Journal of System Simulation ›› 2022, Vol. 34 ›› Issue (6): 1286-1295.doi: 10.16182/j.issn1004731x.joss.21-0112

• Modeling Theory and Methodology • Previous Articles     Next Articles

Multi-UAVs 3D Path Planning Method Based on Random Strategy Search

Sen Zhang(), Mengyan Zhang, Jingping Shao, Jiexin Pu   

  1. College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China
  • Received:2021-02-05 Revised:2021-06-16 Online:2022-06-30 Published:2022-06-16

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.

Key words: multi-UAVs, deep reinforcement learning algorithms, neural network, 3D path planning, energy consumption

CLC Number: