-
Extend Kalman filter
- ESC:
-
Electronic speed controllers
- GCS:
-
Ground control station
- GNSS:
-
Global navigation satellite system
- GPS:
-
Global position system
- HiL:
-
Hardware in the loop
- HRI:
-
Human–robot interface
- IMU:
-
Inertial measurement unit
- INS:
-
Inertial navigation system
- LiDAR:
-
Light detection and ranging
- LSTM:
-
Long Short-Term Memory
- LoS:
-
Line-of-sight
- MDP:
-
Markov decision process
- ML:
-
Machine learning
- MLP:
-
Multilayer perceptrons
- NS:
-
Navigation system
- OGV:
-
On-ground vision
- PHY:
-
Physical layer
- POfD:
-
Policy optimization from demonstrations
- PWM:
-
Pulse width modulation
- RF:
-
Radio-frequency
- RL:
-
Reinforcement learning
- SA:
-
Situational awareness
- SL:
-
Supervised learning
- SLAM:
-
Simultaneous localization and mapping
- UAS:
-
Unmanned aerial systems
- UAV:
-
Unmanned aerial vehicles
- UL:
-
Unsupervised learning
- VO:
-
Visual odometry
References
Mohamed N, Al-Jaroodi J, Jawhar I, Idries A, Mohammed F. Unmanned aerial vehicles applications in future smart cities. Technol Forecast Soc Change. 2020;153:119293.
Doughty CL, Cavanaugh KC. Mapping coastal wetland biomass from high resolution unmanned aerial vehicle (UAV) imagery. Remote Sens. 2019;11(5):540.
Osco LP, Junior JM, Ramos APM, de Castro Jorge LA, Fatholahi SN, de Andrade Silva J, Matsubara ET, Pistori H, Gonçalves WN, Li J. A review on deep learning in UAV remote sensing. Int J Appl Earth Obs Geoinf. 2021;102:102456.
Sun W, Dai L, Zhang X, Chang P, He X. RSOD: real-time small object detection algorithm in UAV-based traffic monitoring. Appl Intell. 2021;52:8448–63.
Mohiuddin A, Taha T, Zweiri Y, Gan D. Dual-UAV payload transportation using optimized velocity profiles via real-time dynamic programming. Drones. 2023;7(3):171.
López S, Cervantes J-A, Cervantes S, Molina J, Cervantes F. The plausibility of using unmanned aerial vehicles as a serious game for dealing with attention deficit-hyperactivity disorder. Cogn Syst Res. 2020;59:160–70.
Sethuraman SC, Tadkapally GR, Mohanty SP, Subramanian A. iDRONE: IoT-enabled unmanned aerial vehicles for detecting wildfires using convolutional neural networks. SN Comput Sci. 2022;3(3):242.
Dabas C. Insight of unmanned aerial vehicles accessing ensemble techniques. SN Comput Sci. 2021;2(6):458.
Balamurugan G, Valarmathi J, Naidu V. Survey on UAV navigation in GPS denied environments. In: 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE; 2016. p. 198–204.
Yang G-Z, Bellingham J, Dupont PE, Fischer P, Floridi L, Full R, Jacobstein N, Kumar V, McNutt M, Merrifield R. The grand challenges of science robotics. Sci Robot. 2018;3(14):7650.
Lu Y, Xue Z, Xia G-S, Zhang L. A survey on vision-based UAV navigation. Geo-spat Inf Sci. 2018;21(1):21–32.
Queralta JP, Almansa CM, Schiano F, Floreano D, Westerlund T. UWB-based system for UAV localization in GNSS-denied environments: characterization and dataset (2020). arXiv preprint arXiv:2003.04380.
Gupte S, Mohandas PIT, Conrad JM. A survey of quadrotor unmanned aerial vehicles. In: 2012 Proceedings of IEEE Southeastcon. IEEE; 2012. p. 1–6.
Kendoul F. Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems. J Field Robot. 2012;29(2):315–78.
Carrio A, Sampedro C, Rodriguez-Ramos A, Campoy P. A review of deep learning methods and applications for unmanned aerial vehicles. J Sens. 2017;2017(2):1–13.
