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Literature Review: Detection 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:
- self learning,
- action planning,
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.
THE PROBLEM — Detection has found considerable applications in the real world but it has some fundamental aspects that have never been formally discussed and experimented. Combinations of deep learning techniques or real-time detection algorithms together with controlled robots can be used to solve many real life detection, classification and execution problems. More requirements have to be met at industrial level for human-robot and multi-robot collaborative tasks, also for different environments and extreme setups.
CASE STUDIES — … buy this booklet from Amazon …
CONCLUSIONS — Despite systems being trained to reject everything other than the classes of interest, unknown objects from the open world end up being incorrectly detected as known objects, often with very high confidence. Deep learning concepts and Arduino Uno with robotic application are good enough for the purposes of simple object detection, classification and grasping. A framework for designing and training object detectors for mobile robots that are real-time capable can define a coarse structure that only depends on few hyperparameters. Collaborative robots are becoming more common on factory floors as well as regular environments, however, their safety still is not a fully solved issue. The open source simulation software Gazebo allows in a single environment to simulate not only the robotic system as a whole, but also the environment with which it must interact. The interaction between robots and persons finds applications in different areas such as video surveillance, health care, road safety, etc. A robotic detection device uses a novel subsurface radar with imaging and target classification to differentiate between dangerous landmines and harmless clutter. A Human Support Robot is able to perform a cleanliness inspection and also clean the food litter on the table by implementing a deep learning technique and planner framework. The aerial refueling of unmanned aerial vehicles is limited by the development of autonomous aerial refueling technology.
TEN FREE REFERENCES FROM THE INTERNET — … buy this booklet from Amazon …