Journal of System Simulation
Abstract
Abstract: In view of the difficulty of the traditional path planning method without energy consumption constraints to meet the emergency rescue requirements in the complex mountain operation environment, a three-dimensional path planning algorithm for multi-UAVs is proposed based on LSTM-DPPO(long short-term memory-distributed proximal policy optimization) framework. The LSTM long and short-term memory neural network is used to extract the important characteristic state information sequence of the multiple unmanned aerial vehicles in their respective flight process. After repeated iteration and updating, an optimal network parameter model is obtained. Combined with the energy consumption, the optimal 3D detection path is generated. Simulation experiments verify that the proposed method is more effective than the traditional path planning method and can plan the optimal detection path with the minimum energy consumption.
Recommended Citation
Zhang, Sen; Zhang, Mengyan; Shao, Jingping; and Pu, Jiexin
(2022)
"Multi-UAVs 3D Path Planning Method Based on Random Strategy Search,"
Journal of System Simulation: Vol. 34:
Iss.
6, Article 11.
DOI: 10.16182/j.issn1004731x.joss.21-0112
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol34/iss6/11
First Page
1286
Revised Date
2021-06-16
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.21-0112
Last Page
1295
CLC
TP183
Recommended Citation
Sen Zhang, Mengyan Zhang, Jingping Shao, Jiexin Pu. Multi-UAVs 3D Path Planning Method Based on Random Strategy Search[J]. Journal of System Simulation, 2022, 34(6): 1286-1295.
DOI
10.16182/j.issn1004731x.joss.21-0112
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