Zahran S, Moussa A, El-Sheimy N. Enhanced UAV navigation in GNSS denied environment using repeated dynamics pattern recognition. In: 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS). IEEE; 2018. p. 1135–42.
Virnodkar SS, Pachghare VK, Patil V, Jha SK. Remote sensing and machine learning for crop water stress determination in various crops: a critical review. Precis Agric. 2020;21(5):1121–55.
Hung GL, Sahimi MSB, Samma H, Almohamad TA, Lahasan B. Faster R-CNN deep learning model for pedestrian detection from drone images. SN Comput Sci. 2020;1:1–9.
Chriki A, Touati H, Snoussi H, Kamoun F. Centralized cognitive radio based frequency allocation for UAVs communication. In: 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC). IEEE; 2019. p. 1674–9.
Iqbal S. A study on UAV operating system security and future research challenges. In: 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC). IEEE; 2021. p. 0759–65.
Touati H, Chriki A, Snoussi H, Kamoun F. Cognitive radio and dynamic TDMA for efficient UAVs swarm communications. Comput Netw. 2021;196:108264.
Khan NA, Jhanjhi NZ, Brohi SN, Nayyar A. Emerging use of UAV’s: secure communication protocol issues and challenges. In: Drones in smart-cities. Amsterdam: Elsevier; 2020. p. 37–55.
Khan NA, Brohi SN, Jhanjhi N. UAV’s applications, architecture, security issues and attack scenarios: a survey. In: Intelligent computing and innovation on data science. Berlin: Springer; 2020. p. 753–60.
Choi SY, Cha D. Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art. Adv Robot. 2019;33(6):265–77.
Bithas PS, Michailidis ET, Nomikos N, Vouyioukas D, Kanatas AG. A survey on machine-learning techniques for UAV-based communications. Sensors. 2019;19(23):5170.
Mitchell R, Michalski J, Carbonell T. An artificial intelligence approach. Berlin: Springer; 2013.
Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S. Quantum machine learning. Nature. 2017;549(7671):195–202.
Alpaydin E, Cambridge. Introduction to machine learning. MIT Press; 2020.
Yijing Z, Zheng Z, Xiaoyi Z, Yang L. Q learning algorithm based UAV path learning and obstacle avoidence approach. In: 2017 36th Chinese Control Conference (CCC). IEEE; 2017. p. 3397–402.
Yavanoglu O, Aydos M. A review on cyber security datasets for machine learning algorithms. In: 2017 IEEE International Conference on Big Data (big Data). IEEE; 2017. p. 2186–93.
Hu W, Fey M, Zitnik M, Dong Y, Ren H, Liu B, Catasta M, Leskovec J. Open graph benchmark: datasets for machine learning on graphs. Adv Neural Inf Process Syst. 2020;33:22118–33.
Gauen K, Dailey R, Laiman J, Zi Y, Asokan N, Lu Y-H, Thiruvathukal GK, Shyu M-L, Chen S-C. Comparison of visual datasets for machine learning. In: 2017 IEEE International Conference on Information Reuse and Integration (IRI). IEEE; 2017. p. 346–55.
Prakash KB, Imambi SS, Ismail M, Kumar TP, Pawan Y. Analysis, prediction and evaluation of Covid-19 datasets using machine learning algorithms. Int J. 2020;8(5).
Kocheturov A, Pardalos PM, Karakitsiou A. Massive datasets and machine learning for computational biomedicine: trends and challenges. Ann Oper Res. 2019;276(1):5–34.
Challita U, Ferdowsi A, Chen M, Saad W. Machine learning for wireless connectivity and security of cellular-connected UAVs. IEEE Wirel Commun. 2019;26(1):28–35.
Kouhdaragh V, Verde F, Gelli G, Abouei J. On the application of machine learning to the design of UAV-based 5G radio access networks. Electronics. 2020;9(4):689.
Han L, Yang G, Dai H, Xu B, Yang H, Feng H, Li Z, Yang X. Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data. Plant Methods. 2019;15(1):1–19.
