Combined perception, control, and learning for teleoperation: key technologies, applications, and challenges

Abstract

Teleoperation provides a promising way for human–robot collaboration in the unknown or unstructured environments to perform a cooperative task. It enables humans to complete a task at a remote side and combines both the human's intelligence and the robots’ capabilities in a collaborative task. Therefore, it is necessary to conduct cross researches in terms of robotics, artificial intelligence, sensors, and mechatronics. This study covers the state-of-the-arts research in terms of perception, control, and learning. In this review, key technologies about information perception mechanism, control algorithms, human–robot interface, and robot learning strategies for teleoperation are introduced. Then, a comprehensive survey is summarised in representative applications in teleoperation, and the existing challenges and potential directions of the development are discussed.

Keywords

• artificial intelligence
• learning (artificial intelligence)
• human-robot interaction
• telerobotics
• mobile robots
• mechatronics
• cross researches
• artificial intelligence
• state-of-the-arts research
• information perception mechanism
• control algorithms
• human–robot interface
• robot learning strategies
• teleoperation
• representative applications
• existing challenges
• combined perception
• human–robot collaboration
• unknown environments
• unstructured environments
• robots
• collaborative task