Crowd evacuation path planning and simulation method based on deep reinforcement learning and repulsive force field (original) (raw)
Liu J, Chen Y, Chen Y (2021) Emergency and disaster management-crowd evacuation research. J Ind Inf Integr 21:100191 MATH Google Scholar
Yao Z, Zhang G, Lu D, Liu H (2019) Data-driven crowd evacuation: A reinforcement learning method. Neurocomputing 366:314–327 ArticleMATH Google Scholar
Li Y, Wei W, Gao Y, Wang D, Fan Z (2020) PQ-RRT*: An improved path planning algorithm for mobile robots. Expert Syst Appl 152:113425 ArticleMATH Google Scholar
Sun Y, Fang M, Su Y (2021) AGV Path Planning based on Improved Dijkstra Algorithm. J Phys: Conf Ser 1746(1):e012052 MATH Google Scholar
Liu X, Zhang D, Zhang T, Cui Y, Chen L, Liu S (2021) Novel best path selection approach based on hybrid improved A* algorithm and reinforcement learning. Appl Intell 51(12):9015–9029 ArticleMATH Google Scholar
Zhao Y, Liu H, Gao K (2021) An evacuation simulation method based on an improved artificial bee colony algorithm and a social force model. Appl Intell 51(1):100–123 ArticleMATH Google Scholar
Liu H, Zhang P, Hu B, Moore P (2015) A novel approach to task assignment in a cooperative multi-agent design system. Appl Intell 43(1):162–175 ArticleMATH Google Scholar
Zhang P, Liu H, Ding Y (2014) Dynamic bee colony algorithm based on multi-species co-evolution. Appl Intell 40(3):427–440 ArticleMATH Google Scholar
Li H, Liu W, Yang C, Wang W, Qie T, Xiang C (2022) An Optimization-Based Path Planning Approach for Autonomous Vehicles Using the DynEFWA-Artificial Potential Field. IEEE Trans Intell Veh 7(2):263–272 ArticleMATH Google Scholar
Ueda Y, Motoi N (2022) Local Path Planning Based on Velocity Obstacle Considering Collision Probability and Kinematic Constraint for Mobile Robot IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, Brussels, 17-20 October 2022. IEEE, New York, pp 1–6. https://doi.org/10.1109/IECON49645.2022.9968733
Gao J, Ye W, Guo J, Li Z (2020) Deep reinforcement learning for indoor mobile robot path planning. Sensors 20(19):5493 ArticleMATH Google Scholar
Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533
Levine S, Pastor P, Krizhevsky A, Ibarz J, Quillen D (2018) Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. Int J Rob Res 37(4–5):421–436 Article Google Scholar
Ladosz P, Weng L, Kim M, Oh H (2022) Exploration in deep reinforcement learning: A survey. Inf Fusion 85:1–22 ArticleMATH Google Scholar
Zhang Y, Chai Z, Lykotrafitis G (2021) Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles. Phys A Stat Mech its Appl 571:125845 Article Google Scholar
Zhou W, Zhang C, Chen S (2023) Dual deep Q-learning network guiding a multiagent path planning approach for virtual fire emergency scenarios. Appl Intell 53(19):21858–21874 Article Google Scholar
Gao F, Du Z, Werner M, Zhao Y (2022) An improved optimization model for crowd evacuation considering individual exit choice preference. Trans GIS 26(7):2850–2873 ArticleMATH Google Scholar
Devidze R, Kamalaruban P, Singla A (2022) Exploration-Guided Reward Shaping for Reinforcement Learning under Sparse Rewards. Adv Neural Inf Process Syst 35:5829–5842 MATH Google Scholar
Xu D, Huang X, Mango J, Li X, Li Z (2021) Simulating multi-exit evacuation using deep reinforcement learning. Trans GIS 25(3):1542–1564 ArticleMATH Google Scholar
Lin E, Chen Q, Qi X (2020) Deep reinforcement learning for imbalanced classification. Appl Intell 50(8):2488–2502 ArticleMATH Google Scholar
