Adaptable robotics: Difference between revisions

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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]], [[Manufacturing|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.

=== Adaptability ===

A robot can be defined as adaptive when it has capabilities such as intrinsic safety and performance without compromise, the ability to learn, and the capacity to perform tasks traditional robots are not capable of. These capabilities can be achieved through force control technology, hierarchical intelligence, and other innovative approaches.<ref name=":12">{{Cite web |last=Content |first=Sponsored |date=2019-07-29 |title=Why Adaptive Robots are the Next Big Thing |url=https://www.roboticsbusinessreview.com/content-from-our-sponsor/why-adaptive-robots-are-the-next-big-thing/ |access-date=2023-11-09 |website=Robotics Business Review |language=en-US}}</ref> John Adler’s invention in 1994, the [[Cyberknife (device)|cyberknife]], is a robotic surgery system that is capable of using ultra-fine precision in medical procedures which demonstrates such adaptations.<ref name=":03">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>

=== Sensing and Perception ===

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=== Actuation ===

Actuation in robotic systems allows the robot to move. Adaptable [[Actuator|actuators]] typically function in response to environmental changes, such as changes in temperature which may change the shape of the actuator. Thus, altering functionality.<ref>{{Cite web |title=Actuators: what is it, definition, types and how does it work |url=https://www.progressiveautomations.com/pages/actuators |access-date=2023-11-09 |website=Progressive Automations |language=en}}</ref> Self-powering (untethered) actuation is achievable, especially in soft robotics where external stimuli can change the shape of an actuator, creating mechanical energy.<ref name=":22">Y.{{cite journal |last1=Chi, Y.|first1=Yinding |last2=Zhao, Y.|first2=Yao |last3=Hong, Y.|first3=Yaoye |last4=Li, and J.|first4=Yanbin |last5=Yin, “A|first5=Jie |title=A Perspective on Miniature Soft Robotics: Actuation, Fabrication, Control, and Applications,” |journal=Advanced intelligentIntelligent systems,Systems Apr.|date=February 2023,2024 doi:|volume=6 <nowiki>https://|issue=2 |article-number=2300063 |doi.org/=10.1002/aisy.202300063</nowiki>.\ |doi-access=free }}</ref> In 1989 Rodney Brooks created Ghengis, a hexapedal robot capable of traversing difficult terrain.<ref name=":03" /> The Hexapedal model uses six actuators for mobility and has remained prominent with modern hexapedal models like the [[Rhex]].

==Software==

The kits come with an [[open software]] platform tailored to a range of common robotic functions. The kits also come with common robotics hardware that connects easily with the software (infrared [[sensor]]s, [[Servo motor|motors]], microphone and video camera), which add to the capabilities of the robot.<ref>{{Cite web |date=2022-06-22 |title=Benefits of programmable robot kits for beginners |url=https://twitiq.com/robotics-kits-for-adults/ |access-date=2023-06-26 |website=Twit IQ |language=en-US}}</ref>

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 |lastlast1=Tokhi |firstfirst1=Mohammad |last2=Gurvinder |first2=Virk |publisher=World Scientific|year=2016|isbn=9789813149120|___location=Hackensack, NJ|pages=159}}</ref>

