Event-driven muti-agent evacuation based on reinforcement learning DOI
Yuhui Liu, Ge Song, Weijian Ni

et al.

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 13, 2023

Various emergencies occur frequently, posing threats and challenges to people’s lives social security. In consequence, the evacuation of multi-Agent has become a significant part emergency response process. However, few existing works only focus on small number agents, which does not consider problem cooperation caused by increase agents impact emergencies. Therefore, framework for event-driven is proposed in this paper, includes three parts: event collection, sending, task execution. During execution, are divided into groups select leader group, while other group move with leader. Then, reinforcement learning algorithm Space Multi-Agent Deep Deterministic Policy Gradient (SMADDPG), used path planning. addition, state, action reward based Markov game designed, an environment presented as scenario. The experiment results show that method can shorten length path, improve interoperability between when occur, provide decision-making reference departments formulate plans.

Language: Английский

Coordinating dynamic signage for evacuation guidance: A multi-agent reinforcement learning approach integrating mesoscopic crowd modeling and fire propagation DOI

C. Xie,

Q. H. Chen, Bin Zhu

et al.

Chaos Solitons & Fractals, Journal Year: 2025, Volume and Issue: 194, P. 116246 - 116246

Published: March 9, 2025

Language: Английский

Citations

2

Mapping the knowledge domain of soft computing applications for emergency evacuation studies: A scientometric analysis and critical review DOI
Benbu Liang, C. Natalie van der Wal,

Kefan Xie

et al.

Safety Science, Journal Year: 2022, Volume and Issue: 158, P. 105955 - 105955

Published: Oct. 15, 2022

Language: Английский

Citations

29

Deep-reinforcement-learning-based UAV autonomous navigation and collision avoidance in unknown environments DOI Creative Commons
Fei Wang, Xiaoping Zhu, Zhou Zhou

et al.

Chinese Journal of Aeronautics, Journal Year: 2023, Volume and Issue: 37(3), P. 237 - 257

Published: Oct. 16, 2023

In some military application scenarios, Unmanned Aerial Vehicles (UAVs) need to perform missions with the assistance of on-board cameras when radar is not available and communication interrupted, which brings challenges for UAV autonomous navigation collision avoidance. this paper, an improved deep-reinforcement-learning algorithm, Deep Q-Network a Faster R-CNN model Data Deposit Mechanism (FRDDM-DQN), proposed. A (FR) introduced optimized obtain ability extract obstacle information from images, new replay memory (DDM) designed train agent better performance. During training, two-part training approach used reduce time spent on as well retraining scenario changes. order verify performance proposed method, series experiments, including test typical episodes conducted in 3D simulation environment. Experimental results show that trained by FRDDM-DQN has navigate autonomously avoid collisions, performs compared FR-DQN, FR-DDQN, FR-Dueling DQN, YOLO-based YDDM-DQN, original FR output-based FR-ODQN.

Language: Английский

Citations

22

Evacuation-path-selection model of real-time fire diffusion in urban underground complexes DOI
Xiaojuan Li, Weibin Chen, Rixin Chen

et al.

Computers & Industrial Engineering, Journal Year: 2023, Volume and Issue: 177, P. 109014 - 109014

Published: Jan. 18, 2023

Language: Английский

Citations

17

An enhanced deep deterministic policy gradient algorithm for intelligent control of robotic arms DOI Creative Commons
Ruyi Dong,

Junjie Du,

Yanan Liu

et al.

Frontiers in Neuroinformatics, Journal Year: 2023, Volume and Issue: 17

Published: Jan. 23, 2023

Aiming at the poor robustness and adaptability of traditional control methods for different situations, deep deterministic policy gradient (DDPG) algorithm is improved by designing a hybrid function that includes rewards superimposed on each other. In addition, experience replay mechanism DDPG also combining priority sampling uniform to accelerate DDPG's convergence. Finally, it verified in simulation environment can achieve accurate robot arm motion. The experimental results show converge shorter time, average success rate robotic end-reaching task as high 91.27%. Compared with original algorithm, has more robust environmental adaptability.

Language: Английский

Citations

10

Crowd Evacuation Under Real Data: A Crowd Congestion Control Method Based on Sensors and Knowledge Graph DOI

Jihao Duan,

Hong Liu,

Weifeng Gong

et al.

IEEE Sensors Journal, Journal Year: 2023, Volume and Issue: 23(8), P. 8923 - 8931

Published: March 15, 2023

Crowd congestion is an important factor affecting evacuation efficiency, and reasonable regulation of crowd in the process one way to improve efficiency. Since traditional simulation methods are based on hypothetical scenarios rules, simulations lack realism. To solve this problem reduce scale during evacuation, article proposes a control method sensors knowledge graph. First, we model scenario by extracting information from real scenes video realism simulation. Second, construct graph (CCKG) represent scenes, which improves model's ability characterize information. Then, gravity field generated graph, guided for evacuation. Finally, use constructed CCKG predict next moment regulate route real-time congestion. The experimental results show that using extract data can At same time, introduction characterization prediction effect model, circumvent large-scale congestion, efficiency

Language: Английский

Citations

9

Crowd evacuation path planning and simulation method based on deep reinforcement learning and repulsive force field DOI
Hongyue Wang, Liu Hong, Wenhao Li

et al.

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(4)

Published: Jan. 11, 2025

Language: Английский

Citations

0

A novel intelligent evacuation system under complex fire scenarios for multi-storey buildings based on computer vision DOI
Y. F. Lai, Xianglong Li, Long Yan

et al.

International Journal of Management Science and Engineering Management, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 17

Published: March 19, 2025

Language: Английский

Citations

0

Machine Learning-Enhanced Dynamic Path Decisions for Emergency Stewards in Emergency Evacuations DOI
Peng Yang,

Bozheng Zhang,

Kai Shi

et al.

Physica A Statistical Mechanics and its Applications, Journal Year: 2025, Volume and Issue: unknown, P. 130561 - 130561

Published: March 1, 2025

Language: Английский

Citations

0

An Exploration-Driven Framework for Path Planning in Complex Buildings Using Improved MADDPG DOI
Chong Zhang, Hong Liu, Wenhao Li

et al.

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112626 - 112626

Published: April 1, 2025

Language: Английский

Citations

0