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Revision as of 09:37, 21 November 2014


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:SoftQuad-Extremes.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 and 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].

Disadvantages of soft robots are that soft structures are difficult to model and, therefore, complicated to control.

“Soft” may refer to the structural compliance of a robot, that means that the softness comes from ad hoc geometrical arrangements and morphology of hard materials – so that structural strains are magnified compared with local material deformation (e.g. compliant mechanisms). Several industrial robots use compliant mechanisms, such as the KUKA Lightweight Robot (LWR) that is characterized by a low mass-payload ratio and a programmable, active compliance.

“Soft” may also refer to an inherent material compliance that involves bulk material properties – including soft matter (e.g. elastomers, polymers, gels, etc.), which guarantee a safe interaction with the environment. As an example of soft robots, the soft silicone-based caterpillar robot inspired by the manduca sexta, the GoQBot[4], exploits SMA (Shape Memory Alloy) actuators and the incompressibility of fluids to deliver performance resembling those of the hydrostatic skeletons. The octopus-inspired robots (OCTOPUS, OctoProp and PoseiDRONE) combine the use of soft materials, SMA actuators and cable driven transmission to accomplish dexterous manipulation by artificial muscular hydrostats[5][6], legged locomotion[7] and swimming by jet propulsion[8], and use the same principles of octopus vulgaris with a biomimetic design approach[9][10]. The STIFF-FLOP endoscopic device represents another octopus-inspired robot that combines high dexterity and stability thanks to the combination of a pneumatic actuation and a granular jamming based mechanism[11].

At Harvard University a series of soft robots based on pneumatic actuation has been developed, such as starfish-like[12] and tentacle-like robots[13], which show large deformation and even camouflage capability. The JamBots are another example of how soft materials in combination with soft actuation technologies can be used for robot locomotion[14] and grasping[15]. In JamBots, the material properties can be changed with granular jamming (determining anisotropies) and motion can be generated with pneumatic actuators or with cable-driven systems, as in the case of the MIT jammable manipulator[16]. Soft and flexible materials can be also be part of the actuation system itself as in the case of the use of EAP (ElectroActive Polymers) in the starfish-like robot[17] or in the tissue-engineered multi-limbed medusoid robot[18], or in the Meshworm robots where a series of SMA springs arranged in antagonistic manner supported by a flexible braided mesh-tube structure is used to produce a peristaltic motion[19].

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 [20]
  • 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 capable of undergoing a certain strain and subsequently recover their original shape when heated[21]. SMAs allow creating robotic systems that are drastically small in size, weight, and complexity, when compared with traditional robots. As actuators for mechatronics systems, these alloys present several drawbacks like low efficiency, high hysteresis and non-linearity, but the interest on these alloys is still growing for their flexibility, large force–weight ratio, limited volume, inherent sensing capability, and noise-free operation which enable the employment of this technology in soft robotics [22][23].

Shape Memory Polymers (SMPs)

Main article: Shape-memory_polymer

Shape Memory Polymers belong to a class of smart polymers, which have drawn considerable research interest in the last few years because of their applications in micro electromechanical systems and actuators in biomedical devices. Shape memory polymers exploit the same principle as SMAs: by stretching the rubber at a temperature above the crystallisation point and then cooling it in that extended shape will lock‐in the deformed material. On heating to melt the crystals, the original network configuration is recovered. In several fields of applications, SMP materials have been proved to be suitable substitutes to metallic ones because of their flexibility, biocompatibility and wide scope of modifications. A comprehensive review can be found in (Ratna and Karger‐Kocsis, 2008)[24].

Electroactive polymers

Main article: Electroactive_polymers

Electro Active Polymers (EAPs) are a new emerging and promising class of technologies which already demonstrated the possibility to fill the gap between natural muscles and artificial artefacts. Most of them are based on polymeric matrixes activated with different mechanisms, but they are all endowed with the capability of varying their size and shape when an electric stimulus is supplied[25]. They have power densities exceeding those of biological muscle, are readily scalable and free-form fabricable, and are ideally suited to biomimetic and biomedical soft robotic applications. However, currently they have limited applicability because of the high electric fields required (in the case of electronic EAP) or the slow response and low lifetime (for the ionic EAP).