Abdulridha J, Batuman O, Ampatzidis Y. UAV-based remote sensing technique to detect citrus canker disease utilizing hyperspectral imaging and machine learning. Remote Sens. 2019;11(11):1373.
Abeysinghe T, Simic Milas A, Arend K, Hohman B, Reil P, Gregory A, Vázquez-Ortega A. Mapping invasive phragmites australis in the old woman creek estuary using UAV remote sensing and machine learning classifiers. Remote Sens. 2019;11(11):1380.
Lei L, Shen G, Zhang L, Li Z. Toward intelligent cooperation of UAV swarms: when machine learning meets digital twin. IEEE Netw. 2020;35(1):386–92.
Wang X, Xu Y, Chen C, Yang X, Chen J, Ruan L, Xu Y, Chen R. Machine learning empowered spectrum sharing in intelligent unmanned swarm communication systems: challenges, requirements and solutions. IEEE Access. 2020;8:89839–49.
Maimaitijiang M, Sagan V, Sidike P, Daloye AM, Erkbol H, Fritschi FB. Crop monitoring using satellite/UAV data fusion and machine learning. Remote Sens. 2020;12(9):1357.
Ge X, Wang J, Ding J, Cao X, Zhang Z, Liu J, Li X. Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring. PeerJ. 2019;7:6926.
Neuville R, Bates JS, Jonard F. Estimating forest structure from UAV-mounted lidar point cloud using machine learning. Remote Sens. 2021;13(3):352.
Wang J-L, Li Y-R, Adege AB, Wang L-C, Jeng S-S, Chen J-Y. Machine learning based rapid 3d channel modeling for UAV communication networks. In: 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC). IEEE; 2019. p. 1–5.
Zhao Y, Zheng Z, Liu Y. Survey on computational-intelligence-based UAV path planning. Knowl-Based Syst. 2018;158:54–64.
Kanellakis C, Nikolakopoulos G. Survey on computer vision for UAVs: current developments and trends. J Intell Robot Syst. 2017;87(1):141–68.
Al-Kaff A, Martin D, Garcia F, de la Escalera A, Armingol JM. Survey of computer vision algorithms and applications for unmanned aerial vehicles. Expert Syst Appl. 2018;92:447–63.
Sanchez-Rodriguez J-P, Aceves-Lopez A. A survey on stereo vision-based autonomous navigation for multi-rotor MUAVs. Robotica. 2018;36(8):1225–43.
Luong NC, Hoang DT, Gong S, Niyato D, Wang P, Liang Y-C, Kim DI. Applications of deep reinforcement learning in communications and networking: a survey. IEEE Commun Surv Tutor. 2019;21(4):3133–74.
Azar AT, Koubaa A, Ali Mohamed N, Ibrahim HA, Ibrahim ZF, Kazim M, Ammar A, Benjdira B, Khamis AM, Hameed IA. Drone deep reinforcement learning: a review. Electronics. 2021;10(9):999.
Rezwan S, Choi W. Artificial intelligence approaches for UAV navigation: recent advances and future challenges. IEEE Access. 2022;10:26320–39.
Alhafnawi M, Salameh HB, Masadeh A, Al-Obiedollah H, Ayyash M, El-Khazali R, Elgala H. A survey of indoor and outdoor UAV-based target tracking systems: current status, challenges, technologies, and future directions. IEEE Access. 2023;11:68324–39.
McEnroe P, Wang S, Liyanage M. A survey on the convergence of edge computing and ai for UAVs: opportunities and challenges. IEEE Internet Things J. 2022;9(17):15435–59.
Polydoros AS, Nalpantidis L. Survey of model-based reinforcement learning: applications on robotics. J Intell Robot Syst. 2017;86(2):153–73.
Fraga-Lamas P, Ramos L, Mondéjar-Guerra V, Fernández-Caramés TM. A review on IoT deep learning UAV systems for autonomous obstacle detection and collision avoidance. Remote Sens. 2019;11(18):2144.
Ullah H, Nair NG, Moore A, Nugent C, Muschamp P, Cuevas M. 5G communication: an overview of vehicle-to-everything, drones, and healthcare use-cases. IEEE Access. 2019;7:37251–68.
