Mobile robots have revolutionized the public and private sectors for transportation, exploration, and search and rescue. Efficient energy consumption and robust environmental interaction needed for complex tasks can be achieved in aerial–terrestrial robots by combining advantages of each locomotion mode. This review surveys over two decades of development in multimodal robots that move on the ground and in air. Multimodality can be achieved by leveraging three main design approaches: adding morphological features, adapting forms for locomotion transitions, and integrating multiple vehicle platforms. Each classification is thoroughly examined and synthesized, encompassing both qualitative and quantitative aspects. The authors delved into the intricacies of these approaches and explored the challenges and opportunities that lie ahead in pursuit of the next generation of mobile robots. This review aims to advance future deployment of multimodal robots in the real world for challenging operations in dangerous, unstructured, contact-prone, cluttered and subterranean environments.
{"title":"Multimodal Locomotion: Next Generation Aerial–Terrestrial Mobile Robotics","authors":"Jane Pauline Ramirez, Salua Hamaza","doi":"10.1002/aisy.202300327","DOIUrl":"https://doi.org/10.1002/aisy.202300327","url":null,"abstract":"Mobile robots have revolutionized the public and private sectors for transportation, exploration, and search and rescue. Efficient energy consumption and robust environmental interaction needed for complex tasks can be achieved in aerial–terrestrial robots by combining advantages of each locomotion mode. This review surveys over two decades of development in multimodal robots that move on the ground and in air. Multimodality can be achieved by leveraging three main design approaches: adding morphological features, adapting forms for locomotion transitions, and integrating multiple vehicle platforms. Each classification is thoroughly examined and synthesized, encompassing both qualitative and quantitative aspects. The authors delved into the intricacies of these approaches and explored the challenges and opportunities that lie ahead in pursuit of the next generation of mobile robots. This review aims to advance future deployment of multimodal robots in the real world for challenging operations in dangerous, unstructured, contact-prone, cluttered and subterranean environments.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138563839","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}
Hybrid aerial–aquatic robots can operate in both air and water and cross between these two. They can be applied to amphibious observation, maritime search and rescue, and cross‐domain environmental monitoring. Herein, an aerial–aquatic hitchhiking robot is proposed that can fly, swim, and rapidly cross the air–water boundaries (0.16 s) and autonomously attach to surfaces in both air and water. Inspired by the mechanoreceptors of the remora ( Echeneis naucrates ) disc, the robot's hitchhiking device is equipped with two flexible bioinspired tactile sensors (FBTS) based on a triboelectric nanogenerator for tactile sensing of attachment status. Based on tactile sensing, the robot can perform reattachment after leakage or adhesion failure, enabling it to achieve long‐term adhesion on complex surfaces. The rotor‐based aerial–aquatic robot, which has two thrust vectoring units for underwater locomotion, can maneuver to pitch, yaw, and roll 360° and control precision motion position. The field tests show that the robot can continuously cross the air–water boundary, attach to the rough stone surface, and record video in both air and underwater. This study may shed light on future autonomous robots capable of intelligent navigation, adhesion, and operation in complex aerial–aquatic environments.
