Adaptable robotics: Difference between revisions

Revision as of 03:26, 16 November 2023

Revision as of 03:30, 16 November 2023

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'''Adaptable Robotics''' refers to a field of [[robotics]] with a focus on creating robotic systems capable of adjusting their hardware and software components to perform a wide range of tasks while adapting to varying environments. The 1960s introduced robotics into the industrial field.<ref name=":02">P. Thomson, “An Exhaustive History of Robotics,” G2, Aug. 30, 2019. <nowiki>https://www.g2.com/articles/history-of-robots</nowiki> (accessed Oct. 30, 2023).</ref> Since then, the need to make robots with new forms of [[Actuator|actuation]], adaptability, [[Peripheral|sensing and perception]], and even the [[Artificial intelligence|ability to learn]] stemmed the field of adaptable robotics. Significant developments such as the PUMA robot, manipulation research, [[soft robotics]], [[swarm robotics]], [[Artificial intelligence|AI]], [[Cobot|cobots]], bio-inspired approaches, and more ongoing research have advanced the adaptable robotics field tremendously. Adaptable robots are usually associated with their [[Robot kit|development kit,]] typically used to create autonomous mobile robots. In some cases, an adaptable kit will still be functional even when certain components break.<ref>{{Cite news |date=2015-05-27 |title=Adaptable robots 'on their way' to the home |language=en-GB |work=BBC News |url=https://www.bbc.com/news/science-environment-32884768 |access-date=2023-11-09}}</ref>

Adaptable Roboticsrobotics systems successfully adapt to their environment using techniques such as [[modular design]], [[machine learning]], and [[sensor]] feedback. Using this, they have revolutionized various industries and can address many real-world challenges in the [[medical]], [[industrial]], [[extraterrestrial]], and [[Experiment|experimental]] fields. There are still many challenges to overcome in adaptable robotics, which presents opportunities for growth in the field.

== Fundamental Concepts:concepts ==

An adaptable robot typically has attributes that distinguish it from robots that perform their task regardless of external factors. Four concepts that adaptable robots utilize to make this distinction are adaptability, sensing and perception, learning and intelligence, and actuation.

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The process of modifying a robot to achieve varying capabilities such as collaboration could merely include the selection of a module, the exchange of modules, robotic instruction via software, and execution.<ref>{{Cite book|title=Advances In Cooperative Robotics - Proceedings Of The 19th International Conference On Clawar 2016 |last=Tokhi |first=Mohammad |last2=Gurvinder |first2=Virk |publisher=World Scientific|year=2016|isbn=9789813149120|___location=Hackensack, NJ|pages=159}}</ref>

== Types of Adaptable Robots: ==

=== Soft Robots ===

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Biohybrid robotics use living tissues or cells to provide machines with functions that would be difficult to achieve otherwise. For instance, muscle cells have been utilized to allow certain biohybrid robots to move. Swarm robotics combine with biohybrid in certain cases, especially within the medical field <ref name=":3" /><ref name=":4">{{Cite web |last=Conocimiento |first=Ventana al |date=2019-10-21 |title=Biohybrid robots, the next step in the robotic revolution |url=https://www.bbvaopenmind.com/en/technology/robotics/biohybrid-robots-the-robotic-revolution/ |access-date=2023-11-09 |website=OpenMind |language=en-US}}</ref>

== Applications of Adaptable Robotics ==

Adaptable robotics possess capabilities that have made them applicable to many fields including, but not limited to, the medical, industrial, and experimental fields.

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In the medical field, SAR technology focuses on taking sensory data from wearable peripherals to perceive the user’s state of being. The information gathered enables the machine to provide personalized monitoring, motivation, and coaching for rehabilitation. Intuitive Physical HRI and interfaces between humans and robots allow functionalities like recording the motions of a surgeon to infer their intent, determining the mechanical parameters of human tissue, and other sensory data to use in medical scenarios.<ref name=":6">{{Cite journal |last=Okamura |first=Allison |last2=Mataric |first2=Maja |last3=Christensen |first3=Henrik |date=September 2010 |title=Medical and Health-Care Robotics |url=http://ieeexplore.ieee.org/document/5569021/ |journal=IEEE Robotics & Automation Magazine |volume=17 |issue=3 |pages=26–37 |doi=10.1109/MRA.2010.937861 |issn=1070-9932}}</ref> Biohybrid robotics have medical applications utilizing biodegradable components to allow robots to function safely within the human body.<ref name=":4" />

AI, Machinemachine Learninglearning, and Deepdeep Learninglearning have allowed advances in adaptable robotics such as autonomous navigation, object recognition and manipulation, natural language processing, and predictive maintenance. These technologies have been essential in the development of cobots (collaborative robots), which are robots capable of working alongside humans capable of adapting to changing environments.<ref name=":7">{{Cite journal |last=Soori |first=Mohsen |last2=Arezoo |first2=Behrooz |last3=Dastres |first3=Roza |date=2023 |title=Artificial intelligence, machine learning and deep learning in advanced robotics, a review |url=https://linkinghub.elsevier.com/retrieve/pii/S2667241323000113 |journal=Cognitive Robotics |language=en |volume=3 |pages=54–70 |doi=10.1016/j.cogr.2023.04.001}}</ref>

In the industrial field, AI, Machine Learning, and Deep Learning can be used to perform quality control checks on manufactured products, identify defects in products, and alert production teams to make necessary changes in real-time.<ref name=":7" />

== Challenges and Limitationslimitations ==

Systems that involve physical collaboration between humans and robots are difficult to design well due to human uncertainty. Humans alter the force of their motions regularly due to human factors like emotion, biological processes, and other extraneous factors unknown to a robot. This can make sensory data difficult to quantify for successful adaptation in robots. Furthermore, the specific needs, characteristics, and preferences that a patient in a medical scenario may need vary from person to person. Adaptable robotic systems need extended time to adapt to the new environment introduced from patient to patient.<ref name=":5" /><ref name=":6" />