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Robotics

Robotics problem: Simulation

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Literature Review: Simulation 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.


7 Simulation

THE PROBLEM — Simulators are an important tool in robotics that is used to develop robot software and generate synthetic data for machine learning algorithms. Unfortunately, in many cases, models and policies trained in simulation are not seamlessly transferable to the real systems. It is still true for traditional robotic arms in manufacturing or medicine and for unmanned aerial vehicles swarms.

CASE STUDIES — … buy this booklet from Amazon …

CONCLUSIONS — Developing and testing robot software can be challenging in the real world and leveraging simulators is one solution to these challenges. Fundamental problems in robotics such as motion planning and perception can be solved in simulation and solutions transferred to the physical robot. Since perfect simulations are unfeasible, several techniques are used to improve transfer to the real world. For mobile cooperative robots, network and control strategies should be incorporated in the simulation to assess how different techniques perform. Medical robot simulations show that redundancy does not necessarily lead to a high dexterity, and can be compensated or outperformed by structural optimization. A simulation environment for reinforcement learning has high-fidelity to model vehicle dynamics and guarantees successful simulation-to-real world transfer. A computer model of industrial robotic stations can be designed using for example the K-Roset software for Kawasaki robots. Microsoft’s Robotics Developer Studio is an integrated environment for robot control and simulation research under the Windows operating system. Previous work that leveraged concurrent multi-UAS simulations was extended to be useful for underwater, aerial and ground vehicles. Drone swarm control based on brain signals could provide various industries such as military service or industry disaster.

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