{"title":"An Aerial–Aquatic Hitchhiking Robot with Remora‐Inspired Tactile Sensors and Thrust Vectoring Units","authors":"Lei Li, Wenbo Liu, Bocheng Tian, Peiyu Hu, Wenzhuo Gao, Yuchen Liu, Fuqiang Yang, Youning Duo, Hongru Cai, Yiyuan Zhang, Zhouhao Zhang, Zimo Li, Li Wen","doi":"10.1002/aisy.202300381","DOIUrl":"https://doi.org/10.1002/aisy.202300381","url":null,"abstract":"Hybrid aerial–aquatic robots can operate in both air and water and cross between these two. They can be applied to amphibious observation, maritime search and rescue, and cross‐domain environmental monitoring. Herein, an aerial–aquatic hitchhiking robot is proposed that can fly, swim, and rapidly cross the air–water boundaries (0.16 s) and autonomously attach to surfaces in both air and water. Inspired by the mechanoreceptors of the remora ( Echeneis naucrates ) disc, the robot's hitchhiking device is equipped with two flexible bioinspired tactile sensors (FBTS) based on a triboelectric nanogenerator for tactile sensing of attachment status. Based on tactile sensing, the robot can perform reattachment after leakage or adhesion failure, enabling it to achieve long‐term adhesion on complex surfaces. The rotor‐based aerial–aquatic robot, which has two thrust vectoring units for underwater locomotion, can maneuver to pitch, yaw, and roll 360° and control precision motion position. The field tests show that the robot can continuously cross the air–water boundary, attach to the rough stone surface, and record video in both air and underwater. This study may shed light on future autonomous robots capable of intelligent navigation, adhesion, and operation in complex aerial–aquatic environments.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136314090","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}
The advent of the intelligent society leads to the exponential growth of information, imposing urgent requirements in a time‐ and energy‐efficient way to process information where data are generated. This issue can be addressed by the neuromorphic paradigm of computing inspired by biological sensory systems that build up the association between external stimuli and the response of an organism in real‐time; in the paradigm, a neuromorphic system is integrated with sensors to form an artificial sensory system. Herein, a neuromorphic sensory system with integrated capabilities of gas sensing, data storage, and processing is demonstrated. Leaky integrate‐and‐fire (LIF) neurons, the basic computing units in the system, are realized with volatile memristive device Pt/Ag/TaOx/Pt; sensory neurons, i.e., the LIF neurons connected with an array of gas sensors, detect gases and convert the chemical information of gases into neural spikes; synapses based on nonvolatile memristive device Pt/Ta/TaOx/Pt transmit the signals from sensory neurons to relay neurons according to synaptic weights, which are trained by the supervised spike‐rate dependent plasticity; relay neurons then process the signals from the synapses and classify gases. The approach of this work can also be applied to emulate other biological perceptions through the integration with different sensors.
{"title":"A Bio‐Inspired Neuromorphic Sensory System","authors":"Tong Wang, Xiao-Xue Wang, Juan Wen, Zhenya Shao, He-Ming Huang, Xin Guo","doi":"10.1002/aisy.202200047","DOIUrl":"https://doi.org/10.1002/aisy.202200047","url":null,"abstract":"The advent of the intelligent society leads to the exponential growth of information, imposing urgent requirements in a time‐ and energy‐efficient way to process information where data are generated. This issue can be addressed by the neuromorphic paradigm of computing inspired by biological sensory systems that build up the association between external stimuli and the response of an organism in real‐time; in the paradigm, a neuromorphic system is integrated with sensors to form an artificial sensory system. Herein, a neuromorphic sensory system with integrated capabilities of gas sensing, data storage, and processing is demonstrated. Leaky integrate‐and‐fire (LIF) neurons, the basic computing units in the system, are realized with volatile memristive device Pt/Ag/TaOx/Pt; sensory neurons, i.e., the LIF neurons connected with an array of gas sensors, detect gases and convert the chemical information of gases into neural spikes; synapses based on nonvolatile memristive device Pt/Ta/TaOx/Pt transmit the signals from sensory neurons to relay neurons according to synaptic weights, which are trained by the supervised spike‐rate dependent plasticity; relay neurons then process the signals from the synapses and classify gases. The approach of this work can also be applied to emulate other biological perceptions through the integration with different sensors.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"120 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87779988","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}
Byung Do Lee, Jin-Woong Lee, W. Park, Joonseo Park, Min-Young Cho, S. Singh, M. Pyo, K. Sohn
Herein, data‐driven symmetry identification, property prediction, and low‐dimensional embedding from powder X‐Ray diffraction (XRD) patterns of inorganic crystal structure database (ICSD) and materials project (MP) entries are reported. For this purpose, a fully convolutional neural network (FCN), transformer encoder (T‐encoder), and variational autoencoder (VAE) are used. The results are compared to those obtained from a well‐established crystal graph convolutional neural network (CGCNN). A task‐specified small dataset that focuses on a narrow material system, knowledge (rule)‐based descriptor extraction, and significant data dimension reduction are not the main focus of this study. Conventional powder XRD patterns, which are most widely used in materials research, can be used as a significantly informative material descriptor for deep learning. Both the FCN and T‐encoder outperform the CGCNN for symmetry classification. For property prediction, the performance of the FCN concatenated with multilayer perceptron reaches the performance level of CGCNN. Machine‐learning‐driven material property prediction from the powder XRD pattern deserves appreciation because no such attempts have been made despite common XRD‐driven symmetry (and lattice size) prediction and phase identification. The ICSD and MP data are embedded in the 2D (or 3D) latent space through the VAE, and well‐separated clustering according to the symmetry and property is observed.