Flexible fluidic actuator

Flexible fluidic actuator is a term used for a wide range of system types, but generically they comprise an expansion chamber defined by an inner wall of an expandable girdle which is connected to at least two anchoring points. The expansion chamber can be pressurized acquiring a minimum or a maximum volume. Thus actuators are able to adapt and transform a fluid pressure force against the inner wall into a traction force or a bending movement. A recent review can be found in (De Greef et al., 2009)[26]. The main disadvantages of fluidic systems are the risk of leakages or—even worst—the possibility of burst of the inflated chambers.

Cable-driven actuation

Cable-driven actuation has the benefit of providing a distributed and continuous action and cables can be fitted at spots within a soft robot where it would be hard to place other actuators otherwise, since powerful motors can be embedded outside the robot thus keeping it flexible[27]. Since cable transmission is continuous and is subject to negligible backlash issues, control is greatly simplified, but friction losses along the robot due to the cables may reduce the controllability of the system. Compared with the other actuation methods, cable actuation offers low inertia, weight and volume, it guarantees fast response times and long range transmission of force and power.

Semi-active actuators

A special class of materials offers the possibility to change its mechanical properties due to controlled physical stimuli. Thermo- magneto- and electro-rheological materials possess the capability to change the stiffness from values resembling low viscosity fluids to values similar to solid materials by applying thermal[28], magnetic or electric fields[29], respectively. The main drawbacks are due to control issues and low response time (for thermal activation) or the high fields required (for the magnetic and electric activation). Granular jamming is another phenomenon that is arising a growing interest for the impressive behaviour which enables particles to act like a liquid, solid, or something in between depending on an applied vacuum level[30].

Variable Impedance Actuators

Variable Impedance Actuators are also know as variable compliance, adjustable stiffness, and controllable stiffness actuators. Passive compliant actuators contain an elastic element, e.g. a spring which can store energy, which is not the case for actuators with active compliance, where the controller of a stiff actuator mimics the behavior of a spring. The latter has the disadvantage that no energy can be stored in the actuation system and due to the limited bandwidth of the controller, no shocks can be absorbed. An advantage of active compliance is that the controller can make the compliance online adaptable. Online adaptability means that the compliance can be adapted during normal operation. A famous robot that uses active compliance for safety is the Kuka/dlr leightweight arm. The most well know example of a passive compliant actuator is the original Series Elastic Actuator SEA), which is a spring in series with a stiff actuator. The compliance of this actuator is fixed and is determined by the selection of the spring; thus, the physical compliance cannot be changed during operation. To obtain variable stiffness, the virtual stiffness of the actuator is adjusted by dynamically adjusting the equilibrium position of the spring. To combine energy storage and adaptable compliance, an elastic element to store energy is needed, together with a way to adapt the compliance. A substantial number of designs have been developed[31] like AWAS, Maccepa, VSA joint, AMASC, VS-joint, vsaUT. Also parallel springs are investigated for actuation and gravity compensation in order to reduce the torque through the motor, a new concept in this field is the series-parallel elastic actuator (SPEA) where several parallel springs can be engaged or disengaged mimicking the variable recruitment of the muscle fibres in a biological muscle.

Soft Sensors

The kind of system that has been mostly investigated in soft applications is tactile sensing. One of the most widely used methods is to detect structural deformation with embedded strain sensors in an artificial skin. Highly sensitive fibre optic strain sensors have been embedded in a plastic robotic finger for force sensing and contact localization[32]. Embedded strain gages and the detection of capacitance change with embedded capacitive sensor[33] arrays are other approaches for tactile sensing[34]. There have been stretchable skin-like sensors proposed with different methods: a strain sensing fabric composites developed using an electrically conductive elastomer[35]; a stretchable tactile sensor proposed also using polymeric composites[36]; a highly twistable tactile sensing array made with stretchable helical electrodes[37]; an artificial skin sensor consisting of multi-layered mirco-channels filled with a conductive liquid capable of detecting multi-axis strains and contact pressure[38]. In addition, many of the compliant soft actuators described above, such as EAPs, can simultaneously act as actuators and electromechanical sensors[39].

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. [40]
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.[41]

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[41]. 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[42] and space exploration.[43] 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.[40] 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

In order to model a soft robot, the traditional mathematical tools typically used for rigid robot dynamic modeling, cannot be applied. A continuum approach is required, with distributed parameters and nonlinear partial differential equations need to be considered. Existing models can be divided into two main categories: constant and non-constant curvature approximation. They vary in the degree of precision obtained. The constant curvature approximation can be considered as the simplest approach to modelking soft robots. As specified in various constant curvature works [44], this approximation fails in many practical cases, for example, when the environmental loads (such as gravity) are significant or in the case of nonconstant sections of the robot arm. Non-constant curvature models belong to three categories: continuum approximation of hyperredundant systems [45], the spring–mass model[46], and the Cosserat geometrically exact approach[47]. Continuum approximation of hyperredundant systems was probably the first continuum approach to be proposed in the robotics community, unveiling a variety of applications such as snake locomotion or trunk manipulation. However, the latest robotic platforms developed, which are made of highly deformable continuum bodies, have led quite naturally to using a pure continuum approach to modeling from the beginning. Cosserat geometrically exact models are the closest to the mechanics of a soft robot structure and actuation while the exploitation of FEM seem a natural development, although computing intensive.


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.[48] 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 [48] 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. [49] 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.[50][51] 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. [52] 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. [53] 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, [54] [55] [56][57][58], 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 , [59] [60], 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 [61], Gregory Chirijkian, [62], Ralph Der [63] , Nihat Ay [64], Daniel Polani [65], and others [66][67][68][69] [70] , 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 – A Coordination Action for Soft Robotics funded by the European Commission under the 7th Framework Programme, Future and Emerging Technologies (FET) Open Scheme. RoboSoft aims at creating and consolidating an international scientific community of scientists and roboticists working in the field of soft robotics to combine their efforts and enable the accumulation and sharing of scientific and technological knowledge to maximize the opportunities and materialize the huge potential impact of soft robotics technologies.

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. The ShanghAI Lectures project aims at

  • building a sustainable community of students and researchers in the area of Embodied Intelligence
  • making education and knowledge on cutting-edge scientific topics accessible to everyone
  • exploring novel methods of knowledge transfer
  • overcoming the complexity of a multi-cultural and interdisciplinary learning context
  • bringing global teaching to a new level

These lectures about Natural and Artificial Intelligence, largely based on Pfeifer's ideas, are held via videoconference at the University of Zurich in Switzerland, Scuola Superiore Sant'Anna of Pisa, Italy, Humboldt University Berlin in Germany, University of Plymouth and University of Salford in the UK, University of Osaka in Japan, Shanghai Jiao Tong University in China, and about 10 other universities and research organizations around the globe. Students from the participating universities work together on the exercises, using a powerful robotics simulator software, Cyberbotics Webots.


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

Industry

Applications of the soft robots are in tasks where there is needs to move in physical interaction with an unknown and dynamic environment and the controlled body-actuator system must achieve abilities like:

  • Safety to humans (and resilience to self-damage) in operations where the robot is required positional accuracy and swiftness of motion, while cooperating, physically interacting or even possibly colliding with the humans and their environment, such as e.g. in collaborative robotics. This has been studied using a pneumatic soft arm, the DLR Hand/Arm system, and illustrated through impacts’ response (e.g. by hammering on the fingers and knocking using a baseball on the arm - see video) and the co-worker Baxter. The social robot Probo uses SEAs to have safe and huggable HRI with children.
  • Efficiency (e.g. natural gait generation, adaptation in legged locomotion applications and prosthetics for lower limbs). Like in the bipeds Lucy, Veronica, Dribbel and Tulip where the natural dynamics are exploited to reduce the energy efficiency. Or the AMPfoot, Cyberlegs, BIOM and Sparky prostheses were energy is stored and released during walking. By storing energy in a spring a ball can be thrown or kicked much further as when a stiff actuator is used.
  • Robustness to external perturbations and unpredictable model errors (changes) of the environment, of the robot kinematics and dynamics, or of the dynamics of a human interacting with it. This is often required in tasks like hammering, holding cups, drumming; typical tasks with tools such as screwdriving, cutting, polishing, drawing or stabilizing a humanoid robot like Coman.
  • Adaptability and force accuracy in the interaction with the operator, in applications in which continuous contact and accurate force exchange is necessary, such as in “hands-on” assistive devices, rehabilitation, exoskeletons and haptics. This is achieved, for example, in the exoskeletons KNEXO, altar, LOPES, etc.

