Deep Reinforcement Learning Assisted UAV Path Planning Relying on Cumulative Reward Mode and Region Segmentation
In recent years, unmanned aerial vehicles (UAVs) have been considered for many applications, such as disaster prevention and control, logistics and transportation, and wireless communication. Most UAVs need to be manually controlled using remote control, which can be challenging in many environments...
Saved in:
Main Authors: | Zhipeng Wang, Soon Xin Ng, Mohammed EI-Hajjar |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Open Journal of Vehicular Technology |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10531630/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Optimizing Autonomous Multi-UAV Path Planning for Inspection Missions: A Comparative Study of Genetic and Stochastic Hill Climbing Algorithms
by: Faten Aljalaud, et al.
Published: (2024-12-01) -
Research on power efficient autonomous UAV navigation algorithm: an edge intelligence driven approach
by: Chunmin LIN, et al.
Published: (2021-06-01) -
Robot Dynamic Path Planning Based on Prioritized Experience Replay and LSTM Network
by: Hongqi Li, et al.
Published: (2025-01-01) -
Integrated perception-communication-logistics multi-objective oriented path planning for emergency UAVs
by: XU Yunpeng, et al.
Published: (2024-04-01) -
Event-Based Visual/Inertial Odometry for UAV Indoor Navigation
by: Ahmed Elamin, et al.
Published: (2024-12-01)