Indoor Emergency Path Planning Based on the Q-Learning Optimization Algorithm
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| Publicado en: | ISPRS International Journal of Geo-Information vol. 11, no. 1 (2022), p. 66 |
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| Autor principal: | |
| Otros Autores: | , , , , , |
| Publicado: |
MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/ijgi11010066 |2 doi | |
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| 045 | 2 | |b d20220101 |b d20221231 | |
| 084 | |a 231472 |2 nlm | ||
| 100 | 1 | |a Xu, Shenghua |u Chinese Academy of Surveying and Mapping, Beijing 100830, China; <email>xushh@casm.ac.cn</email> | |
| 245 | 1 | |a Indoor Emergency Path Planning Based on the Q-Learning Optimization Algorithm | |
| 260 | |b MDPI AG |c 2022 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The internal structure of buildings is becoming increasingly complex. Providing a scientific and reasonable evacuation route for trapped persons in a complex indoor environment is important for reducing casualties and property losses. In emergency and disaster relief environments, indoor path planning has great uncertainty and higher safety requirements. Q-learning is a value-based reinforcement learning algorithm that can complete path planning tasks through autonomous learning without establishing mathematical models and environmental maps. Therefore, we propose an indoor emergency path planning method based on the Q-learning optimization algorithm. First, a grid environment model is established. The discount rate of the exploration factor is used to optimize the Q-learning algorithm, and the exploration factor in the ε-greedy strategy is dynamically adjusted before selecting random actions to accelerate the convergence of the Q-learning algorithm in a large-scale grid environment. An indoor emergency path planning experiment based on the Q-learning optimization algorithm was carried out using simulated data and real indoor environment data. The proposed Q-learning optimization algorithm basically converges after 500 iterative learning rounds, which is nearly 2000 rounds higher than the convergence rate of the Q-learning algorithm. The SASRA algorithm has no obvious convergence trend in 5000 iterations of learning. The results show that the proposed Q-learning optimization algorithm is superior to the SARSA algorithm and the classic Q-learning algorithm in terms of solving time and convergence speed when planning the shortest path in a grid environment. The convergence speed of the proposed Q- learning optimization algorithm is approximately five times faster than that of the classic Q- learning algorithm. The proposed Q-learning optimization algorithm in the grid environment can successfully plan the shortest path to avoid obstacle areas in a short time. | |
| 653 | |a Environment models | ||
| 653 | |a Disaster relief | ||
| 653 | |a Casualties | ||
| 653 | |a Indoor environments | ||
| 653 | |a Planning | ||
| 653 | |a Algorithms | ||
| 653 | |a Maps | ||
| 653 | |a Evacuations & rescues | ||
| 653 | |a Mathematical models | ||
| 653 | |a Robots | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Machine learning | ||
| 653 | |a Emergencies | ||
| 653 | |a Convergence | ||
| 653 | |a Efficiency | ||
| 653 | |a Discount rates | ||
| 653 | |a Exploration | ||
| 653 | |a Iterative methods | ||
| 653 | |a Experiments | ||
| 653 | |a Optimization | ||
| 653 | |a Disasters | ||
| 653 | |a Learning | ||
| 653 | |a Evacuation routing | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Shortest path planning | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Path planning | ||
| 700 | 1 | |a Gu, Yang |u Nantong Export-Oriented Agricultural Comprehensive Development Zone, Nantong 226000, China | |
| 700 | 1 | |a Li, Xiaoyan |u School of Geomatics, Liaoning Technical University, Fuxin 123008, China; <email>472120757@stu.lntu.edu.cn</email> | |
| 700 | 1 | |a Chen, Cai |u School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China; <email>chencai@casm.ac.cn</email> (C.C.); <email>2021220411@jou.edu.cn</email> (Y.H.); <email>ss2021@jou.edu.cn</email> (Y.S.) | |
| 700 | 1 | |a Hu, Yingyi |u School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China; <email>chencai@casm.ac.cn</email> (C.C.); <email>2021220411@jou.edu.cn</email> (Y.H.); <email>ss2021@jou.edu.cn</email> (Y.S.) | |
| 700 | 1 | |a Yu, Sang |u School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China; <email>chencai@casm.ac.cn</email> (C.C.); <email>2021220411@jou.edu.cn</email> (Y.H.); <email>ss2021@jou.edu.cn</email> (Y.S.) | |
| 700 | 1 | |a Jiang, Wenxing |u Faculty of Geosciences and Environment Engineering, Southwest Jiontong University, Chengdu 611756, China; <email>jiangwenxing@stu.cdut.edu.cn</email> | |
| 773 | 0 | |t ISPRS International Journal of Geo-Information |g vol. 11, no. 1 (2022), p. 66 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2621283696/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/2621283696/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2621283696/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |