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Robotics

Robotics problem: Soft

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Literature Review: Soft problem for Robotics

This “Ten Problems for Robotics in the 2020s” booklet identifies ten relevant areas from very recent contributions put forward at academic level in the form journal articles, conference proceedings and students theses. Ten freely accessible internet references have been selected for each area and direct links are provided at the end of each chapter for own consultation. Our selected references do not intend to mirror ranking indexes nor establish novel classifications. On the contrary, they are meant to represent peer-reviewed, diverse and scientifically-sound case studies for vertical dissemination aimed at non-specialist readers. They will also be able to scoop even more references through the bibliography that is reported at the end of each selected reference.

Without further ado, these are the ten problems that we are going to introduce in this booklet:

  1. self learning, 
  2. manipulation,
  3. research,
  4. motion,
  5. detection,
  6. action planning,
  7. simulation,
  8. soft,
  9. education,
  10. accountability.

Each problem has its own dedicated chapter made of an introductory section, a short presentation of the ten selected references and a conclusions section.

The final chapter of this booklet will report the conclusions from each chapter again in order to provide a complete executive summary.


8 Soft

THE PROBLEM — Soft Robotics has been enhancing physical potentialities of robotic structures amplifying the flexibility, rigidity and the strength and hence, accelerating their performance. Different from monofunctional assembled soft robots, the reconfigurable soft robots have multiple arrangements. 3D-printed or even 4D-printed smart materials can be used for frames and sensors.

CASE STUDIES — … buy this booklet from Amazon …

CONCLUSIONS — In contrast to conventional robots composed of rigid materials, soft robots made from soft materials offer remarkable advantages in locomotion, manipulation, human–robot interaction and confined environments tasks. The combination of deep reinforcement learning and imitation learning techniques seems now the best strategy to train soft robots. Two-way and multiple-way shape memory polymers offer unique opportunities to realize untethered soft robots with programmable morphology and/or properties, repeatable actuation, and advanced multi-functionalities. Recycling can be applied in every area of the soft robotics product life cycle: production, product use, and disposal. Metamaterials can be programmed before printing to target specific mechanical properties, in particular heterogeneous stiffness and anisotropic behaviour. Four-dimensional printing and machine learning techniques provide new possibilities for developing stand-alone closed-loop 4D-printed soft robots. Application of soft robotics in surgical instrument inside human body is still in its infancy. Soft robots can support patients by providing medicine to them without the assistance of the nursing staff during injury or illness. The field of soft wearable devices requires new methodologies that empathize with patients’ needs to analyze their physical condition and not only to cope with people demands. A soft inflatable robot can successfully traverse the interior space of a range of diameter pipes using pneumatic and without the need to adjust rigid, mechanical components.

TEN FREE REFERENCES FROM THE INTERNET — … buy this booklet from Amazon …


Robotics
“Ten Problems for Robotics in the 2020s” booklet for Amazon Kindle, 2020; click on the cover to go to the dedicated Amazon listing page

By TenProblems

Literature Reviews for Inquisitive Minds