1.
Wang, Zhipeng; Ng, Soon Xin; El-Hajjar, Mohammed
A 3D Spatial Information Compression Based Deep Reinforcement Learning Technique for UAV Path Planning in Cluttered Environments Journal Article
In: IEEE Open Journal of Vehicular Technology, vol. 6, pp. 647–661, 2025, ISSN: 2644-1330.
Abstract | Links | BibTeX | Tags: 3D path planning, 3D spatial information compression, Autonomous aerial vehicles, Classification algorithms, Convergence, deep reinforcement learning, Navigation, Path planning, Principal component analysis, Search problems, Solid modeling, Three-dimensional displays, Training, training efficiency, unmanned aerial vehicles
@article{wang_3d_2025,
title = {A 3D Spatial Information Compression Based Deep Reinforcement Learning Technique for UAV Path Planning in Cluttered Environments},
author = {Zhipeng Wang and Soon Xin Ng and Mohammed El-Hajjar},
url = {https://ieeexplore.ieee.org/document/10878448},
doi = {10.1109/OJVT.2025.3540174},
issn = {2644-1330},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {IEEE Open Journal of Vehicular Technology},
volume = {6},
pages = {647–661},
abstract = {Unmanned aerial vehicles (UAVs) can be considered in many applications, such as wireless communication, logistics transportation, agriculture and disaster prevention. The flexible maneuverability of UAVs also means that the UAV often operates in complex 3D environments, which requires efficient and reliable path planning system support. However, as a limited resource platform, the UAV systems cannot support highly complex path planning algorithms in lots of scenarios. In this paper, we propose a 3D spatial information compression (3DSIC) based deep reinforcement learning (DRL) algorithm for UAV path planning in cluttered 3D environments. Specifically, the proposed algorithm compresses the 3D spatial information to 2D through 3DSIC, and then combines the compressed 2D environment information with the current UAV layer spatial information to train UAV agents for path planning using neural networks. Additionally, the proposed 3DSIC is a plug and use module that can be combined with various DRL frameworks such as Deep Q-Network (DQN) and deep deterministic policy gradient (DDPG). Our simulation results show that the training efficiency of 3DSIC-DQN is 4.028 times higher than that directly implementing DQN in a 100 textbackslashtimes 100 textbackslashtimes 50 3D cluttered environment. Furthermore, the training efficiency of 3DSIC-DDPG is 3.9 times higher than the traditional DDPG in the same environment. Moreover, 3DSIC combined with fast recurrent stochastic value gradient (FRSVG), which can be considered as the most state-of-the-art DRL algorithm for UAV path planning, exhibits 2.35 times faster training speed compared with the original FRSVG algorithm.},
keywords = {3D path planning, 3D spatial information compression, Autonomous aerial vehicles, Classification algorithms, Convergence, deep reinforcement learning, Navigation, Path planning, Principal component analysis, Search problems, Solid modeling, Three-dimensional displays, Training, training efficiency, unmanned aerial vehicles},
pubstate = {published},
tppubtype = {article}
}
Unmanned aerial vehicles (UAVs) can be considered in many applications, such as wireless communication, logistics transportation, agriculture and disaster prevention. The flexible maneuverability of UAVs also means that the UAV often operates in complex 3D environments, which requires efficient and reliable path planning system support. However, as a limited resource platform, the UAV systems cannot support highly complex path planning algorithms in lots of scenarios. In this paper, we propose a 3D spatial information compression (3DSIC) based deep reinforcement learning (DRL) algorithm for UAV path planning in cluttered 3D environments. Specifically, the proposed algorithm compresses the 3D spatial information to 2D through 3DSIC, and then combines the compressed 2D environment information with the current UAV layer spatial information to train UAV agents for path planning using neural networks. Additionally, the proposed 3DSIC is a plug and use module that can be combined with various DRL frameworks such as Deep Q-Network (DQN) and deep deterministic policy gradient (DDPG). Our simulation results show that the training efficiency of 3DSIC-DQN is 4.028 times higher than that directly implementing DQN in a 100 textbackslashtimes 100 textbackslashtimes 50 3D cluttered environment. Furthermore, the training efficiency of 3DSIC-DDPG is 3.9 times higher than the traditional DDPG in the same environment. Moreover, 3DSIC combined with fast recurrent stochastic value gradient (FRSVG), which can be considered as the most state-of-the-art DRL algorithm for UAV path planning, exhibits 2.35 times faster training speed compared with the original FRSVG algorithm.