视觉伺服
人工智能
计算机科学
光学(聚焦)
机器人
机器人学
人工神经网络
强化学习
控制工程
计算机视觉
机器学习
工程类
光学
物理
作者
Jinhui Wu,Zhehao Jin,Andong Liu,Li Yu,Fang Yang
标识
DOI:10.1016/j.jfranklin.2021.11.009
摘要
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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