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=== International Events ===
=== International Events ===
* 2015 [http://robotics.oregonstate.edu/icra2015softrobotics Soft Robotics: Actuation, Integration, and Applications - Blending research perspectives for a leap forward in soft robotics technology], 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle WA, May 2014
* 2015 [http://robotics.oregonstate.edu/icra2015softrobotics Soft Robotics: Actuation, Integration, and Applications - Blending research perspectives for a leap forward in soft robotics technology], 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle WA, May 2014
* 2015 [http://www.robosoftca.eu/information/events/soft-robotics-week], First Soft Robotics Week, Livorno, April 13-17, 2015
* 2014 [http://www.robosoftca.eu/events/rss2014-workshop Workshop on Advances on Soft Robotics], 2014 Robotics Science an Systems (RSS) Conference, Berkeley, CA, July 13, 2014
* 2014 [http://www.robosoftca.eu/events/rss2014-workshop Workshop on Advances on Soft Robotics], 2014 Robotics Science an Systems (RSS) Conference, Berkeley, CA, July 13, 2014
* 2013 [http://www.softrobot2013.ethz.ch International Workshop on Soft Robotics and Morphological Computation], Monte Verità, July 14-19, 2013
* 2013 [http://www.softrobot2013.ethz.ch International Workshop on Soft Robotics and Morphological Computation], Monte Verità, July 14-19, 2013

Revision as of 14:24, 12 May 2015


Soft Robotics is a subfield of robotics that deals with robots built out of soft and deformable material like silicone, plastic, fabric, rubber, or compliant mechanical parts like springs. Soft robots can actively interact with the environment and can undergo “large” deformations relying on inherent or structural compliance respectively due to the softness or the morphological features of its body.


Aspects of Soft Robots

MIT soft under-actuated fish robots [1]
File:Fish robot by FILOSE.jpg
Robot fish FILOSE [1]
Soft robotic arm inspired by the octopus, developed in the project OCTOPUS
File:SoftUntetheredQuadruped.jpg
The untethered soft quadrupedal robot developed by Tolley et al. is resilient to extreme conditions including snow, fire, water, and being driven over by an automobile[2] .

Soft robots are often, but not necessarily, bio-inspired. They are known to have a number of advantages over classical robotic devices based on traditional robotic technologies. Soft and deformable structures are crucial in systems that deal with uncertain and dynamic tasks and environments, e.g. grasping and manipulation of unknown objects, locomotion in rough terrain, and physical contact with living cells and human bodies.[3][4] JamBots[5][6][7][8][9][10].

Examples of Soft Robots

Here is a list of examples of soft robots.

  • Soft under-actuated fish robots developed in the Mechanical Engineering Department at MIT [2]
  • Octopus robot developed in the EU project OCTOPUS
  • Stiffness controllable flexible and learn-able manipulator for surgical operations STIFF-FLOP
  • Soft caterpillar inspired robot from Tufts University [3]
  • Soft starfish inspired robot developed by Robert Shepherd (Cornell University), see [11]
  • Untethered quadruped soft robot developed by Michael Tolley (UC San Diego) et al., see [2]
  • SMART/MIT soft batoids and sensors [4]
  • Roboy - a tendon driven robot developed by the Artificial Intelligence Laboratory of the University of Zurich
  • JamBot - locomotion based on granular jamming
  • GoQBot
  • CFD-OctoProp
  • PoseiDRONE
  • ECCE robot
  • FILOSE

Components for Soft Robots

In the context of soft robotics a whole range of materials are used like silicone, paper, wood, but also metal (e.g. in springs). In addition, so-called smart materials are playing an important, especially, for actuation and sensing. The main components are actuators, sensors, and structural components, all of which can be to a certain extent soft. In addition, as opposed to classical robot design, in soft robots the concept of morphological computation is considered, where the control of a soft robot is implemented directly in its physical body (morphology) instead of a software running on a CPU.

Soft Actuators

Shape Memory Alloys (SMAs)

Main article: Shape-memory_alloy

Shape Memory Alloys are metal alloys that can deformate and then recover their original shape by heating [12]. SMAs are used in soft robots because of their flexibility, the large force-weight ratio, a limited volume, inherent sensing capability and noise-free operation. The low efficiency, high hysteresis and non-linearity are the major drawbacks [13][14].

Shape Memory Polymers (SMPs)

Main article: Shape-memory_polymer

SMPs are smart polymers that, as SMA, are capable of undergoing a certain strain and then recover the original shape when heated. Differently from SMA, they are used in several fields of applications because of their flexibility, biocompatibility and wide scope of modifications. A comprehensive review can be found in [15].

