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[HTML][HTML] A multi-auv maritime target search method for moving and invisible objects based on multi-agent deep reinforcement learning
Target search for moving and invisible objects has always been considered a challenge, as
the floating objects drift with the flows. This study focuses on target search by multiple …
the floating objects drift with the flows. This study focuses on target search by multiple …
Path Planning for Fully Autonomous UAVs-A Taxonomic Review and Future Perspectives
Autonomous Unmanned Aerial Vehicles (UAVs) rely on advanced path planning to operate
independently, especially in unfamiliar settings without human intervention. The process …
independently, especially in unfamiliar settings without human intervention. The process …
Real-time UAV path planning based on LSTM network
J Zhang, Y Guo, L Zheng, Q Yang… - Journal of Systems …, 2024 - ieeexplore.ieee.org
To address the shortcomings of single-step decision making in the existing deep
reinforcement learning based unmanned aerial vehicle (UAV) real-time path planning …
reinforcement learning based unmanned aerial vehicle (UAV) real-time path planning …
Globally guided deep v-network-based motion planning algorithm for fixed-wing unmanned aerial vehicles
H Du, M You, X Zhao - Sensors, 2024 - mdpi.com
Fixed-wing UAVs have shown great potential in both military and civilian applications.
However, achieving safe and collision-free flight in complex obstacle environments is still a …
However, achieving safe and collision-free flight in complex obstacle environments is still a …
Application of deep reinforcement learning in UAVs: a review
R Wang, L Xu - 2022 34th Chinese Control and Decision …, 2022 - ieeexplore.ieee.org
Deep reinforcement learning is one of the most important branches in the field of artificial
intelligence. It has strong high-latitude data processing capabilities and is mainly used to …
intelligence. It has strong high-latitude data processing capabilities and is mainly used to …
Multi-granularity coverage criteria for deep reinforcement learning systems
Y Shi, B Yin, Z Zheng - Journal of Systems and Software, 2024 - Elsevier
Deep reinforcement learning (DRL) systems are progressively being deployed in safety-and
security-critical domains, such as self-driving cars and unmanned aerial vehicles, raising …
security-critical domains, such as self-driving cars and unmanned aerial vehicles, raising …
[HTML][HTML] Maximizing UAV Coverage in Maritime Wireless Networks: A Multiagent Reinforcement Learning Approach
Q Wu, Q Liu, Z Wu, J Zhang - Future Internet, 2023 - mdpi.com
In the field of ocean data monitoring, collaborative control and path planning of unmanned
aerial vehicles (UAVs) are essential for improving data collection efficiency and quality. In …
aerial vehicles (UAVs) are essential for improving data collection efficiency and quality. In …
Path Planning for Parafoil Airdrop System Based on TD3 Algorithm: Reward Sha** with Potential Field
F Tao, H Sun, B Lv, Q Sun, Z Liu - Chinese Intelligent Systems Conference, 2023 - Springer
The flight time of the parafoil airdrop system is limited by the release altitude, and how to
accurately guide the parafoil landing within the limited control time has become a research …
accurately guide the parafoil landing within the limited control time has become a research …
Autonomous Uav Path Optimization Using Mono and Multi-Objective Evolutionary Algorithms for Effective Data Retrieval in Cache-Enabled Mobile Ad-Hoc Wsns
UB Chaudhry, DC Phillips - Available at SSRN 4931445 - papers.ssrn.com
Retrieving data from nodes in mobile ad-hoc wireless sensor networks presents a persistent
challenge. Traditional methods depend on specialized routing protocols tailored for these …
challenge. Traditional methods depend on specialized routing protocols tailored for these …