A Non-Rigid Hierarchical Discrete Grid Structure and Its Application to UAVs Conflict Detection and Path Planning

Wu, Xiangyu; Lei, Yi; Tong, Xiaochong; Zhang, Yongsheng; He, Li; Qiu, Chunping; Guo, Chuangxin; Sun, Yuekun; Lai, Guangling

doi:10.1109/taes.2022.3170323

Cited by 5 publications

(1 citation statement)

References 38 publications

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“…In order to improve the stability of the algorithm and the efficiency of the task completion during the task execution, designing a new alternative framework for UAV motion performance can effectively reduce the task execution time of UAVs while improving the robustness of the algorithmic framework [ 17 , 18 ]. A non-rigid hierarchical discrete grid structure was proposed in [ 19 ] to achieve path planning of UAVs in 3D space. These optimization algorithms based on traditional algorithms were all based on converting the UAV path planning problem into a path optimization problem and solving the optimization model to obtain the optimal flight path.…”

Section: Introductionmentioning

confidence: 99%

Path Planning Research of a UAV Base Station Searching for Disaster Victims’ Location Information Based on Deep Reinforcement Learning

Zhao¹,

Gan²,

Liang³

et al. 2022

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Aiming at the path planning problem of unmanned aerial vehicle (UAV) base stations when performing search tasks, this paper proposes a Double DQN-state splitting Q network (DDQN-SSQN) algorithm that combines state splitting and optimal state to complete the optimal path planning of UAV based on the Deep Reinforcement Learning DDQN algorithm. The method stores multidimensional state information in categories and uses targeted training to obtain optimal path information. The method also references the received signal strength indicator (RSSI) to influence the reward received by the agent, and in this way reduces the decision difficulty of the UAV. In order to simulate the scenarios of UAVs in real work, this paper uses the Open AI Gym simulation platform to construct a mission system model. The simulation results show that the proposed scheme can plan the optimal path faster than other traditional algorithmic schemes and has a greater advantage in the stability and convergence speed of the algorithm.

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Section: Introductionmentioning

confidence: 99%