Electroactive polymers

Main article: Electroactive_polymers

Electro Active Polymers (EAPs) are based on polymeric matrixes that can change their shape and size when undergoing to an electric stimulus. They are a promising class of smart polymers for biomimetic and biomedical soft robots because they have power densities exceeding those of biological muscle, are readily scalable and free-form fabricable. Currently they have limited application because the required electric field is very high (in the case of electronic EAP), or they have a slow response and low lifetime (for the ionic EAP).

[16]

Flexible fluidic actuator

These actuators use the fluid pressure force to generate a traction force or a bending movement. They are based on an expansion chamber defined by an inner wall of an expandable girdle connected to at least two anchoring points. The expansion chamber can be pressurized acquiring a minimum or a maximum volume. They are finding several applications in the soft robotics field [17]

Cable-driven actuation

Actuators based on cables have the benefit of providing a distributed and continuous action during the movement. They have low inertia, are fast, have low weight and volume and guarantee a fast responce with long range transmission of force and power. The control is simplified, but friction losses along the robot due to the cables may reduce the controllability of the system itself [18].

Semi-active actuators

< copied paragraph removed >

[19][20][21].

Variable Impedance Actuators

< paragraph removed, copied from http://www.eucognition.org/eucog-wiki/Compliant_robots >

[22]

Soft Sensors

< paragraph removed, largely copied from https://www.ri.cmu.edu/pub_files/2011/10/Park_Sensors11.pdf >

[23][24][25][26][27][28][29][30].

Soft Structures

The NASA SUPERball Tensegrity Robot. This is a prototype of a system which will be able to land on another planet without an airbag due to its structural compliance, and then it can roll by changing cable lengths and actively explore. [31]
The Tetraspine robot is an abstract and simplified model of how tensegrity principles may exist in spines. It is compliant and adaptable, and conforms to the terrain it is crossing. Neuroscience inspired CPG controllers were used for a distributed control scheme that enables it to reactively cross a wide range of terrains and obstacles.[32]

By employing hybrid structural approaches, such as tensegrity structures, Robots can also be soft at the "structural" level, even if they use a mix of soft and rigid component elements. A Tensegrity Robot is structurally integrated through its continuous elastic tensile network, and exhibits many of the properties commonly associated with soft robots, such as passive compliance, robust distribution of contact forces, highly deformable shape, and distributed parallel actuation approaches. Likewise, they have the same challenges in sensing, modeling and control due to their passive compliance, oscillatory response, and non-linear structural dynamics. Tensegrity robots have been shown to benefit from many of the same sensing, modeling, and control approaches as other soft robots, such as the use of neuroscience inspired Central Pattern Generators distributed emergent controllers[32]. One advantage that tensegrity robots present relative to purely soft robots is the opportunity to integrate existing sensors and actuators into the rods, yet still achieve a structurally compliant system.

Tensegrity Robots have been developed in hardware and simulation by a growing number of research labs over the last decade, and have been proposed for a wide range of applications, including bio-mimicry[33] and space exploration.[34] Recently NASA released the beta version of an open source NASA Tensegrity Robotics Toolkit (NTRT), which is a physics based simulation engine for tensegrity robots. Due to hardware validation experiments, the elastic cables simulated in the NTRT have been shown to be accurate enough to model the dynamics of real tensegrity robots.[31] During the winter of 2014, it is expected that an update will be released which will further update the elastic cable models to include accurate contact dynamics. These tools to model soft compliant cables at an engineering level of accuracy are notably lacking from other robotic simulation environments and are being used by a wide range of researchers.

Modeling Soft Robots

< paragraph deleted - two sentences match to http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6827980 >

[35][36][37][38]

Control Architectures

In general, soft robotic structures are hard to control. The reason is that soft bodies are highly complex and, therefore, hard to model. Typical properties are strong nonlinearities, a high-dimensional (potentially infinite) state space, bifurcation behavior, underactuation, and delayed communication - all of which are difficult to handle individually and represents a serious challenge when combined. Due to this fact a lot of times soft robots are not actively controlled or are very imprecisely controlled with a simple feedforward controller.

A concept often mentioned in the context of soft robotics is Morphological Computation, which his based on the observation in Nature that the physical body (i.e. the morphology) of biological systems seem to contribute to computational aspects. The concept has repeatedly been applied in robot designs, especially, with soft bodies.[39] The general approach is to cleverly design (soft) bodies, such that they can take over part of the control of the robot. For example, in locomotion a spring in a leg enables the robot to cope with uneven ground as this external perturbations can be absorbed by the springs without the need of an external controller.