Zhao X, Ding S, An Y, Jia W (2019) Applications of asynchronous deep reinforcement learning based on dynamic updating weights. Appl Intell 49(2):581–591 ArticleMATH Google Scholar
Zhang X, Liu Y, Xu X, Huang Q, Mao H, Carie A (2021) Structural relational inference actor-critic for multi-agent reinforcement learning. Neurocomputing 459:383–394 Article Google Scholar
Lowe R, Wu YI, Tamar A, Harb J, Pieter Abbeel O, Mordatch I (2017) Multi-agent actor-critic for mixed cooperative-competitive environments 31st Conference on Neural Information Processing Systems (NIPS 2017), Beach, CA, 24 January 2017. NeurIPS, San Diego, CA, pp 1–12
Zhang F, Li J, Li Z (2020) A TD3-based multi-agent deep reinforcement learning method in mixed cooperation-competition environment. Neurocomputing 411:206–215 ArticleMATH Google Scholar
Panov AI, Yakovlev KS, Suvorov R (2018) Grid path planning with deep reinforcement learning: Preliminary results. Procedia Comput Sci 123:347–353 ArticleMATH Google Scholar
Li X, Liu H, Li J, Li Y (2021) Deep deterministic policy gradient algorithm for crowd-evacuation path planning. Comput Ind Eng 161:107621 ArticleMATH Google Scholar
Wan K, Wu D, Li B, Gao X, Hu Z, Chen D (2022) ME-MADDPG: An efficient learning-based motion planning method for multiple agents in complex environments. Int J Intell Syst 37(3):2393–2427
Sang H, You Y, Sun X, Zhou Y, Liu F (2021) The hybrid path planning algorithm based on improved A* and artificial potential field for unmanned surface vehicle formations. Ocean Eng 223:108709 Article Google Scholar
Lu G, Chen L, Luo W (2016) Real-time crowd simulation integrating potential fields and agent method. ACM Trans Model Comput Simul 26(4):1–16 ArticleMathSciNetMATH Google Scholar
Wei X, Wang H, Tang Y (2023) Deep hierarchical reinforcement learning based formation planning for multiple unmanned surface vehicles with experimental results. Ocean Eng 286:115577 ArticleMATH Google Scholar
Liu H, Liu B, Zhang H, Li L, Qin X, Zhang G (2018) Crowd evacuation simulation approach based on navigation knowledge and two-layer control mechanism. Inf Sci 436–437:247–267 ArticleMathSciNetMATH Google Scholar
Sun Y, Liu H (2021) Crowd evacuation simulation method combining the density field and social force model. Phys A Stat Mech its Appl 566:125652 ArticleMATH Google Scholar
Hughes RL (2002) A continuum theory for the flow of pedestrians. Transp Res Part B Methodol 36(6):507–535 ArticleMATH Google Scholar
Murata T, Fukami K, Fukagata K (2020) Nonlinear mode decomposition with convolutional neural networks for fluid dynamics. J Fluid Mech 882:A13 ArticleMathSciNetMATH Google Scholar
Treuille A, Cooper S, Popović Z (2006) Continuum crowds. ACM transactions on graphics (TOG) 25(3):1160–1168 Article Google Scholar
Liu Q (2018) A social force model for the crowd evacuation in a terrorist attack. Phys A Stat Mech its Appl 502:315–330 ArticleMATH Google Scholar
Duan J, Liu H, Gong W, Lyu L (2023) Crowd Evacuation Under Real Data: A Crowd Congestion Control Method Based on Sensors and Knowledge Graph. IEEE Sens J 23(8):8923–8931 ArticleMATH Google Scholar
Liu H, Xu B, Lu D, Zhang G (2018) A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm. Appl Soft Comput J 68:360–376 ArticleMATH Google Scholar
Xu M, Li C, Lv P, Lin N, Hou R, Zhou B (2017) An efficient method of crowd aggregation computation in public areas. IEEE Trans Circuits Syst Video Technol 28(10):2814–2825 ArticleMATH Google Scholar
Bahamid A, Ibrahim AM, Shafie AA (2024) Crowd evacuation with human-level intelligence via neuro-symbolic approach. Adv Eng Inform 60:102356 ArticleMATH Google Scholar