References

  1. ^ El Daou, Hadi; Salumäe, Taavi; Chambers, Lily D.; Megill, William M.; Kruusmaa, Maarja (2014). "Modelling of a biologically inspired robotic fish driven by compliant parts". Bioinspiration & BiomimeticsEmail alert RSS feed. 9 (1). doi:doi:10.1088/1748-3182/9/1/016010. {{cite journal}}: Check |doi= value (help)
  2. ^ a b Tolley, Michael T.; Shepherd, Robert F.; Mosadegh, Bobak; Galloway, Kevin C.; Wehner, Michael; Karpelson, Michael; Wood, Robert J.; Whitesides, George M. (2014). "A Resilient, Untethered Soft Robot". Soft Robotics.
  3. ^ IEEE RAS Soft Robotics http://www.ieee-ras.org/soft-robotics. Retrieved 8 September 2014. {{cite web}}: Missing or empty |title= (help)
  4. ^ uai-Ti, Lin; Leisk, Gary G.; Trimmer, Barry (2011). "GoQBot: a caterpillar-inspired soft-bodied rolling robot". Bioinspiration & BiomimeticsEmail alert RSS feed. 6. doi:doi:10.1088/1748-3182/6/2/026007. {{cite journal}}: Check |doi= value (help)
  5. ^ Cianchetti, M.; Arienti, A.; Follador, M.; Mazzolai, B.; Dario, P.; Laschi, C. (August 2011). "Design concept and validation of a robotic arm inspired by the octopus". Materials Science and Engineering: C. 31 (6): 1230–1239. doi:10.1016/j.msec.2010.12.004.
  6. ^ Laschi, Cecilia; Cianchetti, Matteo; Mazzolai, Barbara; Margheri, Laura; Follador, Maurizio; Dario, Paolo (January 2012). "Soft Robot Arm Inspired by the Octopus". Advanced Robotics. 26 (7): 709–727. doi:10.1163/156855312X626343.
  7. ^ Calisti, M; Giorelli, M; Levy, G; Mazzolai, B; Hochner, B; Laschi, C; Dario, P (1 September 2011). "An octopus-bioinspired solution to movement and manipulation for soft robots". Bioinspiration & Biomimetics. 6 (3): 036002. doi:10.1088/1748-3182/6/3/036002.
  8. ^ Giorgio Serchi, F.; Arienti, A.; Laschi, C. (April 2013). "Biomimetic Vortex Propulsion: Toward the New Paradigm of Soft Unmanned Underwater Vehicles". IEEE/ASME Transactions on Mechatronics. 18 (2): 484–493. doi:10.1109/TMECH.2012.2220978.
  9. ^ Margheri, L; Laschi, C; Mazzolai, B (1 June 2012). "Soft robotic arm inspired by the octopus: I. From biological functions to artificial requirements". Bioinspiration & Biomimetics. 7 (2): 025004. doi:10.1088/1748-3182/7/2/025004.
  10. ^ Mazzolai, B; Margheri, L; Cianchetti, M; Dario, P; Laschi, C (1 June 2012). "Soft-robotic arm inspired by the octopus: II. From artificial requirements to innovative technological solutions". Bioinspiration & Biomimetics. 7 (2): 025005. doi:10.1088/1748-3182/7/2/025005.
  11. ^ Cianchetti, Matteo; Ranzani, Tommaso; Gerboni, Giada; Nanayakkara, Thrishantha; Althoefer, Kaspar; Dasgupta, Prokar; Menciassi, Arianna (June 2014). "Soft Robotics Technologies to Address Shortcomings in Today's Minimally Invasive Surgery: The STIFF-FLOP Approach". Soft Robotics. 1 (2): 122–131. doi:10.1089/soro.2014.0001.
  12. ^ Morin, S. A.; Shepherd, R. F.; Kwok, S. W.; Stokes, A. A.; Nemiroski, A.; Whitesides, G. M. (16 August 2012). "Camouflage and Display for Soft Machines". Science. 337 (6096): 828–832. doi:10.1126/science.1222149.
  13. ^ Martinez, Ramses V.; Branch, Jamie L.; Fish, Carina R.; Jin, Lihua; Shepherd, Robert F.; Nunes, Rui M. D.; Suo, Zhigang; Whitesides, George M. (11 January 2013). "Robotic Tentacles with Three-Dimensional Mobility Based on Flexible Elastomers". Advanced Materials. 25 (2): 205–212. doi:10.1002/adma.201203002.
