Autonomous obstacle avoidance of UAV based on deep reinforcement learning

In the intelligent unmanned systems, unmanned aerial vehicle (UAV) obstacle avoidance technology is the core and primary condition. Traditional algorithms are not suitable for obstacle avoidance in complex and changeable environments based on the limited sensors on UAVs. In this article, we use an e...

Full description

Saved in:
Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2022-01, Vol.42 (4), p.3323
Main Authors: Yang, Songyue, Yu, Guizhen, Meng, Zhijun, Wang, Zhangyu, Li, Han
Format: Article
Language:English
Subjects:
Algorithms
Convergence
Decision making
Deep learning
Machine learning
Obstacle avoidance
Physical simulation
Unmanned aerial vehicles
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the intelligent unmanned systems, unmanned aerial vehicle (UAV) obstacle avoidance technology is the core and primary condition. Traditional algorithms are not suitable for obstacle avoidance in complex and changeable environments based on the limited sensors on UAVs. In this article, we use an end-to-end deep reinforcement learning (DRL) algorithm to achieve the UAV autonomously avoid obstacles. For the problem of slow convergence in DRL, a Multi-Branch (MB) network structure is proposed to ensure that the algorithm can get good performance in the early stage; for non-optimal decision-making problems caused by overestimation, the Revise Q-value (RQ) algorithm is proposed to ensure that the agent can choose the optimal strategy for obstacle avoidance. According to the flying characteristics of the rotor UAV, we build a V-Rep 3D physical simulation environment to test the obstacle avoidance performance. And experiments show that the improved algorithm can accelerate the convergence speed of agent and the average return of the round is increased by 25%.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-211192