== Types of Adaptable Robots: ==

=== Soft Robots ===

Robotics with soft grippers is an emerging field in the adaptable robotic scene which is based on the [[Venus flytrap]]. Two soft robotic surfaces provide enveloping and pinching grasp modules. This technology is tested in a variety of environments to determine the effects of diverse objects, errors of object position, and SRSsoft (Softrobotic Robotic Surface)surface installation on grasping capacity.<ref>{{Citecite journal |lastlast1=Xiao |firstfirst1=Wei |last2=Liu |first2=Chang |last3=Hu |first3=Dean |last4=Yang |first4=Gang |last5=Han |first5=Xu |date=2022-04 |title=Soft robotic surface enhances the grasping adaptability and reliability of pneumatic grippers |url=https://linkinghub.elsevier.com/retrieve/pii/S0020740322000315 |journal=International Journal of Mechanical Sciences |languagedate=enApril 2022 |volume=219 |pagesarticle-number=107094 |doi=10.1016/j.ijmecsci.2022.107094 }}</ref> Untethered actuation is achievable, especially in soft robots with LCPsliquid crystal polymers, a category of stimuli-responsive materials with two way shape memory effect. This can allow the LCPsliquid crystal polymers to generate mechanical energy by changing shape in response to external stimuli, hence untethered actuation.<ref name=":23">Y.{{cite journal |last1=Chi, Y.|first1=Yinding |last2=Zhao, Y.|first2=Yao |last3=Hong, Y.|first3=Yaoye |last4=Li, and J.|first4=Yanbin |last5=Yin, “A|first5=Jie |title=A Perspective on Miniature Soft Robotics: Actuation, Fabrication, Control, and Applications,” |journal=Advanced intelligentIntelligent systems,Systems Apr.|date=February 2023,2024 doi:|volume=6 <nowiki>https://|issue=2 |article-number=2300063 |doi.org/=10.1002/aisy.202300063</nowiki>.\ |doi-access=free }}</ref>

=== Modular Robots ===

Robots designed for the outdoors that adapt to changing landscapes and obstacles. These are constructed like a chain of individual modules with simple hinge joints, enabling modular robots to morph themselves into various shapes to traverse terrain. Some of these forms include configurations like [[spider]], serpentine, and loop.<ref>{{Citecite webnews |titlelast1=ModularYim Robots|first1=Mark -|last2=Zhang IEEE|first2=Ying Spectrum|last3=Duff |first3=David |title=Modular Robots |url=https://spectrum.ieee.org/modular-robots |access-datework=2023-11-09IEEE |website=spectrum.ieee.orgSpectrum |languagedate=en1 February 2002 }}</ref>

=== Swarm Robotics ===

Field of robotics utilizing swarm intelligence to groups of simple homogeneous robots. Swarm robots follow algorithms, usually designed to mimic the behavior of real animals, in order to determine their movements in response to environmental stimuli.<ref>“Swarm Robotics - an overview | ScienceDirect Topics,” www.sciencedirect.com. <nowiki>https://www.sciencedirect.com/topics/engineering/swarm-robotics</nowiki></ref><ref name=":3">A.{{cite Iglesias,book A|doi=10. Gálvez, and P1016/B978-0-12-819714-1. Suárez, “Chapter 15 00026-9 |chapter=Swarm robotics – a case study: batBat robotics,” ScienceDirect,|title=Nature-Inspired Jan.Computation 01,and Swarm Intelligence |date=2020. <nowiki>https://www.sciencedirect.com/science/article/pii/B9780128197141000269#s0100</nowiki>|last1=Iglesias (accessed|first1=Andrés Nov.|last2=Gálvez 07,|first2=Akemi |last3=Suárez |first3=Patricia 2023).|pages=273–302 |isbn=978-0-12-819714-1 }}</ref>

