A soft robot capable of 2D mobility and self-sensing for obstacle detection and avoidance

Qin, Lei; Tang, Yucheng; Gupta, Ujjaval; Zhu, Jian

doi:10.1088/1361-665x/aab393

Cited by 29 publications

(20 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(i) According to the feature of DE materials, an electrically driven soft crawling robot combining the DEA with EAs referring to Qin et al (2018) is built. It has the advantages of omnidirectional motion and periodic motion mode.…”

Section: Resultsmentioning

confidence: 99%

“…The muscle-like properties and relatively simple actuation method of DEAs have contributed to developing several bio-inspired soft robots, such as worm-like crawling robots (Shian et al, 2015;Mihai Duduta and Wood, 2017;Cao et al, 2018a), underwater robots (Guo et al, 2012;Li et al, 2017) and human-like robots (Carpi and Rossi, 2005;Liu et al, 2008;Wang and Zhu, 2016). There are two notable work Qin et al (2018) and Gu et al (2018) should be pointed out. The work presented in Qin et al (2018) focuses on the robot prototype with its motion, while the work presented in Gu et al (2018) emphasizes more on the adhesion method.…”

Section: Introductionmentioning

confidence: 99%

“…There are two notable work Qin et al (2018) and Gu et al (2018) should be pointed out. The work presented in Qin et al (2018) focuses on the robot prototype with its motion, while the work presented in Gu et al (2018) emphasizes more on the adhesion method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Iterative Learning Control for Motion Trajectory Tracking of a Circular Soft Crawling Robot

Chi

Liang

et al. 2019

Front. Robot. AI

View full text Add to dashboard Cite

Soft robots have recently received much attention with their infinite degrees of freedoms and continuously deformable structures, which allow them to adapt well to the unstructured environment. A new type of soft actuator, namely, dielectric elastomer actuator (DEA) which has several excellent properties such as large deformation and high energy density is investigated in this study. Furthermore, a DEA-based soft robot is designed and developed. Due to the difficulty of accurate modeling caused by nonlinear electromechanical coupling and viscoelasticity, the iterative learning control (ILC) method is employed for the motion trajectory tracking with an uncertain model of the DEA. A D 2 type ILC algorithm is proposed for the task. Furthermore, a knowledge-based model framework with kinematic analysis is explored to prove the convergence of the proposed ILC. Finally, both simulations and experiments are conducted to demonstrate the effectiveness of the ILC, which results show that excellent tracking performance can be achieved by the soft crawling robot.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Iterative Learning Control for Motion Trajectory Tracking of a Circular Soft Crawling Robot

Chi

Liang

et al. 2019

Front. Robot. AI

View full text Add to dashboard Cite

show abstract

Section: Dielectric Elastomersmentioning

confidence: 99%

“…Maeda and co‐workers described a balloon acrylic DEA that functions as a speaker as well 182. Another example of a frame‐mounted disc‐shape DEA robot was one developed by Qin et al by mounting the DEA into the center of a wheel that interacts and guides electro‐adhesion actuators around its perimeter 183. These enable the robot to move around complex geometric barriers as it locomotes along the floor (Figure 8e).…”

