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Current computer-aided design software was not created with free-form 3D fabrication processes in mind, and does not accommodate the complex non-homogeneous 3D designs that may be desired for soft robotics

Design, fabrication and control of soft robots

NATURE, no. 7553.0 (2015): 467-475

Cited by: 2542|Views336
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Abstract

Conventionally, engineers have employed rigid materials to fabricate precise, predictable robotic systems, which are easily modelled as rigid members connected at discrete joints. Natural systems, however, often match or exceed the performance of robotic systems with deformable bodies. Cephalopods, for example, achieve amazing feats of ma...More

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Introduction
  • Engineers have employed rigid materials to fabricate precise, predictable robotic systems, which are modelled as rigid members connected at discrete joints.
  • The key challenge for creating soft machines that achieve their full potential is the development of controllable soft bodies using materials that integrate sensors, actuators and computation, and that together enable the body to deliver the desired behaviour.
  • Conventional approaches to robot control assume rigidity in the linkage structure of the robot and are a poor fit for controlling soft bodies, soft materials require new algorithms.
Highlights
  • Engineers have employed rigid materials to fabricate precise, predictable robotic systems, which are modelled as rigid members connected at discrete joints
  • Softness and body compliance are salient features often exploited by biological systems, which tend to seek simplicity and show reduced complexity in their interactions with their environment[2]
  • In contrast to hard-bodied robots, soft robots have bodies made out of intrinsically soft and/or extensible materials that can deform and absorb much of the energy arising from a collision
  • These robots have a continuously deformable structure with muscle-like actuation that emulates biological systems and results in a relatively large number of degrees of freedom compared with their hard-bodied counterparts
  • Other groups[29,33,34,35] (Fig. 2b, c) used soft lithography techniques adapted from microfluidics, as well as soft composite materials composed of various silicone polymers and elastomers at times embedded with paper or cloth, to design and fabricate pneumatically actuated soft systems
  • Current computer-aided design software was not created with free-form 3D fabrication processes in mind, and does not accommodate the complex non-homogeneous 3D designs that may be desired for soft robotics
Results
  • In the following three sections the authors review recent developments in the field of soft robotics as they pertain to design and fabrication, computation and control, and systems and applications.
  • Other groups[29,33,34,35] (Fig. 2b, c) used soft lithography techniques adapted from microfluidics, as well as soft composite materials composed of various silicone polymers and elastomers at times embedded with paper or cloth, to design and fabricate pneumatically actuated soft systems.
  • Stretchable electronics So far, most integrated soft-robotic systems have relied on conventional, rigid electronics to store the control algorithms and connect to the systems’ actuators, sensors and power sources.
  • Because soft robots are different from conventional rigid linkage-based systems, researchers have developed new static, dynamic and kinematic models that capture their ability to bend and flex[75].
  • Real-time, closed-loop curvature controllers are required that drive the bending of the soft pneumatic body segments of the manipulator despite their high compliance and lack of kinematic constraints.
  • Control Researchers have used these models to develop new approaches to low-level control, inverse kinematics, dynamic operations and planning for soft-robotic systems.
  • Physical phenomena common to soft robots — including actuation limits, the self-loading effects of gravity, and the high compliance of the manipulator — can be represented as constraints within a trajectory optimization algorithm that operates on a dynamic model of the soft robot[7].
Conclusion
  • Dynamic control of soft robots can be achieved using a new dynamic model for a spatial soft fluidic elastomer manipulator, a method for identifying all unknown system parameters, and a planning algorithm that computes locally optimal dynamic manoeuvres through iterative learning control.
  • Soft systems have a natural advantage over rigid robots in grasping and manipulating unknown objects because the compliance of soft grippers allows them to adapt to a variety of objects with simple control schemes.
  • The authors need rapid design tools and fabrication recipes for low-cost soft robots, novel algorithmic approaches to the control of soft robots that account for their material properties, tools for developing device-specific programming environments that allow non-experts to use the soft machines, creative individuals to design new solutions and early adopters of the technology.
Funding
  • This work was done with partial support from the National Science Foundation grant number IIS-1226883, for which we are grateful
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