Mittal P, Sharma A, Singh R. Deep learning-based object detection in low-altitude UAV datasets: a survey. Image Vis Comput. 2020;104:104046.
Li B, Fei Z, Zhang Y. UAV communications for 5G and beyond: recent advances and future trends. IEEE Internet Things J. 2018;6(2):2241–63.
Ullah Z, Al-Turjman F, Mostarda L. Cognition in UAV-aided 5G and beyond communications: a survey. IEEE Trans Cognit Commun Netw. 2020;6(3):872–91.
Hu J, Zhang H, Song L, Han Z, Poor HV. Reinforcement learning for a cellular internet of UAVs: protocol design, trajectory control, and resource management. IEEE Wirel Commun. 2020;27(1):116–23.
Lahmeri M-A, Kishk MA, Alouini M-S. Artificial intelligence for UAV-enabled wireless networks: a survey. IEEE Open J Commun Soc. 2021;2:1015–40.
Srivastava S, Narayan S, Mittal S. A survey of deep learning techniques for vehicle detection from UAV images. J Syst Archit. 2021;117:102152.
Olivares-Mendez MA, Fu C, Ludivig P, Bissyandé TF, Kannan S, Zurad M, Annaiyan A, Voos H, Campoy P. Towards an autonomous vision-based unmanned aerial system against wildlife poachers. Sensors. 2015;15(12):31362–91.
Nex F, Remondino F. UAV for 3D mapping applications: a review. Appl Geomat. 2014;6(1):1–15.
Baena S, Boyd DS, Moat J. UAVs in pursuit of plant conservation-real world experiences. Eco Inform. 2018;47:2–9.
Ross S, Melik-Barkhudarov N, Shankar KS, Wendel A, Dey D, Bagnell JA, Hebert M. Learning monocular reactive UAV control in cluttered natural environments. In: 2013 IEEE International Conference on Robotics and Automation. IEEE; 2013. p. 1765–72.
Pizetta IHB, Brandao AS, Sarcinelli-Filho M. Control and obstacle avoidance for an UAV carrying a load in forestal environments. In: 2018 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE; 2018. p. 62–7.
Sanfourche M, Delaune J, Le Besnerais G, De Plinval H, Israel J, Cornic P, Treil A, Watanabe Y, Plyer A. Perception for UAV: vision-based navigation and environment modeling. AerospaceLab. 2012;1(4):1.
Qin H, Meng Z, Meng W, Chen X, Sun H, Lin F, Ang MH. Autonomous exploration and mapping system using heterogeneous UAVs and UGVs in GPS-denied environments. IEEE Trans Veh Technol. 2019;68(2):1339–50.
Azevedo F, Dias A, Almeida J, Oliveira A, Ferreira A, Santos T, Martins A, Silva E. Lidar-based real-time detection and modeling of power lines for unmanned aerial vehicles. Sensors. 2019;19(8):1812.
Geraldes R, Goncalves A, Lai T, Villerabel M, Deng W, Salta A, Nakayama K, Matsuo Y, Prendinger H. UAV-based situational awareness system using deep learning. IEEE Access. 2019;7:122583–94.
Goerzen C, Kong Z, Mettler B. A survey of motion planning algorithms from the perspective of autonomous UAV guidance. J Intell Rob Syst. 2010;57(1):65–100.
Luebke D, Humphreys G. How GPUs work. Computer. 2007;40(2):96–100.
Sarker IH. Machine learning: algorithms, real-world applications and research directions. SN Comput Sci. 2021;2(3):160.
Yu Y, Si X, Hu C, Zhang J. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 2019;31(7):1235–70.
Škrlj B, Kralj J, Lavrač N, Pollak S. Towards robust text classification with semantics-aware recurrent neural architecture. Mach Learn Knowl Extr. 2019;1(2):575–89.
Shyalika C, Silva T, Karunananda A. Reinforcement learning in dynamic task scheduling: a review. SN Comput Sci. 2020;1:1–17.
Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J. Recent advances in convolutional neural networks. Pattern Recogn. 2018;77:354–77.
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition; 2014. arXiv preprint arXiv:1409.1556.
