Adaptable robotics: Difference between revisions

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{{short description|none}}

'''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. https://www.g2.com/articles/history-of-robots (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>

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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">{{cite journal |last1=Chi |first1=Yinding |last2=Zhao |first2=Yao |last3=Hong |first3=Yaoye |last4=Li |first4=Yanbin |last5=Yin |first5=Jie |title=A Perspective on Miniature Soft Robotics: Actuation, Fabrication, Control, and Applications |journal=Advanced Intelligent Systems |date=February 2024 |volume=6 |issue=2 |article-number=2300063 |doi=10.1002/aisy.202300063 |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==

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=== 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 soft robotic surface installation on grasping capacity.<ref>{{cite journal |last1=Xiao |first1=Wei |last2=Liu |first2=Chang |last3=Hu |first3=Dean |last4=Yang |first4=Gang |last5=Han |first5=Xu |title=Soft robotic surface enhances the grasping adaptability and reliability of pneumatic grippers |journal=International Journal of Mechanical Sciences |date=April 2022 |volume=219 |pagesarticle-number=107094 |doi=10.1016/j.ijmecsci.2022.107094 }}</ref> Untethered actuation is achievable, especially in soft robots with liquid crystal polymers, a category of stimuli-responsive materials with two way shape memory effect. This can allow the liquid crystal polymers to generate mechanical energy by changing shape in response to external stimuli, hence untethered actuation.<ref name=":23">{{cite journal |last1=Chi |first1=Yinding |last2=Zhao |first2=Yao |last3=Hong |first3=Yaoye |last4=Li |first4=Yanbin |last5=Yin |first5=Jie |title=A Perspective on Miniature Soft Robotics: Actuation, Fabrication, Control, and Applications |journal=Advanced Intelligent Systems |date=February 2024 |volume=6 |issue=2 |article-number=2300063 |doi=10.1002/aisy.202300063 |doi-access=free }}</ref>

=== Modular Robots ===

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=== 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 |url=https://linkinghub.elsevier.com/retrieve/pii/S0960982215000834 |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 |url=https://ieeexplore.ieee.org/document/8681737 |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 |chapter-url=https://ieeexplore.ieee.org/document/6346095 |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 |pagearticle-number=0067 |doi=10.34133/cbsystems.0067 |issn=2692-7632 |pmc=10631459 |pmid=38026542}}</ref>.

== Applications ==

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]] is a strategy for transferring human motion skills to robots. The primary goal is to identify significant movement primitives, 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 by learning from demonstration to new environments (a change from the scenario in which the robot was given initial demonstrations). These issues with learning from demonstration have been addressed with a learning model based on a nonlinear dynamic system which encodes trajectories as dynamic motion primitive, which are similar to movement 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. Learning from demonstration has progressed the applicability of robotics in fields where precision is essential, such as surgical environments.<ref name=":5">{{cite journal |last1=Teng |first1=Tao |last2=Gatti |first2=Matteo |last3=Poni |first3=Stefano |last4=Caldwell |first4=Darwin |last5=Chen |first5=Fei |title=Fuzzy dynamical system for robot learning motion skills from human demonstration |journal=Robotics and Autonomous Systems |date=June 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">{{cite journal |last1=Okamura |first1=Allison |last2=Mataric |first2=Maja |last3=Christensen |first3=Henrik |title=Medical and Health-Care Robotics |journal=IEEE Robotics & Automation Magazine |date=September 2010 |volume=17 |issue=3 |pages=26–37 |doi=10.1109/MRA.2010.937861 |bibcode=2010IRAM...17c..26O |hdl=1853/37375 |hdl-access=free }}</ref> Biohybrid robotics have medical applications utilizing biodegradable components to allow robots to function safely within the human body.<ref name=":4" />

AI, machine learning, and deep learning 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 |last1=Soori |first1=Mohsen |last2=Arezoo |first2=Behrooz |last3=Dastres |first3=Roza |title=Artificial intelligence, machine learning and deep learning in advanced robotics, a review |journal=Cognitive Robotics |date=2023 |volume=3 |pages=54–70 |doi=10.1016/j.cogr.2023.04.001 |doi-access=free }}</ref>

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[[Category:Robot kits]]

[[Category:Adaptable robotics]]

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