Section: Dielectric Elastomersmentioning

confidence: 99%

Materials as Machines

2020

View full text Add to dashboard Cite

of responsive materials as machines is in robotics. The vision, leadership, and research of Bar-Cohen is seminal in both invigorating and focusing current-day research activities to develop responsive materials [2] and implement [3] them as lightweight, dexterous, and gentle (e.g., soft) robotic elements. In this spirit, this review exhaustively details the materials, the nature of their stimuli-response, and discusses considerations for their implementation in robotic systems and subsystems.Robotics is a well-established but growing field of research. Sustained progress in both performance and functionality continue to be realized in commercial robotic systems largely based on conventional materials and their integration with mechanisms. A recent example is the Atlas robot from Boston Dynamics [4] (Figure 1a). The incorporation of stimuli-responsive materials in robotics has largely focused on component-level demonstrations to extend the performance of a subsystem (such as a hand or gripper).Stimuli-responsive materials, spanning nearly all classes of materials and size scales, are currently subject to widespread examination in corporate, government, and academic research laboratories (Figure 1b-d). [5][6][7] In some cases, these materials have already found widespread commercial implementation in end use and are comparatively mature. The materials and fundamentals of their responses are summarized in Figure 2. Shape memory alloys (SMAs) and ceramic piezoelectric materials are distinctive in that they are hard, stimuli-responsive materials. Deformation of these materials can produce large energy densities due to their inherent stiffness. Electroactive polymers (EAPs) remain a topic of considerable interest, particularly dielectric elastomer actuators (DEAs). Engineered systems are rapidly emerging and enabling performance gains in robotics, largely based on pneumatic or fluidic (such as HASEL [8] actuators) transport processes that localize deformation to generate force or produce motion. Soft materials, such as shape memory polymers (SMPs), hydrogels, and liquid crystalline polymer networks (LCNs) and elastomers (LCEs) may offer distinctive functional performance to robotic systems in allowing local control of deformation without the need for complex interfacing with mechanisms. As will be evident, each of these materials has inherent advantages and performance tradeoffs that must be considered in functional implementations. However, responsive material systems have a common obstacle to widespread use: the performance and Machines are systems that harness input power to extend or advance function. Fundamentally, machines are based on the integration of materials with mechanisms to accomplish tasks-such as generating motion or lifting an object. An emerging research paradigm is the design, synthesis, and integration of responsive materials within or as machines. Herein, a particular focus is the integration of responsive materials to enable robotic (machine) functions such as gripping, lifting, or motility (walk...

show abstract

Programmable Mechanically Active Hydrogel‐Based Materials

2021

View full text Add to dashboard Cite

Hydrogel-based MAMs are the most diverse and intensely studied, and have potential to be programmed with various responding behaviors and have broad applications in areas ranging from biomedical engineering to robotics. Hydrogel programmability, in some literature, refers to the fine-tuning of hydrogel deformation patterns. [4] Other works define programmability of hydrogels as molecular sequencing or arrangement within gel networks. [5] To provide a more consistent overview of programmable hydrogelbased MAMs, this review defines programmability as the ability to tune the input-output response function of the MAMs through either molecular design or patterning of the material across multiple length scales. There are MAMs that are responsive but the response function cannot be easily altered or tuned. For example, a conventional hydrogel can swell and deswell by controlling the humidity. But without spatially patterning the material or without doping certain copolymers into the gel, this type of conventional material is not inherently programmable. We will further discuss the approaches to integrate programmability into MAMs in Section 3.In this review, we first describe the chemical components of hydrogel-based MAMs and their fabrication, followed by specific approaches toward programmability. Then, a detailed classification of responsivity mechanisms is provided, along with discussion of the intimate relationships between material structure and their responsive behaviors. We then end by summarizing current applications of MAMs across multiple disciplines, and the future directions and applications of mechanically responsive hydrogels. Components of HydrogelsMechanical responsivity has been observed in nearly all types of materials both hard and soft materials, spanning from metals to ceramics and polymers. Popular examples include shape-memory metal alloys, [6] piezoelectric ceramics, [7] and thermoresponsive polymers. [8] Herein, we will exlusively discuss programmable MAMs that are primarily composed of polymer hydrogels. This is because hydrogel materials are great candidates for flexible devices and bioengineering studies, and this has inspired multidisciplinary research and numerous potential applications. To be more consistent, inorganic nanoparticles, plastic, rubber, ceramics, polymer micelles/capsules, will not be discussed here since these types of materials are not hydrogels. Programmable mechanically active materials (MAMs) are defined as materials that can sense and transduce external stimuli into mechanical outputs or conversely that can detect mechanical stimuli and respond through an optical change or other change in the appearance of the material. Programmable MAMs are a subset of responsive materials and offer potential in next generation robotics and smart systems. This review specifically focuses on hydrogel-based MAMs because of their mechanical compliance, programmability, biocompatibility, and cost-efficiency. First, the composition of hydrogel MAMs along with the top-down and bottom-up a...

show abstract

A soft robot capable of 2D mobility and self-sensing for obstacle detection and avoidance

Cited by 29 publications

References 66 publications

Iterative Learning Control for Motion Trajectory Tracking of a Circular Soft Crawling Robot

Iterative Learning Control for Motion Trajectory Tracking of a Circular Soft Crawling Robot

Materials as Machines

Programmable Mechanically Active Hydrogel‐Based Materials

Contact Info

Product

Resources

About