Despite the number of successful designs [39] there exist very little theoretical work to establish a mathematical framework to describe the computational power of (soft) bodies and to use it to design a control approach for soft robots. Füchslin et al. [40] discussed how morphological computation in the context of control can look like. Some initial work on mathematical frameworks has been done by Hauser et al.[41][42] There also exist a number work that shows that theses theoretical models can be applied to real robotics platforms under real world conditions. For example, Nakjima et al. [43] demonstrated the computational power of an octopus-inspired soft silicone arm and how it can be exploited as a computational resources. Furthermore, they demonstrated how the same setup can be used to control the arm for simply movements. Zhao et al. [44] used the same theoretical models of Hauser et al. to control a quadruped robot.

Another approach to rethink the paradigm approach to the control of robots and soft robots, and their modeling, from the point of view of morphological computation is being pursued in a series of theoretical papers, [45] [46] [47][48][49], by Fabio Bonsignorio a follower of the University of Zurich AILab approach to embodied intelligence initiated by Rolf Pfeifer. When considering soft robots it is intuitive that the compliant morphology of the robot makes easier the control. The exploitation of natural dynamics has led to passive walkers , [50] [51], which in terms of cheap computational burden and energy efficiency outperform fully actuated comparable sytems, . Yet a set of mathematical models and control strategies allowing the exploitation of underactuation and soft structures is still in its infancy. Bonsignorio, leveraging on work by Pfeifer himself and collaborators, Touchette and Lloyd [52], Gregory Chirijkian, [53], Ralph Der [54] , Nihat Ay [55], Daniel Polani [56], and others [57][58][59][60] [61] , sets the problem of robot control within a novel paradigm framework integrating information theory , self-organization, dynamical systems and theoretical mechanics, which makes very natural to understand the issues of soft robot control. This theoretical framework , although promising, is still not widely known, needs futher work, in particular to merge with the approaches proposed by Hauser, Fuechslin, Pfeifer and others, and still lacks a thorough experimental validation.

Scientific Community

Although people have been using soft material for robots for a long time, only recently an international community has been formed. For example, since October 2012 exists an IEEE RAS Technical Committee on Soft Robotics, which coordinates the international community around this field of research. In 2013 the International Journal on Soft Robotics was funded. It publishes quarterly results from the field. In October 2013 started RoboSoft –

International Journals

International Events

Projects

  • EU Project OCTOPUS, coordinated by Prof. Cecilia Laschi, BioRobotics Institute, Scuola Superiore Sant'Anna, Italy
  • EU Project LOCOMORPH, coordinated by Prof. Rolf Pfeifer, Artificial Intelligence Laboratory, University of Zurich, Switzerland
  • EU Project ECCE, coordinated by Prof Owen Holland, Department of Informatics, University of Sussex, UK
  • EU Project Myorobotics, coordinated by Prof. Alois Knoll, Robotics and Embedded Systems, Technical University of Munich, Germany
  • EU Project STIFF-FLOP, coordinated by Prof. Kaspar Althoefer, Centre for Robotics Research, King's College of London, UK
  • IGERT, coordinated by Prof. Barry Trimmer, Neuromechanics and Biomimetic Devices Laboratory, Tufts University, Boston, USA
  • EU ERC Grant Project SPEAR, coordinated by Prof. Bram Vanderborght, Robotics and Multibody Mechanics Group, Vrije Universiteit Brussel, Belgium

Education

Soft robotics and morphological computation are central topics in the ShanghAI Lectures The ShanghAI Lectures initiated by Prof. Rolf Pfeifer and currently coordinated by Prof. Fabio Bonsignorio.

< paragraph removed, copied from http://shanghailectures.org/ >

Educational and Scientific Tools in Soft Robotics

There are a number of educational tools for soft robotics.

Fields of Application

Medicine

  • STIFF-FLOP is a project to develop tools in the context of minimally invasive surgery to go through narrow openings and manipulate soft organs that can move, deform, or change stiffness.
  • Protheses

Training and Therapy

  • Allegro is a force-controlled soft robotic training partner for dynamic resistance training, rehabilitation and measurement.

Marine robotics


Exoskeletons

  • KNEXO (powered KNee EXOskeleton)
  • LOPES (LOwer-extremity Powered ExoSkeleton)

Human Robot Interaction

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