Dionisio-Ortega S, Rojas-Perez LO, Martinez-Carranza J, Cruz-Vega I. A deep learning approach towards autonomous flight in forest environments. In: 2018 International Conference on Electronics, Communications and Computers (CONIELECOMP). IEEE; 2018. p. 139–44.
Espejo-Garcia B, Mylonas N, Athanasakos L, Fountas S. Improving weeds identification with a repository of agricultural pre-trained deep neural networks. Comput Electron Agric. 2020;175:105593.
Siddiqui SA, Salman A, Malik MI, Shafait F, Mian A, Shortis MR, Harvey ES. Automatic fish species classification in underwater videos: exploiting pre-trained deep neural network models to compensate for limited labelled data. ICES J Mar Sci. 2018;75(1):374–89.
Roshanzamir A, Aghajan H, Soleymani Baghshah M. Transformer-based deep neural network language models for Alzheimer’s disease risk assessment from targeted speech. BMC Med Inform Decis Mak. 2021;21(1):1–14.
Lee H, Ho H, Zhou Y. Deep learning-based monocular obstacle avoidance for unmanned aerial vehicle navigation in tree plantations. J Intell Robot Syst. 2021;101(1):1–18.
Vanegas F, Gaston KJ, Roberts J, Gonzalez F. A framework for UAV navigation and exploration in GPS-denied environments. In: 2019 IEEE Aerospace Conference. IEEE; 2019. p. 1–6.
Jung S, Hwang S, Shin H, Shim DH. Perception, guidance, and navigation for indoor autonomous drone racing using deep learning. IEEE Robot Autom Lett. 2018;3(3):2539–44.
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M. ImageNet large scale visual recognition challenge. Int J Comput Vis. 2015;115(3):211–52.
Padhy RP, Verma S, Ahmad S, Choudhury SK, Sa PK. Deep neural network for autonomous UAV navigation in indoor corridor environments. Procedia Comput Sci. 2018;133:643–50.
Jenssen R, Roverso D. Automatic autonomous vision-based power line inspection: a review of current status and the potential role of deep learning. Int J Electr Power Energy Syst. 2018;99:107–20.
Dai Z, Yi J, Zhang Y, Zhou B, He L. Fast and accurate cable detection using CNN. Appl Intell. 2020;50(12):4688–707.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016. p. 770–8.
Giusti A, Guzzi J, Cireşan DC, He F-L, Rodríguez JP, Fontana F, Faessler M, Forster C, Schmidhuber J, Di Caro G. A machine learning approach to visual perception of forest trails for mobile robots. IEEE Robot Autom Lett. 2015;1(2):661–7.
Zhang X, Zhang L, Pei H, Lewis FL. Part-based multi-task deep network for autonomous indoor drone navigation. Trans Inst Meas Control. 2020;42(16):3243–53.
Kupervasser O, Kutomanov H, Levi O, Pukshansky V, Yavich R. Using deep learning for visual navigation of drone with respect to 3D ground objects. Mathematics. 2020;8(12):2140.
Liu Y, Xie K, Huang H. VGF-Net: visual-geometric fusion learning for simultaneous drone navigation and height mapping. Graph Models. 2021;116:101108.
Opromolla R, Inchingolo G, Fasano G. Airborne visual detection and tracking of cooperative UAVs exploiting deep learning. Sensors. 2019;19(19):4332.
Waqas A, Kang D, Cha Y-J. Deep learning-based obstacle-avoiding autonomous UAVs with fiducial marker-based localization for structural health monitoring. Struct Health Monit. 2023;14759217231177314.
Chakravarty P, Kelchtermans K, Roussel T, Wellens S, Tuytelaars T, Van Eycken L. CNN-based single image obstacle avoidance on a quadrotor. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2017. p. 6369–74.
Arulkumaran K, Deisenroth MP, Brundage M, Bharath AA. Deep reinforcement learning: a brief survey. IEEE Signal Process Mag. 2017;34(6):26–38.
Sutton RS, Barto AG. Reinforcement learning: an introduction. Cambridge: MIT Press; 2018.
