Autonomous nanomotors have become the new paradigm for current research as they are expected to shift the momentum in the development of next‐generation technologies. However, there is a grand challenge in gaining control over the nanomotors’ motion, speed, directionality, and using biocompatible fuels to power them. Currently, light is recognized for powering micromotors with advancement in using visible light for driving motion at the nanoscale regime. In this context, micron‐scaled motors are fabricated but they contain metal surfaces and fabrication is quite laborious. Herein, encapsulation of plant organelles into supramolecular assemblies for active motion is conducted to fabricate bio‐nanomotors, utilizing the natural photosynthesis process for powering motion at the nanoscale. The oxygen produced by the water‐splitting reaction by plant organelles in visible light and the photophoresis effect due to the transparent nature of the supramolecular assembly are the main driving forces for bio‐nanomotors. The bio‐nanomotors are observed to have propelled motion with speed reaching up to 120.42 ± 12 μm s−1, together with on‐demand reversible on/off motion and real‐time control over change in directionality at the nanoscale. The observed results shift the momentum toward harnessing energy from natural processes to power nanosystems for varied applications.
自主纳米电机已经成为当前研究的新范式，因为它们有望改变下一代技术发展的势头。然而，在控制纳米马达的运动、速度、方向性和使用生物相容燃料为它们提供动力方面存在着巨大的挑战。目前，光被认为是驱动微电机的动力，在纳米尺度下使用可见光驱动运动。在这种情况下，微米级电机被制造出来，但它们含有金属表面，制造起来相当费力。在此，将植物细胞器封装成用于主动运动的超分子组件来制造生物纳米马达，利用自然光合作用过程为纳米级运动提供动力。植物细胞器在可见光下的水分解反应所产生的氧气以及超分子组装的透明性质所产生的光致导入效应是生物纳米马达的主要驱动力。研究人员观察到，生物纳米马达的推进运动速度可达120.42±12 μm s - 1，同时具有随需可逆的开/关运动和对纳米级方向变化的实时控制。观察到的结果改变了利用自然过程中的能量为各种应用的纳米系统提供动力的势头。
{"title":"Photosynthesis Drives the Motion of Bio‐nanomotors","authors":"M. Mathesh, D. Wilson","doi":"10.1002/aisy.202000028","DOIUrl":"https://doi.org/10.1002/aisy.202000028","url":null,"abstract":"Autonomous nanomotors have become the new paradigm for current research as they are expected to shift the momentum in the development of next‐generation technologies. However, there is a grand challenge in gaining control over the nanomotors’ motion, speed, directionality, and using biocompatible fuels to power them. Currently, light is recognized for powering micromotors with advancement in using visible light for driving motion at the nanoscale regime. In this context, micron‐scaled motors are fabricated but they contain metal surfaces and fabrication is quite laborious. Herein, encapsulation of plant organelles into supramolecular assemblies for active motion is conducted to fabricate bio‐nanomotors, utilizing the natural photosynthesis process for powering motion at the nanoscale. The oxygen produced by the water‐splitting reaction by plant organelles in visible light and the photophoresis effect due to the transparent nature of the supramolecular assembly are the main driving forces for bio‐nanomotors. The bio‐nanomotors are observed to have propelled motion with speed reaching up to 120.42 ± 12 μm s−1, together with on‐demand reversible on/off motion and real‐time control over change in directionality at the nanoscale. The observed results shift the momentum toward harnessing energy from natural processes to power nanosystems for varied applications.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"57 5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87717623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chao Zhang, Pingan Zhu, Yangqiao Lin, Zhongdong Jiao, J. Zou
A state‐of‐the‐art review of the modular soft robots (MSRs) is presented, with an outlook on the challenges and future directions of intelligent MSRs. In contrast to conventional robots composed of rigid materials, soft robots made from soft materials offer remarkable advantages in achieving various adaptive locomotion, manipulating delicate objects, providing safe human–robot interaction and adapting to confined environments due to their excellent compliance and adaptability, which have the potential to be widely used in numerous applications such as medical, exploration and rescue devices, etc. Unlike fixed‐morphology soft robots, modularization of soft robots is a low‐cost and rapid strategy that enables them to adapt to changing tasks and environments by rearranging the connectivity of module units and attain complex functionalities such as self‐assembly, self‐repair, or self‐replication. Although MSRs exhibit many advantages, they are still in the nascent stage with plenty of challenges. Herein, first the materials, fabrication, actuation, sensor, and control of various modular units in MSRs are introduced. Then, some main connection methods between modular units are summarized. Finally, the applications, challenges, and developing directions of intelligent MSRs are discussed.
