Well, Actuate(ly)...: Parametric Multi-Material 3D Printed Soft Robotics

10.26686/wgtn.14072330 ◽

2021 ◽

Author(s):

Patrick Coulson

Keyword(s):

Complex Geometry ◽

Casting Process ◽

Soft Materials ◽

Research Field ◽

Soft Robotics ◽

Soft Robots ◽

3D Print ◽

Parametric Control ◽

3D Printed ◽

Polyjet Printing

In recent years, soft robotics has gained wide interest in the research field and has also garnered some commercial success. This is because soft robots are comprised of soft materials that have inherent compliance which lends them to a wide variety of applications that are not suited to traditional hard-bodied robots. Soft robots are generally created using a casting process, which comes with limitations to the geometry due to the removal of the cast body from the mould. This research seeks to enhance the capabilities of soft robotic limbs using multi-material Polyjet printing – a recently developed additive manufacturing technology – which allows for geometric freedom and variable materials within a singular soft 3D print which is not feasible using other fabrication methods. This research draws inspiration from natural mechanisms such as muscular hydrostats, to enable the exploration of singular channel soft robots that exhibit bending, twisting, elongation, and expansion all in one 3D print. The geometric freedom and variable materiality of the Stratasys J750 produce actuation results for each motion that cannot be easily replicated using traditional fabrication techniques. The printable materials of the Stratasys J750 were found to have tendencies to tear upon inflation, however, a large array of prints with complex geometry were able to successfully actuate despite this. In some areas, results outperformed actuators made using other fabrication techniques, as was particularly evident in the twisting actuators. Through fine-tuned parametric control with equation-driven modelling, this portfolio presents a method for soft robotic design and construction that can produce a limb with multiple motions and up to 5 axes of movement that can be tailored to specific pre-defined applications.

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Additive Manufacturing for Soft Robotics: Design and Fabrication of Airtight, Monolithic Bending PneuNets with Embedded Air Connectors

Micromachines ◽

10.3390/mi11050485 ◽

2020 ◽

Vol 11 (5) ◽

pp. 485 ◽

Cited By ~ 2

Author(s):

Gianni Stano ◽

Luca Arleo ◽

Gianluca Percoco

Keyword(s):

Additive Manufacturing ◽

Wall Thickness ◽

Chamber Wall ◽

Soft Robotics ◽

Soft Robot ◽

Soft Robots ◽

Bending Performance ◽

3D Print ◽

Air Tightness ◽

The Relationship

Air tightness is a challenging task for 3D-printed components, especially for fused filament fabrication (FFF), due to inherent issues, related to the layer-by-layer fabrication method. On the other hand, the capability of 3D print airtight cavities with complex shapes is very attractive for several emerging research fields, such as soft robotics. The present paper proposes a repeatable methodology to 3D print airtight soft actuators with embedded air connectors. The FFF process has been optimized to manufacture monolithic bending PneuNets (MBPs), an emerging class of soft robots. FFF has several advantages in soft robot fabrication: (i) it is a fully automated process which does not require manual tasks as for molding, (ii) it is one of the most ubiquitous and inexpensive (FFF 3D printers costs < $200) 3D-printing technologies, and (iii) more materials can be used in the same printing cycle which allows embedding of several elements in the soft robot body. Using commercial soft filaments and a dual-extruder 3D printer, at first, a novel air connector which can be easily embedded in each soft robot, made via FFF technology with a single printing cycle, has been fabricated and tested. This new embedded air connector (EAC) prevents air leaks at the interface between pneumatic pipe and soft robot and replaces the commercial air connections, often origin of leakages in soft robots. A subsequent experimental study using four different shapes of MBPs, each equipped with EAC, showed the way in which different design configurations can affect bending performance. By focusing on the best performing shape, among the tested ones, the authors studied the relationship between bending performance and air tightness, proving how the Design for Additive Manufacturing approach is essential for advanced applications involving FFF. In particular, the relationship between chamber wall thickness and printing parameters has been analyzed, the thickness of the walls has been studied from 1.6 to 1 mm while maintaining air tightness and improving the bending angle by 76.7% under a pressure of 4 bar. It emerged that the main printing parameter affecting chamber wall air tightness is the line width that, in conjunction with the wall thickness, can ensure air tightness of the soft actuator body.