  14. ^ Steltz E., Mozeika A., Rembisz J., Corson N., and Jaeger H. M., “Jamming as an enabling technology for soft robotics”, in Proceedings of the SPIE: Electroactive Polymer Actuators and Devices (EAPAD), 7642, pp. 764225-764229, 2010. DOI: 10.1117/12.853182
  15. ^ Brown, E.; Rodenberg, N.; Amend, J.; Mozeika, A.; Steltz, E.; Zakin, M. R.; Lipson, H.; Jaeger, H. M. (25 October 2010). "Universal robotic gripper based on the jamming of granular material". Proceedings of the National Academy of Sciences. 107 (44): 18809–18814. doi:10.1073/pnas.1003250107.
  16. ^ Cheng N. G., Lobovsky M. B., Keating S. J., Setapen A. M., Gero K. I., Hosoi A. E., Iagnemma K. D., “Design and Analysis of a Robust, Low-cost, Highly Articulated manipulator enabled by jamming of granular media”, IEEE Int. Conf. on Robotics and Automation, pp. 4328-4333, 2012. DOI: 10.1109/ICRA.2012.6225373
  17. ^ Otake, Mihoko; Kagami, Yoshiharu; Inaba, Masayuki; Inoue, Hirochika (August 2002). "Motion design of a starfish-shaped gel robot made of electro-active polymer gel". Robotics and Autonomous Systems. 40 (2–3): 185–191. doi:10.1016/S0921-8890(02)00243-9.
  18. ^ Nawroth, Janna C; Lee, Hyungsuk; Feinberg, Adam W; Ripplinger, Crystal M; McCain, Megan L; Grosberg, Anna; Dabiri, John O; Parker, Kevin Kit (22 July 2012). "A tissue-engineered jellyfish with biomimetic propulsion". Nature Biotechnology. 30 (8): 792–797. doi:10.1038/nbt.2269.
  19. ^ Seok, Sangok; Onal, Cagdas Denizel; Cho, Kyu-Jin; Wood, Robert J.; Rus, Daniela; Kim, Sangbae (October 2013). "Meshworm: A Peristaltic Soft Robot With Antagonistic Nickel Titanium Coil Actuators". IEEE/ASME Transactions on Mechatronics. 18 (5): 1485–1497. doi:10.1109/TMECH.2012.2204070.
  20. ^ Shepherd; et al. "Multigait soft robot". Retrieved 27 July 2014. {{cite web}}: Explicit use of et al. in: |last1= (help)
  21. ^ Kennedy, edited by Hiroyasu Funakubo ; translated from the Japanese by J.B. (1987). Shape memory alloys. New York: Gordon and Breach Science Publishers. ISBN 2881241360. {{cite book}}: |first1= has generic name (help)CS1 maint: multiple names: authors list (link)
  22. ^ Cianchetti M., “Fundamentals on the Use of Shape Memory Alloys in Soft Robotics, in Interdisciplinary Mechatronics: Engineering Science and Research Development”, edited by M. K. Habib and J. Paulo Davim, pp. 227-254, Wiley-ISTE, 2013. DOI: 10.1002/9781118577516.ch10
  23. ^ Cianchetti, Matteo; Licofonte, Alessia; Follador, Maurizio; Rogai, Francesco; Laschi, Cecilia (9 July 2014). "Bioinspired Soft Actuation System Using Shape Memory Alloys". Actuators. 3 (3): 226–244. doi:10.3390/act3030226.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  24. ^ Ratna, Debdatta; Karger-Kocsis, J. (17 October 2007). "Recent advances in shape memory polymers and composites: a review". Journal of Materials Science. 43 (1): 254–269. doi:10.1007/s10853-007-2176-7.
  25. ^ Mirfakhrai, Tissaphern; Madden, John D.W.; Baughman, Ray H. (April 2007). "Polymer artificial muscles". Materials Today. 10 (4): 30–38. doi:10.1016/S1369-7021(07)70048-2.
  26. ^ De Greef, Aline; Lambert, Pierre; Delchambre, Alain (October 2009). "Towards flexible medical instruments: Review of flexible fluidic actuators". Precision Engineering. 33 (4): 311–321. doi:10.1016/j.precisioneng.2008.10.004.
  27. ^ Calisti M., Arienti A., Giannaccini M. E., Follador M., Giorelli M., Cianchetti M., Mazzolai B., Laschi C., and Dario P., “Study and fabrication of bioinspired Octopus arm mockups tested on a multipurpose platform”, IEEE Int. Conf. on Biomedical Robotics and Biomechatronics, pp. 461-466, 2010. DOI: 10.1109/BIOROB.2010.5625959
