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 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. (C) 2021 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Automated summary

The major difficulties and challenges of modern robotics systems lie in equipping robots with self-learning and self-decision-making abilities. Visual servoing control strategy is an important approach for robots to perceive the environment through vision. This survey focuses on describing state-of-the-art learning-based algorithms, particularly those that combine model predictive control (MPC) in visual servoing systems, and provides pioneering references and advanced numerical simulations. The survey introduces general modeling methods of visual servo and the impact of traditional control strategies on robotic visual servoing systems. It discusses the advantages of incorporating neural-network-based algorithms and reinforcement-learning-based algorithms into the systems. Finally, the survey summarizes and forecasts the future directions of robotic visual servoing systems based on existing research progress and references.