{"title":"Modular Soft Robotics: Modular Units, Connection Mechanisms, and Applications","authors":"Chao Zhang, Pingan Zhu, Yangqiao Lin, Zhongdong Jiao, J. Zou","doi":"10.1002/aisy.201900166","DOIUrl":"https://doi.org/10.1002/aisy.201900166","url":null,"abstract":"A state‐of‐the‐art review of the modular soft robots (MSRs) is presented, with an outlook on the challenges and future directions of intelligent MSRs. In contrast to conventional robots composed of rigid materials, soft robots made from soft materials offer remarkable advantages in achieving various adaptive locomotion, manipulating delicate objects, providing safe human–robot interaction and adapting to confined environments due to their excellent compliance and adaptability, which have the potential to be widely used in numerous applications such as medical, exploration and rescue devices, etc. Unlike fixed‐morphology soft robots, modularization of soft robots is a low‐cost and rapid strategy that enables them to adapt to changing tasks and environments by rearranging the connectivity of module units and attain complex functionalities such as self‐assembly, self‐repair, or self‐replication. Although MSRs exhibit many advantages, they are still in the nascent stage with plenty of challenges. Herein, first the materials, fabrication, actuation, sensor, and control of various modular units in MSRs are introduced. Then, some main connection methods between modular units are summarized. Finally, the applications, challenges, and developing directions of intelligent MSRs are discussed.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85802232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Diverse synaptic plasticity with a wide range of timescales in biological synapses plays an important role in memory, learning, and various signal processing with exceptionally low power consumption. Emulating biological synaptic functions by electric devices for neuromorphic computation has been considered as a way to overcome the traditional von Neumann architecture in which separated memory and information processing units require high power consumption for their functions. Synaptic devices are expected to conduct complex signal processing such as image classification, decision‐making, and pattern recognition in artificial neural networks. Among various materials and device architectures for synaptic devices, 2D materials and their van der Waals (vdW) heterostructures have been attracting tremendous attention from researchers based on their capacity to mimic unique synaptic plasticity for neuromorphic computing. Herein, the basic operations of biological synapses and physical properties of 2D materials are discussed, and then 2D materials and their vdW heterostructures for advanced synaptic operations with novel working mechanisms are reviewed. In particular, there is a focus on how to design synaptic devices with the vdW structures in terms of critical 2D materials and their limitations, providing insight into the emerging synaptic device systems and artificial neural networks with 2D materials.