  28. ^ Cheng N. G., Ishigami G., Hawthorne S., Hao C., Hansen M., Telleria M., Playter R., and Iagnemma K., “Design and Analysis of a Soft Mobile Robot Composed of Multiple Thermally Activated Joints Driven by a Single Actuator”, IEEE Int. Conf. on Robotics and Automation, pp. 5207‐5212, 2010. DOI: 10.1109/ROBOT.2010.5509247
  29. ^ Yalcintas, Melek; Dai, Heming (1 October 1999). "Magnetorheological and electrorheological materials in adaptive structures and their performance comparison". Smart Materials and Structures. 8 (5): 560–573. doi:10.1088/0964-1726/8/5/306.
  30. ^ Liu, Andrea J.; Nagel, Sidney R. (5 November 1998). Nature. 396 (6706): 21–22. doi:10.1038/23819. {{cite journal}}: Missing or empty |title= (help)
  31. ^ Vanderborght, B.; Albu-Schaeffer, A.; Bicchi, A.; Burdet, E.; Caldwell, D.G.; Carloni, R.; Catalano, M.; Eiberger, O.; Friedl, W.; Ganesh, G.; Garabini, M.; Grebenstein, M.; Grioli, G.; Haddadin, S.; Hoppner, H.; Jafari, A.; Laffranchi, M.; Lefeber, D.; Petit, F.; Stramigioli, S.; Tsagarakis, N.; Van Damme, M.; Van Ham, R.; Visser, L.C.; Wolf, S. (December 2013). "Variable impedance actuators: A review". Robotics and Autonomous Systems. 61 (12): 1601–1614. doi:10.1016/j.robot.2013.06.009.
  32. ^ Yong-Lae Park; Seok Chang Ryu; Black, R.J.; Chau, K.K.; Moslehi, B.; Cutkosky, M.R. (December 2009). "Exoskeletal Force-Sensing End-Effectors With Embedded Optical Fiber-Bragg-Grating Sensors". IEEE Transactions on Robotics. 25 (6): 1319–1331. doi:10.1109/TRO.2009.2032965.
  33. ^ Yamada D., Maeno T., and Yamada Y., “Artificial finger skin having ridges and distributed tactile sensors used for grasp force control”, IEEE Int. Conf. on Intelligent Robots and Systems, pp. 686-691, vol.2, 2001. DOI: 10.1109/IROS.2001.976249
  34. ^ Kirchner, Nathan; Hordern, Daniel; Liu, Dikai; Dissanayake, Gamini (November 2008). "Capacitive sensor for object ranging and material type identification". Sensors and Actuators A: Physical. 148 (1): 96–104. doi:10.1016/j.sna.2008.07.027.
  35. ^ Ulmen J. and Cutkosky M., “A robust, low-cost and low-noise artficial skin for human-friendly robots”, Proc. IEEE Int. Conf. Rob. Autom., Anchorage, AK, pp. 4836-4841, 2010. DOI: 10.1109/ROBOT.2010.5509295
  36. ^ Ventrelli L., Beccai L., Mattoli V., Menciassi A., and Dario P., “Development of a stretchable skin-like tactile sensor based on polymer composites”, Proc. IEEE Int. Conf. Rob. Biomimetics, Gui-lin, China, pp. 123-128, 2009. DOI: 10.1109/ROBIO.2009.5420644
  37. ^ Cheng, M.-Y.; Tsao, C.-M.; Lai, Y.-Z.; Yang, Y.-J. (April 2011). "The development of a highly twistable tactile sensing array with stretchable helical electrodes". Sensors and Actuators A: Physical. 166 (2): 226–233. doi:10.1016/j.sna.2009.12.009.
  38. ^ Yong-Lae Park; Bor-Rong Chen; Wood, R. J. (August 2012). "Design and Fabrication of Soft Artificial Skin Using Embedded Microchannels and Liquid Conductors". IEEE Sensors Journal. 12 (8): 2711–2718. doi:10.1109/JSEN.2012.2200790.
  39. ^ Jung, Kwangmok; Kim, Kwang J.; Choi, Hyouk Ryeol (May 2008). "A self-sensing dielectric elastomer actuator". Sensors and Actuators A: Physical. 143 (2): 343–351. doi:10.1016/j.sna.2007.10.076.
