Deep Reinforcement Learning Unmanned Aerial Vehicle Autonomous Cruise System with Fusion of Visual Information
Author:
Affiliation:
1. School of Automation, Wuhan University of Technology, China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3660043.3660158
Reference7 articles.
1. Wu Y Wu S M Zhang D J. 2013. Designing and Realizing of UAV Simulation System [J]. Applied Mechanics and Materials 2240(284-287): 3401-3404. https://doi.org/10.4028/www.scientific.net/AMM.284-287.3401.
2. Jonatan A Assia B Faiza B. 2023. Forest Fire Localization: From Reinforcement Learning Exploration to a Dynamic Drone Control [J]. Journal of Intelligent Robotic Systems 109(4). https://doi.org/10.1007/s10846-023-02004-z.
3. Multiple UAVs Path Planning Based on Deep Reinforcement Learning in Communication Denial Environment
4. Chen Y. 2023. Study on Flight Attitude Control of Four-rotor UAV [J]. Advances in Computer Signals and Systems 7(9). https://doi.org/ 10.23977/acss.2023.070904.
5. UAV 3D online track planning based on improved SAC algorithm
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