{"title":"Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction","authors":"Byung Do Lee, Jin-Woong Lee, W. Park, Joonseo Park, Min-Young Cho, S. Singh, M. Pyo, K. Sohn","doi":"10.1002/aisy.202200042","DOIUrl":"https://doi.org/10.1002/aisy.202200042","url":null,"abstract":"Herein, data‐driven symmetry identification, property prediction, and low‐dimensional embedding from powder X‐Ray diffraction (XRD) patterns of inorganic crystal structure database (ICSD) and materials project (MP) entries are reported. For this purpose, a fully convolutional neural network (FCN), transformer encoder (T‐encoder), and variational autoencoder (VAE) are used. The results are compared to those obtained from a well‐established crystal graph convolutional neural network (CGCNN). A task‐specified small dataset that focuses on a narrow material system, knowledge (rule)‐based descriptor extraction, and significant data dimension reduction are not the main focus of this study. Conventional powder XRD patterns, which are most widely used in materials research, can be used as a significantly informative material descriptor for deep learning. Both the FCN and T‐encoder outperform the CGCNN for symmetry classification. For property prediction, the performance of the FCN concatenated with multilayer perceptron reaches the performance level of CGCNN. Machine‐learning‐driven material property prediction from the powder XRD pattern deserves appreciation because no such attempts have been made despite common XRD‐driven symmetry (and lattice size) prediction and phase identification. The ICSD and MP data are embedded in the 2D (or 3D) latent space through the VAE, and well‐separated clustering according to the symmetry and property is observed.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88297752","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}
Jinwoo Lee, Yeosang Yoon, H. Park, Joonhwa Choi, Yeong-Tae Jung, Seung Hwan Ko, W. Yeo
Animal locomotion offers valuable references as it is a critical component of survival as animals adapting to a specific environment. Especially, underwater locomotion poses a challenge because water exerts a high antagonistic drag force against the direction of progress. However, marine vertebrates usually use much lower aerobic energy for locomotion than aerial or terrestrial vertebrates due to their unique intermittent gliding locomotion. None of the prior works demonstrate the locomotive strategies of marine vertebrates. Herein, an untethered soft robotic fish capable of reconstructing the marine vertebrates’ effective locomotion and traveling underwater by controlling localized buoyancy with thermoelectric pneumatic actuators is introduced. The actuators enable both heating and cooling to control a localized buoyancy while providing a substantial driving force to the system. Besides mimicking the locomotion, the bidirectional communication system enables the untethered delivery of commands to the underwater subject and real‐time acquisition of the robotic fish's physical information. Underwater imaging validates the fish's practical use as a drone, allowing for inspecting the aquatic environment that is not easily accessible to humans. Future work studies the operation of the robotic fish as a collective swarm to examine a broader range of the underwater area and conduct various strategic missions.
