Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 69, Issue 2, Pages 1898-1908Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2021.3057005
Keywords
Cameras; Visual servoing; Servomotors; Robots; Task analysis; Aerospace electronics; Robot kinematics; Image-based visual servoing (IBVS); mobile robot; policy-based deep reinforcement learning (DRL); visibility constraints
Categories
Funding
- National Natural Science Foundation of China [61973275]
- NSFC-Zhejiang Joint Foundation for the Integration of Industrialization and Informatization [U1709213]
- Key R&D Foundation of Zhejiang [2020C01109]
- Fundamental Research Funds for the Provincial Universities of Zhejiang [RF-A2020004]
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The article explores the image-based visual servoing problem for mobile robots with visibility constraints using a policy-based deep reinforcement learning approach, presenting a method to solve feature-loss problem and improve servo efficiency.
In this article, the image-based visual servoing (IBVS) problem for mobile robots with visibility constraints is studied by using a policy-based deep reinforcement learning (DRL) approach. First, the classical IBVS (C-IBVS) method and its feature-loss problem are introduced. Then, a DRL-based IBVS method is presented to solve the feature-loss problem and improve the servo efficiency.Specifically, the formulation of the C-IBVS controller is inherited by the designed controller to ensure the analytical stability, and a policy-based DRL algorithm is proposed to design an adaptive law for tuning the controller gain in the continuous space, which can maintain the feature in the field of the view of the camera as well as improving the servo efficiency. Finally, the effectiveness of the proposed method is demonstrated by various comparative experiments.
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