  40. ^ a b Ken Caluwaerts, Jérémie Despraz, Atil Isçen, Andrew P. Sabelhaus, Jonathan Bruce, Benjamin Schrauwen, and Vytas SunSpiral, “Design and Control of Compliant Tensegrity Robots through Simulation and Hardware Validation,” Journal of the Royal Society Interface, Vol. 11, No 98. September 6, 2014; doi:10.1098/rsif.2014.0520 1742-5662
  41. ^ a b Brian Tietz, Ross Carnahan, Richard Bachmann, Roger Quinn, and Vytas SunSpiral, “Tetraspine: Robust Terrain Handling on a Tensegrity Robot Using Central Pattern Generators,” In Proceedings of 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2013), Wollongong, Australia, July 2013.
  42. ^ Brian T. Mirletz, In-Won Park, Thomas E. Flemons, Adrian K. Agogino, Roger D. Quinn, and Vytas SunSpiral, “Design and Control of Modular Spine-Like Tensegrity Structures”, in Proceedings of The 6th World Conference of the International Association for Structural Control and Monitoring (6WCSCM), Barcelona, Spain, July 2014.
  43. ^ Vytas SunSpiral, George Gorospe, Jonathan Bruce, Atil Iscen, George Korbel, Sophie Milam, Adrian Agogino, David Atkinson, “Tensegrity Based Probes for Planetary Exploration: Entry, Descent and Landing (EDL) and Surface Mobility Analysis,” in Proceedings of 10th International Planetary Probe Workshop, San Jose, California, June 2013.
  44. ^ Webster III, R. J; Jones, B. A. (2010). "Design and kinematic modeling of constant curvature continuum robots: a review". Int. J. Robot. Res. 29: 1661–1683.
  45. ^ Chirikjian, G. S. (1995). "Hyper-redundant manipulator dynamics: A continuum approximation". J. Adv. Robot. 9: 217–243.
  46. ^ Yekutieli, Y.; Sagiv-Zohar, R.; Aharonov, R.; Engel, Y.; Hochner, B.; Flash, T. (2005). "Dynamic model of the octopus arm. I. biomechanics of the octopus reaching movement". J. Neurophysiol. 94: 1443–1458.
  47. ^ Renda, Federico; Giorelli, M.; Calisti, M.; Cianchetti, M.; Laschi, C. "Dynamic Model of a Multibending Soft Robot Arm Driven by Cables". Robotics, IEEE Transactions on. doi:10.1109/TRO.2014.2325992.
  48. ^ a b Pfeifer, Rolf; Bongard, Josh (2006). How the Body Shapes the Way We Think. MIT Press. p. 424. ISBN 9780262162395. Cite error: The named reference "Rolf_BodyShapesWeThink" was defined multiple times with different content (see the help page).
  49. ^ Füchslin, Rudolf M.; Dzyakanchuk, Andrej; Flumini, Dandolo; Hauser, Helmut; Hunt, Kenneth J.; Luchsinger, Rolf; Reller, Benedikt; Scheidegger, Stephan; Walker, Richard (2013). "Morphological computation and morphological control: steps toward a formal theory and applications". Artificial Life. 19: 9–34. doi:10.1162/ARTL\_a\_00079.
  50. ^ Hauser, Helmut; Ijspeert, Auke J.; Füchslin, Rudolf M.; Pfeifer, Rolf; Maass, Wolfgang. "Towards a theoretical foundation for morphological computation with compliant bodies". Biological Cybernetics. 105: 1–19. doi:10.1007/s00422-012-0471-0.
  51. ^ Hauser, Helmut; Ijspeert, Auke J.; Füchslin, Rudolf M.; Pfeifer, Rolf; Maass, Wolfgang. "The role of feedback in morphological computation with compliant bodies". Biological Cybernetics. 106: 595–613. doi:10.1007/s00422-012-0471-0.
  52. ^ Nakajima, Kohei; Li, Tao; Hauser, Helmut; Pfeifer, Rolf (2014). "Exploiting short−term memory in soft body dynamics as a computational resource". Journal of the Royal Society Interface.
  53. ^ Zhao, Qian; Nakajima, Kohei; Sumioka, Hidenobu; Hauser, Helmut; Pfeifer, Rolf (2013). "Spine Dynamics As a Computational Resource in Spine-Driven Quadruped Locomotion". IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