{"title":"Bioinspired Soft Robotic Fish for Wireless Underwater Control of Gliding Locomotion","authors":"Jinwoo Lee, Yeosang Yoon, H. Park, Joonhwa Choi, Yeong-Tae Jung, Seung Hwan Ko, W. Yeo","doi":"10.1002/aisy.202100271","DOIUrl":"https://doi.org/10.1002/aisy.202100271","url":null,"abstract":"Animal locomotion offers valuable references as it is a critical component of survival as animals adapting to a specific environment. Especially, underwater locomotion poses a challenge because water exerts a high antagonistic drag force against the direction of progress. However, marine vertebrates usually use much lower aerobic energy for locomotion than aerial or terrestrial vertebrates due to their unique intermittent gliding locomotion. None of the prior works demonstrate the locomotive strategies of marine vertebrates. Herein, an untethered soft robotic fish capable of reconstructing the marine vertebrates’ effective locomotion and traveling underwater by controlling localized buoyancy with thermoelectric pneumatic actuators is introduced. The actuators enable both heating and cooling to control a localized buoyancy while providing a substantial driving force to the system. Besides mimicking the locomotion, the bidirectional communication system enables the untethered delivery of commands to the underwater subject and real‐time acquisition of the robotic fish's physical information. Underwater imaging validates the fish's practical use as a drone, allowing for inspecting the aquatic environment that is not easily accessible to humans. Future work studies the operation of the robotic fish as a collective swarm to examine a broader range of the underwater area and conduct various strategic missions.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77979308","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}
Modular soft robots have strong adaptability and versatility in various application contexts. However, the introduction of connection mechanisms will always either reduce the structural compliance or need extra actuation appendages, resulting in the complexity of the structure and system of the robot. To address these issues, herein, a compliant and passive connection strategy is demonstrated, which is accomplished utilizing the reclosable fasteners (RFs), and other varieties including hook‐and‐loop fasteners, as the connection mechanisms to the soft modules for the rapid assembly of various soft machines. The module is a pneumatic soft actuator with both ends designed with a multifaceted structure to attach the RFs in different orientations, resulting in various assembling patterns, including linear connection, orthogonal connection, and oblique connection. Moreover, an alignment mechanism is also designed to improve the alignment precision between two assembled modules. The versatility of the RF enables soft modules capable of assembling not only between identical modules but also with diverse additional accessories for various application scenarios. Different functional assemblies are demonstrated including soft grippers, soft walking robots, and shape‐morphing electrical devices. This approach to the connection mechanisms provides routes to new modular soft robots and devices.
{"title":"Modular Assembly of Soft Machines via Multidirectional Reclosable Fasteners","authors":"Huiyan Yang, Shiyan Jin, Wei Dawid Wang","doi":"10.1002/aisy.202200048","DOIUrl":"https://doi.org/10.1002/aisy.202200048","url":null,"abstract":"Modular soft robots have strong adaptability and versatility in various application contexts. However, the introduction of connection mechanisms will always either reduce the structural compliance or need extra actuation appendages, resulting in the complexity of the structure and system of the robot. To address these issues, herein, a compliant and passive connection strategy is demonstrated, which is accomplished utilizing the reclosable fasteners (RFs), and other varieties including hook‐and‐loop fasteners, as the connection mechanisms to the soft modules for the rapid assembly of various soft machines. The module is a pneumatic soft actuator with both ends designed with a multifaceted structure to attach the RFs in different orientations, resulting in various assembling patterns, including linear connection, orthogonal connection, and oblique connection. Moreover, an alignment mechanism is also designed to improve the alignment precision between two assembled modules. The versatility of the RF enables soft modules capable of assembling not only between identical modules but also with diverse additional accessories for various application scenarios. Different functional assemblies are demonstrated including soft grippers, soft walking robots, and shape‐morphing electrical devices. This approach to the connection mechanisms provides routes to new modular soft robots and devices.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88523356","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}
Davide Zappetti, Yi Sun, Matthieu Gevers, S. Mintchev, D. Floreano
Collision resilience is an important feature of robots deployed in unstructured and partially unpredictable environments. Herein, a novel dual stiffness (DS) tensegrity platform to integrate collision resilience into a robot body is proposed. The proposed DS tensegrity platform is rigid during normal robot operation, but softens upon collision to withstand the impact. The DS behavior is achieved by means of a novel DS strut that is rigid, but can buckle without breaking under high loads, thus preventing damage to the robot. Compression tests and finite element method simulations show that both the DS struts and DS tensegrities undergo substantial stiffness change with maximum load‐bearing ratios up to 10.5 and 5.74, respectively, before and after buckling. These DS tensegrity structures are integrated into two types of robots, a drone and a rover, that are shown to withstand falls from 2 and 5 m, respectively. The mechanical tunability of the proposed DS tensegrity system makes it suitable for impact attenuation in a wide range of situations and robot types.
