UAV Collision Avoidance in Unknown Scenarios with Causal Representation Disentanglement DOI Creative Commons
Zhun Fan,

Zihao Xia,

Che Lin

et al.

Drones, Journal Year: 2024, Volume and Issue: 9(1), P. 10 - 10

Published: Dec. 25, 2024

Deep reinforcement learning (DRL) has significantly advanced online path planning for unmanned aerial vehicles (UAVs). Nonetheless, DRL-based methods often encounter reduced performance when dealing with unfamiliar scenarios. This decline is mainly caused by the presence of non-causal and domain-specific elements within visual representations, which negatively impact policies. Present techniques generally depend on predefined augmentation or regularization intended to direct model toward identifying causal domain-invariant components, thereby enhancing model’s ability generalize. However, these manually crafted approaches are intrinsically constrained in their coverage do not consider entire spectrum possible scenarios, resulting less effective new environments. Unlike prior studies, this work prioritizes representation presents a novel method disentanglement. The approach successfully distinguishes between data. By concentrating aspects during policy phase, factors minimized, improving generalizability DRL models. Experimental results demonstrate that our technique achieves reliable navigation collision avoidance unseen surpassing state-of-the-art deep algorithms.

Language: Английский

Autonomous obstacle avoidance and target tracking of UAV: Transformer for observation sequence in reinforcement learning DOI
Weilai Jiang,

Tianqing Cai,

Guoqiang Xu

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 290, P. 111604 - 111604

Published: March 2, 2024

Language: Английский

Citations

8
A Multi-Area Task Path-Planning Algorithm for Agricultural Drones Based on Improved Double Deep Q-Learning Net DOI Creative Commons
Jian Li, Weijian Zhang,

Junfeng Ren

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(8), P. 1294 - 1294

Published: Aug. 5, 2024

With the global population growth and increasing food demand, development of precision agriculture has become particularly critical. In agriculture, accurately identifying areas nitrogen stress in crops planning precise fertilization paths are crucial. However, traditional coverage path-planning (CPP) typically considers only single-area tasks overlooks multi-area CPP. To address this problem, study proposed a Regional Framework for Coverage Path-Planning Precision Fertilization (RFCPPF) crop protection UAVs tasks. This framework includes three modules: spatial distribution extraction, environmental map construction, path-planning. Firstly, Sentinel-2 remote-sensing images processed using Google Earth Engine (GEE) platform, Green Normalized Difference Vegetation Index (GNDVI) is calculated to extract stress. A constructed guide multiple UAV agents. Subsequently, improvements based on Double Deep Q Network (DDQN) introduced, incorporating Long Short-Term Memory (LSTM) dueling network structures. Additionally, multi-objective reward function state action selection strategy suitable area plant operations designed. Simulation experiments verify superiority method reducing redundant improving efficiency. The improved DDQN achieved an overall step count that 60.71% MLP-DDQN 90.55% Breadth-First Search–Boustrophedon Algorithm (BFS-BA). total repeated rate was reduced by 7.06% compared 8.82% BFS-BA.

Language: Английский

Citations

4
Pursuit–Evasion Game Theoretic Decision Making for Collision Avoidance in Automated Vehicles DOI
Liang Shan, Yan Ping,

Haidong Feng

et al.

Dynamic Games and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

Language: Английский

Citations

0
Secure task offloading strategy optimization of UAV-aided outdoor mobile high-definition live streaming DOI Creative Commons
Ming Yan, Yuxuan Zhang, Chien Aun Chan

et al.

Chinese Journal of Aeronautics, Journal Year: 2025, Volume and Issue: unknown, P. 103454 - 103454

Published: March 1, 2025

Language: Английский

Citations

0
MFR-YOLOv10:Object detection in UAV-taken images based on multilayer feature reconstruction network DOI Creative Commons

M. Tian,

Mengqiu cui, Zhi‐Min Chen

et al.

Chinese Journal of Aeronautics, Journal Year: 2025, Volume and Issue: unknown, P. 103456 - 103456

Published: March 1, 2025

Language: Английский

Citations

0
A Survey on Obstacle Detection and Avoidance Methods for UAVs DOI Creative Commons
Ahmad Merei, Hamid Mcheick, Alia Ghaddar

et al.

Drones, Journal Year: 2025, Volume and Issue: 9(3), P. 203 - 203

Published: March 12, 2025

Obstacle avoidance is crucial for the successful completion of UAV missions. Static and dynamic obstacles, such as trees, buildings, flying birds, or other UAVs, can threaten these As a result, safe path planning essential, particularly missions involving multiple UAVs. Collision-free paths be designed in either 2D 3D environments, depending on scenario. This study provides an overview recent advancements obstacle These methods are compared based various criteria, including techniques, types, environment explored, sensor equipment, map statuses. Additionally, this paper includes process addressing detection reviews evolution (ODA) techniques UAVs over past decade.

Language: Английский

Citations

0
Integrating just-in-time expansion primitives and an adaptive variable-step-size mechanism for feasible path planning of fixed-wing UAVs DOI Creative Commons
Hui Gao,

Yuhong Jia,

Qingyang Qin

et al.

Chinese Journal of Aeronautics, Journal Year: 2025, Volume and Issue: unknown, P. 103566 - 103566

Published: May 1, 2025

Language: Английский

Citations

0
VizNav: A Modular Off-Policy Deep Reinforcement Learning Framework for Vision-Based Autonomous UAV Navigation in 3D Dynamic Environments DOI Creative Commons
Fadi AlMahamid, Katarina Grolinger

Drones, Journal Year: 2024, Volume and Issue: 8(5), P. 173 - 173

Published: April 27, 2024

Unmanned aerial vehicles (UAVs) provide benefits through eco-friendliness, cost-effectiveness, and reduction of human risk. Deep reinforcement learning (DRL) is widely used for autonomous UAV navigation; however, current techniques often oversimplify the environment or impose movement restrictions. Additionally, most vision-based systems lack precise depth perception, while range finders a limited environmental overview, LiDAR energy-intensive. To address these challenges, this paper proposes VizNav, modular DRL-based framework navigation in dynamic 3D environments without imposing conventional mobility constraints. VizNav incorporates Twin Delayed Deterministic Policy Gradient (TD3) algorithm with Prioritized Experience Replay Importance Sampling (PER) to improve performance continuous action spaces mitigate overestimations. employs map images (DMIs) enhance visual by accurately estimating objects’ information, thereby improving obstacle avoidance. Empirical results show that leveraging TD3, improves navigation, inclusion PER DMI further boosts performance. Furthermore, deployment across various experimental settings confirms its flexibility adaptability. The framework’s architecture separates agent’s from training process, facilitating integration DRL algorithms, simulation environments, reward functions. This modularity creates potential influence RL systems, including robotics control vehicles.

Language: Английский

Citations

1
Review of vision-based reinforcement learning for drone navigation DOI

Anas Aburaya,

Hazlina Selamat, Mohd Taufiq Muslim

et al.

International Journal of Intelligent Robotics and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: June 28, 2024

Language: Английский

Citations

1
Autonomous UAV navigation using deep learning-based computer vision frameworks: A systematic literature review DOI Creative Commons

Aditya Vardhan Reddy Katkuri,

Hakka Madan,

Narendra Khatri

et al.

Array, Journal Year: 2024, Volume and Issue: 23, P. 100361 - 100361

Published: Aug. 29, 2024

Language: Английский

Citations

1