Autonomous Obstacle Avoidance and Target Tracking of UAV Based on Deep Reinforcement Learning

When using deep reinforcement learning algorithm to complete Unmanned Aerial Vehicle (UAV) autonomous obstacle avoidance and target tracking tasks, there are often some problems such as slow convergence speed and low success rate. Therefore, this paper proposes a new deep reinforcement learning algo...

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Vydané v:Journal of intelligent & robotic systems Ročník 104; číslo 4; s. 60
Hlavní autori: Xu, Guoqiang, Jiang, Weilai, Wang, Zhaolei, Wang, Yaonan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.04.2022
Springer
Springer Nature B.V
Predmet:
Algorithms
Analysis
Artificial Intelligence
Control
Convergence
Data mining
Deep learning
Drone aircraft
Electrical Engineering
Engineering
label V
Machine learning
Mechanical Engineering
Mechatronics
Obstacle avoidance
Pools
Regular Paper
Robotics
Topical collection on Robotics Vision and Intelligent Control
Tracking
Unmanned aerial vehicles
Unmanned Aerial Vehicle (UAV)
Autonomous obstacle avoidance
Deep reinforcement learning
Target tracking
Continuous control
ISSN:0921-0296, 1573-0409
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Popis
Shrnutí:When using deep reinforcement learning algorithm to complete Unmanned Aerial Vehicle (UAV) autonomous obstacle avoidance and target tracking tasks, there are often some problems such as slow convergence speed and low success rate. Therefore, this paper proposes a new deep reinforcement learning algorithm, namely Multiple Pools Twin Delay Deep Deterministic Policy Gradient (MPTD3) algorithm. Firstly, the state space and action space of UAV are established as continuous models, which is closer to engineering practice than discrete models. Then, multiple experience pools mechanism and gradient truncation are designed to improve the convergence of the algorithm. Furthermore, the generalization ability of the algorithm is obtained by giving UAV environmental perception ability. Experimental results verify the effectiveness of the proposed method.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-022-01601-8