{"title":"Recent Progress in Synaptic Devices Based on 2D Materials","authors":"Linfeng Sun, Wei Wang, Heejun Yang","doi":"10.1002/aisy.201900167","DOIUrl":"https://doi.org/10.1002/aisy.201900167","url":null,"abstract":"Diverse synaptic plasticity with a wide range of timescales in biological synapses plays an important role in memory, learning, and various signal processing with exceptionally low power consumption. Emulating biological synaptic functions by electric devices for neuromorphic computation has been considered as a way to overcome the traditional von Neumann architecture in which separated memory and information processing units require high power consumption for their functions. Synaptic devices are expected to conduct complex signal processing such as image classification, decision‐making, and pattern recognition in artificial neural networks. Among various materials and device architectures for synaptic devices, 2D materials and their van der Waals (vdW) heterostructures have been attracting tremendous attention from researchers based on their capacity to mimic unique synaptic plasticity for neuromorphic computing. Herein, the basic operations of biological synapses and physical properties of 2D materials are discussed, and then 2D materials and their vdW heterostructures for advanced synaptic operations with novel working mechanisms are reviewed. In particular, there is a focus on how to design synaptic devices with the vdW structures in terms of critical 2D materials and their limitations, providing insight into the emerging synaptic device systems and artificial neural networks with 2D materials.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86035134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Highly efficient and versatile natural motors play a pivotal role in biological processes. Inspired by these biological motors, researchers developed their synthetic counterparts that can convert various energies into locomotion. With the potential to revolutionize the biomedical treatment process, these micro/nanomotors have been attracting a booming research enthusiasm since the birth of the first micro/nanomotor 15 years ago (since 2004). First, typical motion mechanisms are elucidated and a detailed comparison is provided regarding their efficiency in a biological context. Next, cutting‐edge proof‐of‐concept biomedical applications of the motors are overviewed, including on‐demand drug dispensing, cell transporting, and precise microsurgery. Current achievements and remaining bottlenecks are discussed, to spur more collaboration among chemistry, nanoengineering, and the biomedical fields. With increasing attention and continuing innovation of the field, clinical translation of micro/nanomotors is possible in the next 15 years.
{"title":"Emerging Micro/Nanomotor‐Based Platforms for Biomedical Therapy","authors":"Zhen Wang, Yingfeng Tu, Yongming Chen, F. Peng","doi":"10.1002/aisy.201900081","DOIUrl":"https://doi.org/10.1002/aisy.201900081","url":null,"abstract":"Highly efficient and versatile natural motors play a pivotal role in biological processes. Inspired by these biological motors, researchers developed their synthetic counterparts that can convert various energies into locomotion. With the potential to revolutionize the biomedical treatment process, these micro/nanomotors have been attracting a booming research enthusiasm since the birth of the first micro/nanomotor 15 years ago (since 2004). First, typical motion mechanisms are elucidated and a detailed comparison is provided regarding their efficiency in a biological context. Next, cutting‐edge proof‐of‐concept biomedical applications of the motors are overviewed, including on‐demand drug dispensing, cell transporting, and precise microsurgery. Current achievements and remaining bottlenecks are discussed, to spur more collaboration among chemistry, nanoengineering, and the biomedical fields. With increasing attention and continuing innovation of the field, clinical translation of micro/nanomotors is possible in the next 15 years.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86231364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since the discovery of electricity and the creation of the first transistors two centuries ago, the field of electronics has evolved rapidly to become omnipresent. Today, electronic devices are challenged by new demands in function and performance: they are expected to be lightweight, highly efficient, flexible, smart, implantable, and so on. To meet these demands, the materials and components in devices need to be carefully selected and assembled together. In this regard, the controlled assembly of 3D graphene structures holds tremendous potential to achieve such levels of multifunctionality and outstanding properties. Advanced processing approaches, such as 3D printing, allow the fabrication of a variety of 3D graphene–based materials that present outstanding properties and a high degree of multifunctionality. Herein, the recent progress in the fabrication of graphene‐based devices for advanced electronics using controlled assembly is reported. The benefits of controlling the microstructure of graphene nanomaterials for enhanced properties and functionalities are highlighted, and the various fabrication methods and their implications on the organization of materials are reviewed, as well as selected electrical devices. The approaches described here are opening up new avenues for the fabrication of health or structural monitoring devices, autonomous machines, and interconnected objects.