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A review of 3D printing processes and materials for soft robotics

Rapid Prototyping Journal ◽

10.1108/rpj-11-2019-0302 ◽

2020 ◽

Vol 26 (8) ◽

pp. 1345-1361 ◽

Cited By ~ 1

Author(s):

Yee Ling Yap ◽

Swee Leong Sing ◽

Wai Yee Yeong

Keyword(s):

3D Printing ◽

Soft Robotics ◽

Soft Robots ◽

Content Type ◽

Advantages And Disadvantages ◽

The Future ◽

3D Printed ◽

Multiple Materials ◽

Printing Processes ◽

Printing Techniques

Purpose Soft robotics is currently a rapidly growing new field of robotics whereby the robots are fundamentally soft and elastically deformable. Fabrication of soft robots is currently challenging and highly time- and labor-intensive. Recent advancements in three-dimensional (3D) printing of soft materials and multi-materials have become the key to enable direct manufacturing of soft robots with sophisticated designs and functions. Hence, this paper aims to review the current 3D printing processes and materials for soft robotics applications, as well as the potentials of 3D printing technologies on 3D printed soft robotics. Design/methodology/approach The paper reviews the polymer 3D printing techniques and materials that have been used for the development of soft robotics. Current challenges to adopting 3D printing for soft robotics are also discussed. Next, the potentials of 3D printing technologies and the future outlooks of 3D printed soft robotics are presented. Findings This paper reviews five different 3D printing techniques and commonly used materials. The advantages and disadvantages of each technique for the soft robotic application are evaluated. The typical designs and geometries used by each technique are also summarized. There is an increasing trend of printing shape memory polymers, as well as multiple materials simultaneously using direct ink writing and material jetting techniques to produce robotics with varying stiffness values that range from intrinsically soft and highly compliant to rigid polymers. Although the recent work is done is still limited to experimentation and prototyping of 3D printed soft robotics, additive manufacturing could ultimately be used for the end-use and production of soft robotics. Originality/value The paper provides the current trend of how 3D printing techniques and materials are used particularly in the soft robotics application. The potentials of 3D printing technology on the soft robotic applications and the future outlooks of 3D printed soft robotics are also presented.

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Review of machine learning methods in soft robotics

PLoS ONE ◽

10.1371/journal.pone.0246102 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0246102

Author(s):

Daekyum Kim ◽

Sang-Hun Kim ◽

Taekyoung Kim ◽

Brian Byunghyun Kang ◽

Minhyuk Lee ◽

...

Keyword(s):

Machine Learning ◽

Soft Materials ◽

Research Field ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Learning Methods ◽

Soft Robots ◽

Machine Learning Methods ◽

Wearable Robots ◽

Soft Actuators

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.

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Soft Adaptive Mechanical Metamaterials

Frontiers in Robotics and AI ◽

10.3389/frobt.2021.673478 ◽

2021 ◽

Vol 8 ◽

Author(s):

Romik Khajehtourian ◽

Dennis M. Kochmann

Keyword(s):

