A survey Of learning-Based control of robotic visual servoing systems
Major difficulties and challenges of modern robotics systems focus on how to give robots self-learning and self-decision-making ability. Visual servoing control strategy is an important strategy of robotic systems to perceive the environment via the vision. The vision can guide new robotic systems t...
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| Published in | Journal of the Franklin Institute Vol. 359; no. 1; pp. 556 - 577 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elmsford
Elsevier Ltd
01.01.2022
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Major difficulties and challenges of modern robotics systems focus on how to give robots self-learning and self-decision-making ability. Visual servoing control strategy is an important strategy of robotic systems to perceive the environment via the vision. The vision can guide new robotic systems to complete more complicated tasks in complex working environments. This survey aims at describing the state-of-the-art learning-based algorithms, especially those algorithms that combine with model predictive control (MPC) used in visual servoing systems, and providing some pioneering and advanced references with several numerical simulations. The general modeling methods of visual servo and the influence of traditional control strategies on robotic visual servoing systems are introduced. The advantages of introducing neural-network-based algorithms and reinforcement-learning-based algorithms into the systems are discussed. Finally, according to the existing research progress and references, the future directions of robotic visual servoing systems are summarized and prospected. |
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| AbstractList | Major difficulties and challenges of modern robotics systems focus on how to give robots self-learning and self-decision-making ability. Visual servoing control strategy is an important strategy of robotic systems to perceive the environment via the vision. The vision can guide new robotic systems to complete more complicated tasks in complex working environments. This survey aims at describing the state-of-the-art learning-based algorithms, especially those algorithms that combine with model predictive control (MPC) used in visual servoing systems, and providing some pioneering and advanced references with several numerical simulations. The general modeling methods of visual servo and the influence of traditional control strategies on robotic visual servoing systems are introduced. The advantages of introducing neural-network-based algorithms and reinforcement-learning-based algorithms into the systems are discussed. Finally, according to the existing research progress and references, the future directions of robotic visual servoing systems are summarized and prospected. |
| Author | Jin, Zhehao Yu, Li Yang, Fuwen Liu, Andong Wu, Jinhui |
| Author_xml | – sequence: 1 givenname: Jinhui surname: Wu fullname: Wu, Jinhui organization: College of Information Engineering, Zhejiang University of Technology, Hangzhou, People’s Republic of China – sequence: 2 givenname: Zhehao surname: Jin fullname: Jin, Zhehao organization: College of Information Engineering, Zhejiang University of Technology, Hangzhou, People’s Republic of China – sequence: 3 givenname: Andong orcidid: 0000-0003-2445-8457 surname: Liu fullname: Liu, Andong email: lad@zjut.edu.cn organization: College of Information Engineering, Zhejiang University of Technology, Hangzhou, People’s Republic of China – sequence: 4 givenname: Li surname: Yu fullname: Yu, Li organization: College of Information Engineering, Zhejiang University of Technology, Hangzhou, People’s Republic of China – sequence: 5 givenname: Fuwen surname: Yang fullname: Yang, Fuwen organization: School of Engineering and Built Environment, Griffith University (Gold Coast), Southport, QLD 4222, Australia |
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| SubjectTerms | Algorithms Control systems Decision making Learning Machine learning Mathematical models Neural networks Predictive control Robot control Robotics Robots Servocontrol Task complexity Vision Visual control |
| Title | A survey Of learning-Based control of robotic visual servoing systems |
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