This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Cui Z, Wang Y. UAV Path Planning Based on Multi-Layer Reinforcement Learning Technique [J]. IEEE Access, 2021: 59486–59497.CuiZWangY.UAV Path Planning Based on Multi-Layer Reinforcement Learning Technique [J]. , 2021: 59486–59497.Search in Google Scholar
Qadir Z, Zafar M H, MOOSAVI S K R, et al. Autonomous UAV path planning optimization using Metaheuristic approach for pre-disaster assessment [J]. IEEE Internet of Things Journal, 2022: 12505–12514.QadirZZafarM HMoosaviS K RAutonomous UAV path planning optimization using Metaheuristic approach for pre-disaster assessment [J]. , 2022: 12505–12514.Search in Google Scholar
Wu R, Gu F, Liu H L, et al. UAV Path Planning Based on Multicritic-Delayed Deep Deterministic Policy Gradient [J]. Wireless Communications and Mobile Computing, 2022: 1–12.WuRGuFLiuH LUAV Path Planning Based on Multicritic-Delayed Deep Deterministic Policy Gradient [J]. , 2022: 1–12.Search in Google Scholar
Yan C, Xiang X, Wang C. Towards Real-Time Path Planning through Deep Reinforcement Learning for a UAV in Dynamic Environments [J]. Journal of Intelligent & Robotic Systems, 2020: 297–309.YanCXiangXWangC.Towards Real-Time Path Planning through Deep Reinforcement Learning for a UAV in Dynamic Environments [J]. , 2020: 297–309.Search in Google Scholar
Faust A, Chiang H T, Rackley N, et al. Avoiding moving obstacles with stochastic hybrid dynamics using PEARL: PrEference Appraisal Reinforcement Learning [C]//2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden. 2016.FaustAChiangH TRackleyN//2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden. 2016.Search in Google Scholar
Jaradat M A K, Al-Rousan M, Quadan L. Reinforcement based mobile robot navigǎtion in dynamic environment [J]. Robotics and Computer Integrated Manufacturing, 2011, 27(1): 135–149.JaradatM A KAl-RousanMQuadanL.Reinforcement based mobile robot navigǎtion in dynamic environment [J]. , 2011, 27(1): 135–149.Search in Google Scholar
Shalev-Shwartz S, Shammah S, Shashua A. Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving [J]. arXiv:1610.03295v1, 2016.Shalev-ShwartzSShammahSShashuaA.Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving [J]. , 2016.Search in Google Scholar
Wang Y H, Li T H S, Lin C J. Backward Q-learning: The combination of Sarsa algorithm and Q-learning [J]. Engineering Applications of Artificial Intelligence, 2013, 26(9): 2184–2193.WangY HLiT H SLinC J.Backward Q-learning: The combination of Sarsa algorithm and Q-learning [J]. , 2013, 26(9): 2184–2193.Search in Google Scholar
Bianchi R A, Martins M F, Ribeiro C H, et al. Heuristically-accelerated multiagent reinforcement learning.[J]. IEEE Transactions on Cybernetics, 2014, 44(2): 252–265.BianchiR AMartinsM FRibeiroC HHeuristically-accelerated multiagent reinforcement learning.[J]. , 2014, 44(2): 252–265.Search in Google Scholar
Roberge V, Tarbouchi M, Labonte G. Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning [J]. IEEE Transactions on Industrial Informatics, 2013: 132–141.RobergeVTarbouchiMLabonteG.Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning [J]. , 2013: 132–141.Search in Google Scholar
Smolyanskiy N, Kamenev A, Smith J, et al. Toward low-flying autonomous MAV trail navigation using deep neural networks for environmental awareness [C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC. 2017.SmolyanskiyNKamenevASmithJ//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC. 2017.Search in Google Scholar
Walker O, Vanegas F, Gonzalez F, et al. A Deep Reinforcement Learning Framework for UAV Navigation in Indoor Environments [C]//2019 IEEE Aerospace Conference, Big Sky, MT, USA. 2019.WalkerOVanegasFGonzalezF//2019 IEEE Aerospace Conference, Big Sky, MT, USA. 2019.Search in Google Scholar
Walker O, Vanegas F, Gonzalez F, et al. A Deep Reinforcement Learning Framework for UAV Navigation in Indoor Environments [C]//2019 IEEE Aerospace Conference, Big Sky, MT, USA. 2019.WalkerOVanegasFGonzalezF//2019 IEEE Aerospace Conference, Big Sky, MT, USA. 2019.Search in Google Scholar
Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning [J]. Nature, 2015: 529–533.MnihVKavukcuogluKSilverDHuman-level control through deep reinforcement learning [J]. , 2015: 529–533.Search in Google Scholar
Chen P, Pei J, Lu W, et al. A Deep Reinforcement Learning Based Method for Real-Time Path Planning and Dynamic Obstacle Avoidance [J]. Neurocomputing, 2022, 497: 64–75.ChenPPeiJLuWA Deep Reinforcement Learning Based Method for Real-Time Path Planning and Dynamic Obstacle Avoidance [J]. , 2022, 497: 64–75.Search in Google Scholar