=== Biohybrid Robots ===

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> Insect-machine hybrid robots, also known as cyborg insects or insect biobots, is the fusion of a living insect and artificial control system integrated with its body to drive its locomotion or behaviours.<ref>{{Cite journal |last1=Sato |first1=Hirotaka |last2=Vo Doan |first2=Tat Thang |last3=Kolev |first3=Svetoslav |last4=Huynh |first4=Ngoc Anh |last5=Zhang |first5=Chao |last6=Massey |first6=Travis L. |last7=van Kleef |first7=Joshua |last8=Ikeda |first8=Kazuo |last9=Abbeel |first9=Pieter |last10=Maharbiz |first10=Michel M. |date=March 2015 |title=Deciphering the Role of a Coleopteran Steering Muscle via Free Flight Stimulation |journal=Current Biology |volume=25 |issue=6 |pages=798–803 |doi=10.1016/j.cub.2015.01.051 |pmid=25784033 |bibcode=2015CBio...25..798S |issn=0960-9822|doi-access=free }}</ref><ref>{{Cite journal |last1=Vo Doan |first1=Tat Thang |last2=Tan |first2=Melvin Y.W. |last3=Bui |first3=Xuan Hien |last4=Sato |first4=Hirotaka |date=February 2018 |title=An Ultralightweight and Living Legged Robot |url=https://www.liebertpub.com/doi/10.1089/soro.2017.0038 |journal=Soft Robotics |language=en |volume=5 |issue=1 |pages=17–23 |doi=10.1089/soro.2017.0038 |pmid=29412086 |bibcode=2018SoftR...5...17V |issn=2169-5172|url-access=subscription }}</ref><ref>{{Cite journal |last1=Cao |first1=Feng |last2=Sato |first2=Hirotaka |date=August 2019 |title=Insect–Computer Hybrid Robot Achieves a Walking Gait Rarely Seen in Nature by Replacing the Anisotropic Natural Leg Spines With Isotropic Artificial Leg Spines |journal=IEEE Transactions on Robotics |volume=35 |issue=4 |pages=1034–1038 |doi=10.1109/TRO.2019.2903416 |bibcode=2019ITRob..35.1034C |issn=1552-3098}}</ref><ref>{{Cite book |last1=Latif |first1=T. |last2=Bozkurt |first2=A. |chapter=Line following terrestrial insect biobots |date=August 2012 |title=2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society |publisher=IEEE |pages=972–975 |doi=10.1109/EMBC.2012.6346095 |pmid=23366056 |isbn=978-1-4577-1787-1}}</ref><ref>{{Cite journal |last1=Ma |first1=Songsong |last2=Chen |first2=Yuansheng |last3=Yang |first3=Songlin |last4=Liu |first4=Shen |last5=Tang |first5=Lingqi |last6=Li |first6=Bing |last7=Li |first7=Yao |date=January 2023 |title=The Autonomous Pipeline Navigation of a Cockroach Bio-Robot with Enhanced Walking Stimuli |journal=Cyborg and Bionic Systems |language=en |volume=4 |article-number=0067 |doi=10.34133/cbsystems.0067 |issn=2692-7632 |pmc=10631459 |pmid=38026542}}</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.

[[Reinforcement learning|Learning from demonstration]] (LfD) is a strategy for transferring human motion skills to robots. The primary goal is to identify significant movement primitives (MPs), significant movements humans make, from demonstration and remake these motions to adapt the robot to that motion. There have been a few issues with robots being unable to adapt skills learned throughby LfDlearning from demonstration to new environments (a change from the scenario in which the robot was given initial demonstrations). These Issuesissues with LfDlearning from demonstration have been addressed with a learning model based on a nonlinear dynamic system (DS) which encodes trajectories as dynamic motion primitive, (DMP). DMPswhich are similar to MPsmovement primitives, but they are significant movements represented by a mathematical equation; equation variables change with the changing environment, altering the motion performed. The trajectories recorded through these systems have proven to apply to a wide variety of environments making the robots more effective in their respective spheres. LfDLearning from demonstration has progressed the applicability of robotics in fields where precision is essential, such as surgical environments.<ref name=":5">{{Citecite journal |lastlast1=Teng |firstfirst1=Tao |last2=Gatti |first2=Matteo |last3=Poni |first3=Stefano |last4=Caldwell |first4=Darwin |last5=Chen |first5=Fei |date=2023-06 |title=Fuzzy dynamical system for robot learning motion skills from human demonstration |url=https://linkinghub.elsevier.com/retrieve/pii/S0921889023000453 |journal=Robotics and Autonomous Systems |languagedate=enJune 2023 |volume=164 |pagesarticle-number=104406 |doi=10.1016/j.robot.2023.104406 }}</ref>

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">{{Citecite journal |lastlast1=Okamura |firstfirst1=Allison |last2=Mataric |first2=Maja |last3=Christensen |first3=Henrik |date=2010-09 |title=Medical and Health-Care Robotics |url=http://ieeexplore.ieee.org/document/5569021/ |journal=IEEE Robotics & Automation Magazine |date=September 2010 |volume=17 |issue=3 |pages=26–37 |doi=10.1109/MRA.2010.937861 |issnbibcode=10702010IRAM...17c..26O |hdl=1853/37375 |hdl-9932access=free }}</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">{{Citecite journal |lastlast1=Soori |firstfirst1=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 |languagedate=en2023 |volume=3 |pages=54–70 |doi=10.1016/j.cogr.2023.04.001 |doi-access=free }}</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" />

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[[Category:Robot kits]]

[[Category:Adaptable robotics]]

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