  54. ^ Bonsignorio, Fabio (2013). "Quantifying the evolutionary self-structuring of embodied cognitive networks". Artificial Life. 19 (2): 267–289. doi:10.1162/ARTL_a_00109.
  55. ^ Bonsignorio, Fabio (2007). "Preliminary considerations for a quantitative theory of networked embodied intelligence". 50 Years of AI, Festschrift.
  56. ^ Bonsignorio, Fabio (2010). "On the stochastic stability and observability of controlled serial kinematic chains". ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis ESDA2010.
  57. ^ Bonsignorio, Fabio (2009). "Steps to a cyber-physical model of networked embodied anticipatory behavior". Proceedings of ABiALS 2008.
  58. ^ Bonsignorio, Fabio (2013). "The new science of physical cognitive systems". SAPERE, Studies in Applied Philosophy, Epistemology and Rational Ethics.
  59. ^ Garcia, M.; Chatterjee, A.; Ruina, A.; Coleman, M. (1998). "The simplest walking model: Stability, complexity, and scaling". J BIOMECH. ENG. TRANS. ASME. {{cite journal}}: Cite has empty unknown parameter: |1= (help)
  60. ^ "Cornell Ranger website". {{cite journal}}: Cite journal requires |journal= (help)
  61. ^ Touchette, Hugo; Lloyd, Seth (2003). "Information-theoretic approach to the study of control systems". Physica A.
  62. ^ Chirikjian, Gregory (2011). Stochastic models, Information Theory, and Lie groups, Vol 2. Birkhauser.
  63. ^ Der, Ralf; Martius, G.; Hesse, F. (2006). "Let it roll -- emerging sensorimotor coordination in a spherical robot". Proceedings of Artificial Life X. {{cite journal}}: Cite has empty unknown parameters: |1= and |2= (help)
  64. ^ Ay, Nihat; Bertschinger, N.; Der, Ralf (2008). "Predictive information and explorative behavior of autonomous robots". The European Physical Journal B-Condensed Matter and Complex Systems. {{cite journal}}: Cite has empty unknown parameters: |1= and |2= (help)
  65. ^ Olsson, Lars; Nehaiv, Chrystoper; Polani, Daniel (2004). "Information trade-offs and the evolution of sensory layouts". Proceedings of Artificial Life IX. {{cite journal}}: Cite has empty unknown parameters: |1= and |2= (help)
  66. ^ Lungarella, Max; Sporns, Olaf (2006). "Mapping information flow in sensorimotor network". PLOS Computational Biology. {{cite journal}}: Cite has empty unknown parameter: |1= (help)
  67. ^ Gomez, G.; Lungarella, Max; Tarapore, D. (2005). "Information-theoretic approach to embodied category learning". Proceedings of the 10th Int. Conf. on Artificial Life and Robotics. {{cite journal}}: Cite has empty unknown parameter: |1= (help)
  68. ^ Philpona, D.; O’ Regan, Kevin (2004). "Perception of the structure of the physical world using unknown multimodal sensors and effectors". Proceedings of Advances in Neural Information Processing Systems 16.
  69. ^ Prokopenko, M.; Gerasimov,, V.; Tanev, I. (2006). "Evolving spatiotemporal coordination in a modular robotic system". Proceedings of From Animals to Animats 9: 9th International Conference on the Simulation of Adaptive Behavior. {{cite journal}}: Cite has empty unknown parameter: |1= (help)CS1 maint: extra punctuation (link)
  70. ^ Tanev, I.; Ray, T.S.; Buller, A. (2005). "Automated evolutionary design, robustness, and adaptation of sidewinding locomotion of a simulated snake-like robot". IEEE Transactions on Robotics. {{cite journal}}: Cite has empty unknown parameter: |1= (help)
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