Visual servoing with deep reinforcement learning for rotor unmanned helicopter

International Journal of Advanced Robotic Systems

article

Chunyang Hu¹  Wenping Cao¹  Bin Ning¹ 

[1] School of Computer Engineering, Hubei University of Arts and Science

关键词: Visual servoing;    interaction matrix;    deep Q-network;    rotor unmanned helicopter;   

DOI  :  10.1177/17298806221084825

学科分类：社会科学、人文和艺术（综合）

来源: InTech

【 摘 要 】

Visual servoing is a key approach to achieve visual control for the rotor unmanned helicopter. The challenges of the inaccurate matrix estimation and the target loss restrict the performance of the visual servoing control systems. This work proposes a novel visual servoing controller using the deep Q-network to achieve an efficient matrix estimation. A deep Q-network learning agent learns a policy estimating the interaction matrix for visual servoing of a rotor unmanned helicopter using continuous observation. The observation includes a combination of feature errors. The current matrix and the desired matrix constitute the action space. A well-designed reward guides the deep Q-network agent to get a policy to generate a time-varying linear combination between the current matrix and the desired matrix. Then, the interaction matrix is calculated by the linear combination. The potential mapping between the observation and the interaction matrix is learned by cascading the deep neural network layers. Experimental results show that the proposed method achieves faster convergence and lower target loss probability in tracking than the visual servoing methods with the fixed parameter.

【 授权许可】

CC BY