{"title":"Dual Stiffness Tensegrity Platform for Resilient Robotics","authors":"Davide Zappetti, Yi Sun, Matthieu Gevers, S. Mintchev, D. Floreano","doi":"10.1002/aisy.202200025","DOIUrl":"https://doi.org/10.1002/aisy.202200025","url":null,"abstract":"Collision resilience is an important feature of robots deployed in unstructured and partially unpredictable environments. Herein, a novel dual stiffness (DS) tensegrity platform to integrate collision resilience into a robot body is proposed. The proposed DS tensegrity platform is rigid during normal robot operation, but softens upon collision to withstand the impact. The DS behavior is achieved by means of a novel DS strut that is rigid, but can buckle without breaking under high loads, thus preventing damage to the robot. Compression tests and finite element method simulations show that both the DS struts and DS tensegrities undergo substantial stiffness change with maximum load‐bearing ratios up to 10.5 and 5.74, respectively, before and after buckling. These DS tensegrity structures are integrated into two types of robots, a drone and a rover, that are shown to withstand falls from 2 and 5 m, respectively. The mechanical tunability of the proposed DS tensegrity system makes it suitable for impact attenuation in a wide range of situations and robot types.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86938441","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}
Cong Fang, Huayao Li, Long Li, Hu-Yin Su, Jiang Tang, Xiang Bai, Huan Liu
An electronic nose (e‐nose) mimics the mammalian olfactory system in identifying odors and expands human olfaction boundaries by tracing toxins and explosives. However, existing feature‐based odor recognition algorithms rely on domain‐specific expertise, which may limit the performance due to information loss during the feature extraction process. Inspired by human olfaction, a smart electronic nose enabled by an all‐feature olfactory algorithm (AFOA) is proposed, whereby all features in a gas sensing cycle of semiconductor gas sensors, including the response, equilibrium, and recovery processes are utilized. Specifically, our method combines 1D convolutional and recurrent neural networks with channel and temporal attention modules to fully utilize complementary global and dynamic information. It is further demonstrated that a novel data augmentation method can transform the raw data into a suitable representation for feature extraction. Results show that the e‐nose simply comprising of six semiconductor gas sensors achieves superior performances to state‐of‐the‐art methods on the Chinese liquor data. Ablation studies reveal the contribution of each sensor in odor recognition. Therefore, a deep‐learning‐enabled codesign of sensor arrays and recognition algorithms can reduce the heavy demand for a huge amount of highly specialized gas sensors and provide interpretable insights into odor recognition dynamics in an iterative way.
