Robotics problem: Motion

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Literature Review: Motion 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.

4 Motion

THE PROBLEM — A mobile robot can be defined as a mechanical system capable of moving in its environment in an autonomous manner. Several algorithms and electro-mechanical architectures can be employed at different level of accuracy and precision, in accordance with the requirements of the specific application. One of the most difficult challenges in robot motion planning is to account for the behavior of other moving agents.

CASE STUDIES — … buy this booklet from Amazon …

CONCLUSIONS — Autonomous mobile robots must be equipped with sensors, actuators and regulators. There are four main components involved in creating a motion plan: perception, roadmap construction, collision detection, path search. At a high level, motion planning problems can be solved exactly, while at a low level, sampling techniques can eliminate the need to exactly reason about geometric minutiae. Taking inspiration from nature, robotics is used to suggest the advantages and benefits of employing adaptive controllers in conjunction with optimization strategies, such as evolutionary algorithms. Recent works in control of robotic systems have effectively leveraged local, low-dimensional embeddings of high-dimensional dynamics. A biased-sampler uses prior knowledge in a “soft way” avoiding the hard-commitment to complete paths, induced by databases methods, which may need costly repairing when there are significant changes in the workspace. Heuristic approaches converge faster to an acceptable solution. No predictive model is perfect: an agent can always move unexpectedly, in a way that is not predicted or not assigned sufficient probability, leading to a collision. There is an advantage of using robots in agriculture as economics models show that the net returns can increase by up to 22% compared to the current practice of using conventional machines in row crop production. Path finding is the front-end of most motion planning systems, and the trajectory optimization usually serves as the back-end.

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

“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