Imanberdiyev N, Fu C, Kayacan E, Chen I-M. Autonomous navigation of UAV by using real-time model-based reinforcement learning. In: 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE; 2016. p. 1–6.
Pham HX, La HM, Feil-Seifer D, Van Nguyen L. Reinforcement learning for autonomous UAV navigation using function approximation. In: 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). IEEE; 2018. p. 1–6.
Ma Z, Wang C, Niu Y, Wang X, Shen L. A saliency-based reinforcement learning approach for a UAV to avoid flying obstacles. Robot Auton Syst. 2018;100:108–18.
Maciel-Pearson BG, Marchegiani L, Akcay S, Atapour-Abarghouei A, Garforth J, Breckon TP. Online deep reinforcement learning for autonomous UAV navigation and exploration of outdoor environments; 2019. arXiv preprint arXiv:1912.05684.
Koch W, Mancuso R, West R, Bestavros A. Reinforcement learning for UAV attitude control. ACM Trans Cybern-Phys Syst. 2019;3(2):1–21.
Wang C, Wang J, Wang J, Zhang X. Deep-reinforcement-learning-based autonomous UAV navigation with sparse rewards. IEEE Internet Things J. 2020;7(7):6180–90.
Zhou S, Li B, Ding C, Lu L, Ding C. An efficient deep reinforcement learning framework for UAVs. In: 2020 21st International Symposium on Quality Electronic Design (ISQED). IEEE; 2020. p. 323–8.
Bouhamed O, Ghazzai H, Besbes H, Massoud Y. Autonomous UAV navigation: a DDPG-based deep reinforcement learning approach. In: 2020 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE; 2020. p. 1–5.
Agarwal V, Tewari RR. Improving energy efficiency in UAV attitude control using deep reinforcement learning. J Sci Res. 2021;65(3):209–19.
Tong G, Jiang N, Biyue L, Xi Z, Ya W, Wenbo D. UAV navigation in high dynamic environments: a deep reinforcement learning approach. Chin J Aeronaut. 2021;34(2):479–89.
Fu C, Xu X, Zhang Y, Lyu Y, Xia Y, Zhou Z, Wu W. Memory-enhanced deep reinforcement learning for UAV navigation in 3D environment. Neural Comput Appl. 2022;34(17):14599–607.
Sun Q, Fang J, Zheng WX, Tang Y. Aggressive quadrotor flight using curiosity-driven reinforcement learning. IEEE Trans Ind Electron. 2022;69(12):13838–48.
Walker O, Vanegas F, Gonzalez F, Koenig S. A deep reinforcement learning framework for UAV navigation in indoor environments. In: 2019 IEEE Aerospace Conference. IEEE; 2019. p. 1–14.
Acknowledgements
The authors would like to thank the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), an agency of the Brazilian Ministry of Science, Technology, Innovations and Communications that supports scientific and technological development, as well as the Fundação de Amparo à Pesquisa e Inovação de Minas Gerais (FAPEMIG), an agency of the State of MG, Brazil, that supports scientific and technological development-for financing this project. Mr. Fagundes would like to thank the Fundação Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), an agency of the Brazilian Ministry of Education that supports human resources perfection, in where this work is inserted, and MSc. de Carvalho would also like to thank the Fundação de Amparo à Pesquisa de Minas Gerais (FAPEMIG) for the scholarship that allowed him to develop his MSc. and PhD. studies, respectively.
Funding
This study was funded by Fundação de Amparo à Pesquisa e Inovação de Minas Gerais (FAPEMIG) (Grant number APQ-02573-21 edital \(\hbox {N}^\circ\)001/2021 - Demanda Universal) and by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (Grant number 401816/2021-4 Chamada CNPq/MCTI/SEMPI \(\hbox {N}^\circ\)14/2021).
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Fagundes-Junior, L.A., de Carvalho, K.B., Ferreira, R.S. et al. Machine Learning for Unmanned Aerial Vehicles Navigation: An Overview. SN COMPUT. SCI. 5, 256 (2024). https://doi.org/10.1007/s42979-023-02592-5
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DOI: https://doi.org/10.1007/s42979-023-02592-5