Soft robots have recently attracted increased attention because their characteristics of low‐cost fabrication, durability, and deformability make them uniquely suited for applications in bio‐integrated systems. Being fundamentally different from traditional rigid robots, soft robots exhibit properties of infinite degrees of freedom (DOF) and nonlinear materials properties that require innovations in control systems. With the rapid development of materials science, robotics, and artificial intelligence, the diversification of actuator mechanisms and algorithms has enabled a wide range of unique control strategies. This review summarizes the basics of actuator mechanisms and control strategies, including open‐loop control, closed‐loop control, and autonomous control, and discusses their implementation from diversified perspectives. Control strategies are evaluated based on their compatibility with materials sets, application goals, and implementation route. The emerging directions are forecasted from the perspectives of interfacing between controller and actuator, underactuated control strategies, and implementation of artificial intelligence (AI).
Reconfigurable morphing surfaces provide new opportunities for advanced human-machine interfaces and bio-inspired robotics. Morphing into arbitrary surfaces on demand requires a device with a sufficiently large number of actuators and an inverse control strategy. Developing compact, efficient control interfaces and algorithms is vital for broader adoption. In this work, we describe a passively addressed robotic morphing surface (PARMS) composed of matrix-arranged ionic actuators. To reduce the complexity of the physical control interface, we introduce passive matrix addressing. Matrix addressing allows the control of N 2 independent actuators using only 2 N control inputs, which is substantially lower than traditional direct addressing ( N 2 control inputs). Using machine learning with finite element simulations for training, our control algorithm enables real-time, high-precision forward and inverse control, allowing PARMS to dynamically morph into arbitrary achievable predefined surfaces on demand. These innovations may enable the future implementation of PARMS in wearables, haptics, and augmented reality/virtual reality.
Flatness plays an important role in the assembly process of aircraft engine rotors, especially the flange plane that is annular between the rotor shaft and cone. It determines the contact stiffness and assembly accuracy of the engine rotor. However, a specific method to measure the annular plane is lacking, especially in the on-machine conditions and for on-machine measurement. As a low-cost, high-precision and easy-to-use measurement method, error separating techniques (ESTs) are widely used for flatness measurement. However, when applying them, the initial error and probe fixture tilt error cannot be eliminated at the same time. This paper proposes a novel design of an on-machine measurement system and method for annular plane flatness, which can be applied to the assembly of aeroengine rotors. The novel method proposed in this paper combines the advantages of ESTs and utilizes the properties of the annular plane to eliminate the two main errors successfully. At the same time as processing data, the equivalent homogenization processing and 3D least squares method are introduced to further improve the credibility of the data. According to the method above, in order to meet the needs of on-machine measurement, a novel device that can adjust two axes and that is equipped with an indexing plate is designed. The measuring system uses the Keyence GT2-H12K contact probe, which can level itself by using the probe values to control two axes. Finally, the annular plane was measured by a three-coordinate measuring machine (CMM). The result of the CMM is 47.6 µm, which is close to the 45.9 µm measured by the novel on-machine conditions method, which proves the reliability of the method proposed in this paper.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.