{"title":"3D Assembly of Graphene Nanomaterials for Advanced Electronics","authors":"H. Ferrand, Sakineh Chabi, S. Agarwala","doi":"10.1002/aisy.201900151","DOIUrl":"https://doi.org/10.1002/aisy.201900151","url":null,"abstract":"Since the discovery of electricity and the creation of the first transistors two centuries ago, the field of electronics has evolved rapidly to become omnipresent. Today, electronic devices are challenged by new demands in function and performance: they are expected to be lightweight, highly efficient, flexible, smart, implantable, and so on. To meet these demands, the materials and components in devices need to be carefully selected and assembled together. In this regard, the controlled assembly of 3D graphene structures holds tremendous potential to achieve such levels of multifunctionality and outstanding properties. Advanced processing approaches, such as 3D printing, allow the fabrication of a variety of 3D graphene–based materials that present outstanding properties and a high degree of multifunctionality. Herein, the recent progress in the fabrication of graphene‐based devices for advanced electronics using controlled assembly is reported. The benefits of controlling the microstructure of graphene nanomaterials for enhanced properties and functionalities are highlighted, and the various fabrication methods and their implications on the organization of materials are reviewed, as well as selected electrical devices. The approaches described here are opening up new avenues for the fabrication of health or structural monitoring devices, autonomous machines, and interconnected objects.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"63 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78185387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Biomolecular machines are widely present in nature, especially in complex living organisms, and are involved in important biological processes. Inspired by nature and increasingly mature synthetic technologies, a series of artificial molecular machines (AMMs) that exhibit similar processes and functions as biomolecular counterparts have been developed, and to date, some dramatic achievements have been obtained. Herein, the use of AMMs in smart systems and materials with controllable regulations and interesting functions are summarized, presenting the specific micro‐ to macroscale applications in solid surface modification, transmembrane transport, smart catalysts, liquid crystals, artificial molecular muscles, and stimuli‐responsive polymers. The challenges of developing novel complex AMMs with intelligent functions are discussed, and some potential solutions are proposed.
{"title":"Driving Smart Molecular Systems by Artificial Molecular Machines","authors":"Zhao-Tao Shi, Qi Zhang, H. Tian, Da‐Hui Qu","doi":"10.1002/aisy.201900169","DOIUrl":"https://doi.org/10.1002/aisy.201900169","url":null,"abstract":"Biomolecular machines are widely present in nature, especially in complex living organisms, and are involved in important biological processes. Inspired by nature and increasingly mature synthetic technologies, a series of artificial molecular machines (AMMs) that exhibit similar processes and functions as biomolecular counterparts have been developed, and to date, some dramatic achievements have been obtained. Herein, the use of AMMs in smart systems and materials with controllable regulations and interesting functions are summarized, presenting the specific micro‐ to macroscale applications in solid surface modification, transmembrane transport, smart catalysts, liquid crystals, artificial molecular muscles, and stimuli‐responsive polymers. The challenges of developing novel complex AMMs with intelligent functions are discussed, and some potential solutions are proposed.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86895409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial intelligence (AI) in healthcare holds great potential to expand access to high‐quality medical care, while reducing systemic costs. Despite hitting headlines regularly and many publications of proofs‐of‐concept, certified products are failing to break through to the clinic. AI in healthcare is a multiparty process with deep knowledge required in multiple individual domains. A lack of understanding of the specific challenges in the domain is the major contributor to the failure to deliver on the big promises. Herein, a “decision perspective” framework for the development of AI‐driven biomedical products from conception to market launch is presented. The framework highlights the risks, objectives, and key results which are typically required to navigate a three‐phase process to market‐launch of a validated medical AI product. Clinical validation, regulatory affairs, data strategy, and algorithmic development are addressed. The development process proposed for AI in healthcare software strongly diverges from modern consumer software development processes. Key time points to guide founders, investors, and key stakeholders throughout the process are highlighted. This framework should be seen as a template for innovation frameworks, which can be used to coordinate team communications and responsibilities toward a viable product development roadmap, thus unlocking the potential of AI in medicine.
