Abstract:Soft robots are primarily composed of soft materials that can allow for mechanically robust maneuvers that are not typically possible with conventional rigid robotic systems. However, owing to the current limitations in simulation, design and control of soft robots often involve a painstaking trial. With the ultimate goal of a computational framework for soft robotic engineering, here we introduce a numerical simulation tool for limbed soft robots that draws inspiration from discrete differential geometry base… Show more
“…Simulation techniques build upon these modeling methods as in the finite-element methods (FEMs), which construct continuum robot structures using a chain of rigid elements connected with tunable spring-damper mechanisms (Chenevier et al, 2018;Goury and Duriez, 2018). Numerical approaches using voxel-based representations (Hiller and Lipson, 2014) and discrete differential geometries (DDGs) (Huang et al, 2020) improve the computation time of soft robotic simulations at the expense of nonlinear dynamics precision. These models and simulation tools typically allow the implementation of static and dynamic controllers for continuum robots on a larger scale (Thuruthel et al, 2018).…”
Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can directly and non-invasively access confined and hard-to-reach spaces in the human body. For such potential biomedical applications, the adaptivity of the robot control is essential to ensure the continuity of the operations, as task environment conditions show dynamic variations that can alter the robot’s motion and task performance. The applicability of the conventional modeling and control methods is further limited for soft robots at the small-scale owing to their kinematics with virtually infinite degrees of freedom, inherent stochastic variability during fabrication, and changing dynamics during real-world interactions. To address the controller adaptation challenge to dynamically changing task environments, we propose using a probabilistic learning approach for a millimeter-scale magnetic walking soft robot using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme by finding the gait controller parameters while optimizing the stride length of the walking soft millirobot using a small number of physical experiments. To demonstrate the controller adaptation, we test the walking gait of the robot in task environments with different surface adhesion and roughness, and medium viscosity, which aims to represent the possible conditions for future robotic tasks inside the human body. We further utilize the transfer of the learned GP parameters among different task spaces and robots and compare their efficacy on the improvement of data-efficient controller learning.
“…Simulation techniques build upon these modeling methods as in the finite-element methods (FEMs), which construct continuum robot structures using a chain of rigid elements connected with tunable spring-damper mechanisms (Chenevier et al, 2018;Goury and Duriez, 2018). Numerical approaches using voxel-based representations (Hiller and Lipson, 2014) and discrete differential geometries (DDGs) (Huang et al, 2020) improve the computation time of soft robotic simulations at the expense of nonlinear dynamics precision. These models and simulation tools typically allow the implementation of static and dynamic controllers for continuum robots on a larger scale (Thuruthel et al, 2018).…”
Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can directly and non-invasively access confined and hard-to-reach spaces in the human body. For such potential biomedical applications, the adaptivity of the robot control is essential to ensure the continuity of the operations, as task environment conditions show dynamic variations that can alter the robot’s motion and task performance. The applicability of the conventional modeling and control methods is further limited for soft robots at the small-scale owing to their kinematics with virtually infinite degrees of freedom, inherent stochastic variability during fabrication, and changing dynamics during real-world interactions. To address the controller adaptation challenge to dynamically changing task environments, we propose using a probabilistic learning approach for a millimeter-scale magnetic walking soft robot using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme by finding the gait controller parameters while optimizing the stride length of the walking soft millirobot using a small number of physical experiments. To demonstrate the controller adaptation, we test the walking gait of the robot in task environments with different surface adhesion and roughness, and medium viscosity, which aims to represent the possible conditions for future robotic tasks inside the human body. We further utilize the transfer of the learned GP parameters among different task spaces and robots and compare their efficacy on the improvement of data-efficient controller learning.
“…Smart material-based actuators have attracted much research interest owing to their envisioned applications in the fields of soft robots, [1,2] artificial muscles, [3,4] motors, [5,6] and energy generators. [7,8] Practically, an artificial actuator remotely controlled by light is preferable for realizing sophisticated actuation [9,10] owing to the advantages of cost-effectiveness, wireless actuation, and fast response.…”
Azobenzene actuator has attracted wide research attention in the fields of soft robots, artificial muscles, etc., owing to the typical photoresponsive material based on its reversible trans–cis isomerization. However, it remains challenging to enhance the actuation performance of azobenzene actuators through simple methods and can work in complex and variable environments. In contrast to complex molecular functional design, this study presents a Janus azobenzene inverse opal actuator: one side of a monodomain azobenzene polymer and the other side of a polydomain azobenzene inverse opal structure. The proposed design can significantly enhance the photoactuation performance in the liquid phase based on the synergetic effects of the Janus structure and the plastic influence of solvent/thermal/photo responses on the polymer segment. Promising applications of photo‐driven ring rolling in the liquid phase are demonstrated. The results of this study are of great significance for the design and fabrication of novel‐type azobenzene actuators in the liquid phase.
“…The key milestones in these developments are illustrated in Scheme 1. [ 18–28 ] Generally speaking, the uniformity and integrity of the prepared heterostructures are highly dependent on the as‐used fabrication processes. Based on the evaluation of the step‐by‐step stacking flow path, one can disassemble the stacking processes into three crucial steps, selecting the target 2D material flakes (or called as Lego‐like basic building blocks), designing the out‐of‐plane stacking order of the heterostructures (for vertical microstructure definition), and the twist‐angle alignment between adjacent flakes (for lateral microstructure definition), which finally determines the electronic band structures and physical properties of the heterostructures.…”
2D heterostructures have garnered tremendous attention for potential applications in electronics and optoelectronics. Heterostructures can be constructed by assembling individual atomically thin layers of 2D materials into integrated devices, which involves three primary degrees of freedom (DOFs), i.e., Lego‐like basic building blocks, out‐of‐plane stacking order, and in‐plane twist‐angle alignment. By steering the DOFs of 2D materials, devices and structures such as artificial Shockley junction, quantum wells, and superlattices can be conveniently established based on well‐developed fabrication and/or assembly techniques, beneficial for next‐generation ultracompact information technologies. Herein, the recent progress on constructing the artificial atomic structures by taking advantage of three primary DOFs is overviewed. An outlook of the challenges and future developments is presented as well. Future advancements in the rational construction of complex devices and artificial heterostructures are also suggested.
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