Soft Materials ◽

Building Blocks ◽

Soft Robotics ◽

Energy Potential ◽

Soft Robots ◽

Strongly Nonlinear ◽

Mechanical Metamaterials ◽

Unit Cells ◽

Potential Applications ◽

Mechanical Logic

Soft materials are inherently flexible and make suitable candidates for soft robots intended for specific tasks that would otherwise not be achievable (e.g., smart grips capable of picking up objects without prior knowledge of their stiffness). Moreover, soft robots exploit the mechanics of their fundamental building blocks and aim to provide targeted functionality without the use of electronics or wiring. Despite recent progress, locomotion in soft robotics applications has remained a relatively young field with open challenges yet to overcome. Justly, harnessing structural instabilities and utilizing bistable actuators have gained importance as a solution. This report focuses on substrate-free reconfigurable structures composed of multistable unit cells with a nonconvex strain energy potential, which can exhibit structural transitions and produce strongly nonlinear transition waves. The energy released during the transition, if sufficient, balances the dissipation and kinetic energy of the system and forms a wave front that travels through the structure to effect its permanent or reversible reconfiguration. We exploit a triangular unit cell’s design space and provide general guidelines for unit cell selection. Using a continuum description, we predict and map the resulting structure’s behavior for various geometric and material properties. The structural motion created by these strongly nonlinear metamaterials has potential applications in propulsion in soft robotics, morphing surfaces, reconfigurable devices, mechanical logic, and controlled energy absorption.

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Analysis of Soft Robotics Based on the Concept of Category of Mobility

Complexity ◽

10.1155/2019/1490541 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 4

Author(s):

Hayato Saigo ◽

Makoto Naruse ◽

Kazuya Okamura ◽

Hirokazu Hori ◽

Izumi Ojima

Keyword(s):

Category Theory ◽

Soft Materials ◽

Design Principles ◽

Theoretical Background ◽

Rigid Bodies ◽

The Body ◽

Soft Robotics ◽

Soft Robots ◽

Natural Processes ◽

Rigorous Description

Soft robotics is an emerging field of research where the robot body is composed of flexible and soft materials. It allows the body to bend, twist, and deform to move or to adapt its shape to the environment for grasping, all of which are difficult for traditional hard robots with rigid bodies. However, the theoretical basis and design principles for soft robotics are not well-founded despite their recognized importance. For example, the control of soft robots is outsourced to morphological attributes and natural processes; thus, the coupled relations between a robot and its environment are particularly crucial. In this paper, we propose a mathematical foundation for soft robotics based on category theory, a branch of abstract mathematics where any notions can be described by objects and arrows. It allows for a rigorous description of the inherent characteristics of soft robots and their relation to the environment as well as the differences compared to conventional hard robots. We present a notion called the category of mobility to well describe the subject matter. The theory has been applied to a model system and analysis to highlight the adaptation behavior observed in universal grippers, which are a typical example of soft robotics. The aim of the present study is not to offer concrete engineering solutions to existing robotics but to provide clear mathematical description of soft robots by category theory and to imply its potential abilities by a simple soft gripper demonstration. This paper paves the way to developing a theoretical background and design principles for soft robotics.

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Tensegrity metamaterials for soft robotics

Science Robotics ◽

10.1126/scirobotics.abd9158 ◽

2020 ◽

Vol 5 (45) ◽

pp. eabd9158 ◽

Cited By ~ 1

Author(s):

Li Wen ◽

Fei Pan ◽

Xilun Ding

Keyword(s):

Soft Robotics ◽

Soft Robots ◽

3D Printed

3D-printed flexible tensegrities with metamaterial properties enable customizable complex locomotion in soft robots.

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Soft robotics: the route to true robotic organisms

Artificial Life and Robotics ◽

10.1007/s10015-021-00688-w ◽

2021 ◽

Author(s):

Jonathan Rossiter

Keyword(s):

Environmental Impact ◽

Soft Materials ◽

Soft Robotics ◽

Biodegradable Materials ◽

Soft Robots ◽

Soft Systems ◽

Biological Organisms ◽

The Way

AbstractSoft Robotics has come to the fore in the last decade as a new way of conceptualising, designing and fabricating robots. Soft materials empower robots with locomotion, manipulation, and adaptability capabilities beyond those possible with conventional rigid robots. Soft robots can also be made from biological, biocompatible and biodegradable materials. This offers the tantalising possibility of bridging the gap between robots and organisms. Here, we discuss the properties of soft materials and soft systems that make them so attractive for future robots. In doing so, we consider how future robots can behave like, and have abilities akin to, biological organisms. These include huge numbers, finite lifetime, homeostasis and minimal—and even positive—environmental impact. This paves the way for future robots, not as machines, but as robotic organisms.

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