{"title":"From Bit to Bedside: A Practical Framework for Artificial Intelligence Product Development in Healthcare","authors":"David C. Higgins, V. Madai","doi":"10.1002/aisy.202000052","DOIUrl":"https://doi.org/10.1002/aisy.202000052","url":null,"abstract":"Artificial intelligence (AI) in healthcare holds great potential to expand access to high‐quality medical care, while reducing systemic costs. Despite hitting headlines regularly and many publications of proofs‐of‐concept, certified products are failing to break through to the clinic. AI in healthcare is a multiparty process with deep knowledge required in multiple individual domains. A lack of understanding of the specific challenges in the domain is the major contributor to the failure to deliver on the big promises. Herein, a “decision perspective” framework for the development of AI‐driven biomedical products from conception to market launch is presented. The framework highlights the risks, objectives, and key results which are typically required to navigate a three‐phase process to market‐launch of a validated medical AI product. Clinical validation, regulatory affairs, data strategy, and algorithmic development are addressed. The development process proposed for AI in healthcare software strongly diverges from modern consumer software development processes. Key time points to guide founders, investors, and key stakeholders throughout the process are highlighted. This framework should be seen as a template for innovation frameworks, which can be used to coordinate team communications and responsibilities toward a viable product development roadmap, thus unlocking the potential of AI in medicine.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"14 24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89572512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ken Matsubara, Daiki Tachibana, R. Matsuda, H. Onoe, O. Fuchiwaki, H. Ota
Hydrogel actuators, comprising gels that convert external stimuli into mechanical motion for actuation, are attracting attention for their promising applications, such as in robotics. The driving force is the absorption or release of water or another solvent, which results in swelling and shrinking motions, leading in turn to more complex functionalities. However, practical hydrogel actuators that can be controlled locally, such as ones that allow local actuation around the joints in rigid‐bodied robots, do not exist. Herein, the driving target of a thermo‐responsive hydrogel, poly(N‐isopropyl acrylamide), is integrated with the stimulation module using a liquid metal. The stimulation module provides heat as an external stimulus to the hydrogel actuator. The motion of the actuator is triggered by the heat supplied by an ultrasoft hydrogel coil, with liquid metal surrounding the driving target. The heat generated by current flowing through the liquid metal changes the temperature only around the desired part of the actuator, which enables the electrical control of an individual part of the hydrogel actuator. The concept of integrating the driving target and stimulator is expected to facilitate functional movement of actuators and expand the range of potential applications of hydrogels.
{"title":"Hydrogel Actuator with a Built‐In Stimulator Using Liquid Metal for Local Control","authors":"Ken Matsubara, Daiki Tachibana, R. Matsuda, H. Onoe, O. Fuchiwaki, H. Ota","doi":"10.1002/aisy.202000008","DOIUrl":"https://doi.org/10.1002/aisy.202000008","url":null,"abstract":"Hydrogel actuators, comprising gels that convert external stimuli into mechanical motion for actuation, are attracting attention for their promising applications, such as in robotics. The driving force is the absorption or release of water or another solvent, which results in swelling and shrinking motions, leading in turn to more complex functionalities. However, practical hydrogel actuators that can be controlled locally, such as ones that allow local actuation around the joints in rigid‐bodied robots, do not exist. Herein, the driving target of a thermo‐responsive hydrogel, poly(N‐isopropyl acrylamide), is integrated with the stimulation module using a liquid metal. The stimulation module provides heat as an external stimulus to the hydrogel actuator. The motion of the actuator is triggered by the heat supplied by an ultrasoft hydrogel coil, with liquid metal surrounding the driving target. The heat generated by current flowing through the liquid metal changes the temperature only around the desired part of the actuator, which enables the electrical control of an individual part of the hydrogel actuator. The concept of integrating the driving target and stimulator is expected to facilitate functional movement of actuators and expand the range of potential applications of hydrogels.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"155 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88004962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cara A. Koepele, M. Guix, Chenghao Bi, G. Adam, D. Cappelleri
The implementation of two‐photon polymerization (TPP) in the microrobotics community has permitted the fabrication of complex 3D structures at the microscale, creating novel platforms with potential biomedical applications for minimizing procedure invasiveness and diagnosis accuracy. Although advanced functionalities for manipulation and drug delivery tasks have been explored, one remaining challenge is achieving improved visualization, identification, and accurate closed‐loop control of microscale robots. To enable this, distinguishable identifying and trackable features must be included on the microrobot. Toward this end, the construction of micro‐ and nanoscale patterns using TPP is demonstrated for the first time on microrobot surfaces with the intent of mimicking color‐expressing nanostructures present on beetles or butterflies. The patterns provide identification and tracking targets due to their vivid color expression under visible light. Helical and rectangular microrobots are designed with the topical patterns and further functionalized with magnetic materials to be externally actuated by magnetic fields. Vision‐based tracking of a 20 μm × 30 μm colored feature on a 100 μm‐long helical microrobot using a fixed angular position light source during microrobotic motion is shown. This versatile structural color patterning approach shows great potential for the visual differentiation of various microrobots and tracking for improved closed‐loop control.