{"title":"Smart Electronic Nose Enabled by an All‐Feature Olfactory Algorithm","authors":"Cong Fang, Huayao Li, Long Li, Hu-Yin Su, Jiang Tang, Xiang Bai, Huan Liu","doi":"10.1002/aisy.202200074","DOIUrl":"https://doi.org/10.1002/aisy.202200074","url":null,"abstract":"An electronic nose (e‐nose) mimics the mammalian olfactory system in identifying odors and expands human olfaction boundaries by tracing toxins and explosives. However, existing feature‐based odor recognition algorithms rely on domain‐specific expertise, which may limit the performance due to information loss during the feature extraction process. Inspired by human olfaction, a smart electronic nose enabled by an all‐feature olfactory algorithm (AFOA) is proposed, whereby all features in a gas sensing cycle of semiconductor gas sensors, including the response, equilibrium, and recovery processes are utilized. Specifically, our method combines 1D convolutional and recurrent neural networks with channel and temporal attention modules to fully utilize complementary global and dynamic information. It is further demonstrated that a novel data augmentation method can transform the raw data into a suitable representation for feature extraction. Results show that the e‐nose simply comprising of six semiconductor gas sensors achieves superior performances to state‐of‐the‐art methods on the Chinese liquor data. Ablation studies reveal the contribution of each sensor in odor recognition. Therefore, a deep‐learning‐enabled codesign of sensor arrays and recognition algorithms can reduce the heavy demand for a huge amount of highly specialized gas sensors and provide interpretable insights into odor recognition dynamics in an iterative way.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80125502","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}
Xinqiang Pan, Jiejun Wang, Zhen Deng, Y. Shuai, W. Luo, Qin Xie, Yao Xiao, Song Tang, Shuwen Jiang, Chuangui Wu, Feng Zhu, Jianwei Zhang, W. Zhang
Sensory adaptation plays a critical role in humans interacting with the environment. Inspired by humans, realization of sensory adaptation on robots can make them adapt to the environment gradually. The gradual change of sensitivity that depends on recent experience of external stimuli is the most important process for the adaptation. To realize sensory adaptation, such change of sensitivity needs to be realized. It is proposed to fabricate the memristor based on single‐crystalline LiNbO3 thin film. The resistance of the memristor can be changed monotonically and gradually with the increase in the number of voltage pulses, which can be ascribed to the property of single‐crystalline thin films. Based on the characteristic, it is proposed to use the memristor as artificial synapse of the proposed bioinspired system, using conductance of the memristor to denote susceptibility value to realize the gradual change of sensitivity by recent external stimuli. A novel general excitation method of signals from multimodal sensors on memristor is proposed and utilized in the signal‐coupling module of the system, which makes the system realize sensory adaptation for different stimuli accepted by multimodal sensors. Using artificial sensory memory systems, sensory adaptation on robot is realized for the first time herein.
{"title":"A Memristor‐Based Bioinspired Multimodal Sensory Memory System for Sensory Adaptation of Robots","authors":"Xinqiang Pan, Jiejun Wang, Zhen Deng, Y. Shuai, W. Luo, Qin Xie, Yao Xiao, Song Tang, Shuwen Jiang, Chuangui Wu, Feng Zhu, Jianwei Zhang, W. Zhang","doi":"10.1002/aisy.202200031","DOIUrl":"https://doi.org/10.1002/aisy.202200031","url":null,"abstract":"Sensory adaptation plays a critical role in humans interacting with the environment. Inspired by humans, realization of sensory adaptation on robots can make them adapt to the environment gradually. The gradual change of sensitivity that depends on recent experience of external stimuli is the most important process for the adaptation. To realize sensory adaptation, such change of sensitivity needs to be realized. It is proposed to fabricate the memristor based on single‐crystalline LiNbO3 thin film. The resistance of the memristor can be changed monotonically and gradually with the increase in the number of voltage pulses, which can be ascribed to the property of single‐crystalline thin films. Based on the characteristic, it is proposed to use the memristor as artificial synapse of the proposed bioinspired system, using conductance of the memristor to denote susceptibility value to realize the gradual change of sensitivity by recent external stimuli. A novel general excitation method of signals from multimodal sensors on memristor is proposed and utilized in the signal‐coupling module of the system, which makes the system realize sensory adaptation for different stimuli accepted by multimodal sensors. Using artificial sensory memory systems, sensory adaptation on robot is realized for the first time herein.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"76 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86673593","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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