The Closed-Loop Construction of Soft Robot Perception, Decision-Making, and Control Under the Cyber-Physical Systems (CPS) Framework
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Keywords
cyber-physical system (CPS), soft robot, machine learning, human-robot collaboration
Abstract
This paper aims to explore the “Sense-Plan-Act” closed-loop construction of soft robot within the Cyber-Physical System (CPS) framework. The soft robot is deconstructed into the perception layer, decision layer, and control layer, and the organic combination of these three layers to form an effective closed-loop system is elaborated. Initially, within the perception layer, key challenges and advanced technologies for soft robot state acquisition are discussed, including the integration of multi-modal sensors and data fusion methods. Furthermore, the application of soft sensors to address the perception challenges arising from complex deformations is examined. Subsequently, within the decision layer, the architectures and algorithms for information processing, state estimation, and intelligent decision-making are analyzed, with a particular focus on leveraging artificial intelligence (AI) and machine learning (ML) technologies to extract meaningful information from noisy data and enable robust decision-making. Finally, in the control layer, the actuation system and the challenges and progress of its practical implementation will be explored. This includes adaptive control strategy and human-robot collaboration methods, which are crucial for ensuring the precise and safe operation of the soft robot in complex environments. Through the establishment of this framework, this review aims to provide comprehensive theoretical support and guidance for the research and application of soft robot within a CPS context.
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