{"title":"3D‐Printed Microrobots with Integrated Structural Color for Identification and Tracking","authors":"Cara A. Koepele, M. Guix, Chenghao Bi, G. Adam, D. Cappelleri","doi":"10.1002/aisy.201900147","DOIUrl":"https://doi.org/10.1002/aisy.201900147","url":null,"abstract":"The implementation of two‐photon polymerization (TPP) in the microrobotics community has permitted the fabrication of complex 3D structures at the microscale, creating novel platforms with potential biomedical applications for minimizing procedure invasiveness and diagnosis accuracy. Although advanced functionalities for manipulation and drug delivery tasks have been explored, one remaining challenge is achieving improved visualization, identification, and accurate closed‐loop control of microscale robots. To enable this, distinguishable identifying and trackable features must be included on the microrobot. Toward this end, the construction of micro‐ and nanoscale patterns using TPP is demonstrated for the first time on microrobot surfaces with the intent of mimicking color‐expressing nanostructures present on beetles or butterflies. The patterns provide identification and tracking targets due to their vivid color expression under visible light. Helical and rectangular microrobots are designed with the topical patterns and further functionalized with magnetic materials to be externally actuated by magnetic fields. Vision‐based tracking of a 20 μm × 30 μm colored feature on a 100 μm‐long helical microrobot using a fixed angular position light source during microrobotic motion is shown. This versatile structural color patterning approach shows great potential for the visual differentiation of various microrobots and tracking for improved closed‐loop control.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74982441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Herein, the progress of machine learning methods in the field of soft robotics, specifically in the applications of sensing and control, is outlined. Data‐driven methods such as machine learning are especially suited to systems with governing functions that are unknown, impractical or impossible to represent analytically, or computationally intractable to integrate into real‐world solutions. Function approximation with careful formulation of the machine learning architecture enables the encoding of dynamic behavior and nonlinearities, with the added potential to address hysteresis and nonstationary behavior. Supervised learning and reinforcement learning in simulation and on a wide variety of physical robotic systems have shown promising results for the use of empirical data‐driven methods as a solution to contemporary soft robotics problems.
{"title":"Machine Learning for Soft Robotic Sensing and Control","authors":"Keene Chin, T. Hellebrekers, C. Majidi","doi":"10.1002/aisy.201900171","DOIUrl":"https://doi.org/10.1002/aisy.201900171","url":null,"abstract":"Herein, the progress of machine learning methods in the field of soft robotics, specifically in the applications of sensing and control, is outlined. Data‐driven methods such as machine learning are especially suited to systems with governing functions that are unknown, impractical or impossible to represent analytically, or computationally intractable to integrate into real‐world solutions. Function approximation with careful formulation of the machine learning architecture enables the encoding of dynamic behavior and nonlinearities, with the added potential to address hysteresis and nonstationary behavior. Supervised learning and reinforcement learning in simulation and on a wide variety of physical robotic systems have shown promising results for the use of empirical data‐driven methods as a solution to contemporary soft robotics problems.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89304246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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