Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309012502> ?p ?o ?g. }
- W4309012502 abstract "Compliant and soft sensors that detect machinal deformations become prevalent in emerging soft robots for closed-loop feedback control. In contrast to conventional sensing applications, the stretchy body of the soft robot enables programmable actuating behaviors and automated manipulations across a wide strain range, which poses high requirements for the integrated sensors of customized sensor characteristics, high-throughput data processing, and timely decision-making. As various soft robotic sensors (strain, pressure, shear, etc.) meet similar challenges, in this perspective, we choose strain sensor as a representative example and summarize the latest advancement of strain sensor-integrated soft robotic design driven by machine learning techniques, including sensor materials optimization, sensor signal analyses, and in-sensor computing. These machine learning implementations greatly accelerate robot automation, reduce resource consumption, and expand the working scenarios of soft robots. We also discuss the prospects of fusing machine learning and soft sensing technology for creating next-generation intelligent soft robots." @default.
- W4309012502 created "2022-11-20" @default.
- W4309012502 creator A5001827630 @default.
- W4309012502 creator A5049798683 @default.
- W4309012502 date "2022-11-14" @default.
- W4309012502 modified "2023-09-25" @default.
- W4309012502 title "A review: Machine learning for strain sensor-integrated soft robots" @default.
- W4309012502 cites W1515749184 @default.
- W4309012502 cites W1999653735 @default.
- W4309012502 cites W1999959880 @default.
- W4309012502 cites W2025613329 @default.
- W4309012502 cites W2055548088 @default.
- W4309012502 cites W2280243739 @default.
- W4309012502 cites W2285847257 @default.
- W4309012502 cites W2560704029 @default.
- W4309012502 cites W2560730116 @default.
- W4309012502 cites W2619793087 @default.
- W4309012502 cites W2772398882 @default.
- W4309012502 cites W2775854755 @default.
- W4309012502 cites W2785110060 @default.
- W4309012502 cites W2793544513 @default.
- W4309012502 cites W2797402103 @default.
- W4309012502 cites W2799396491 @default.
- W4309012502 cites W2801279626 @default.
- W4309012502 cites W2801584653 @default.
- W4309012502 cites W2801704144 @default.
- W4309012502 cites W2803735436 @default.
- W4309012502 cites W2805926569 @default.
- W4309012502 cites W2883279767 @default.
- W4309012502 cites W2884430236 @default.
- W4309012502 cites W2888714402 @default.
- W4309012502 cites W2904858388 @default.
- W4309012502 cites W2907667791 @default.
- W4309012502 cites W2910222073 @default.
- W4309012502 cites W2913446864 @default.
- W4309012502 cites W2914657787 @default.
- W4309012502 cites W2916971279 @default.
- W4309012502 cites W2937307539 @default.
- W4309012502 cites W2940242941 @default.
- W4309012502 cites W2947434510 @default.
- W4309012502 cites W2947665220 @default.
- W4309012502 cites W2948471521 @default.
- W4309012502 cites W2963819344 @default.
- W4309012502 cites W2964017656 @default.
- W4309012502 cites W2968923792 @default.
- W4309012502 cites W2970521931 @default.
- W4309012502 cites W2971278234 @default.
- W4309012502 cites W2972418846 @default.
- W4309012502 cites W2972453911 @default.
- W4309012502 cites W2980398362 @default.
- W4309012502 cites W2988521097 @default.
- W4309012502 cites W2996448308 @default.
- W4309012502 cites W3002340625 @default.
- W4309012502 cites W3005667846 @default.
- W4309012502 cites W3005965793 @default.
- W4309012502 cites W3017503219 @default.
- W4309012502 cites W3018423233 @default.
- W4309012502 cites W3021947263 @default.
- W4309012502 cites W3033578652 @default.
- W4309012502 cites W3035576118 @default.
- W4309012502 cites W3037486115 @default.
- W4309012502 cites W3042314988 @default.
- W4309012502 cites W3046203962 @default.
- W4309012502 cites W3048902732 @default.
- W4309012502 cites W3091559712 @default.
- W4309012502 cites W3102407952 @default.
- W4309012502 cites W3104775050 @default.
- W4309012502 cites W3112665924 @default.
- W4309012502 cites W3117080414 @default.
- W4309012502 cites W3134259965 @default.
- W4309012502 cites W3134556996 @default.
- W4309012502 cites W3138929090 @default.
- W4309012502 cites W3138965819 @default.
- W4309012502 cites W3162741140 @default.
- W4309012502 cites W3163614806 @default.
- W4309012502 cites W3206507112 @default.
- W4309012502 cites W3211501692 @default.
- W4309012502 cites W4210412723 @default.
- W4309012502 cites W4224229286 @default.
- W4309012502 cites W4229007520 @default.
- W4309012502 cites W4282927077 @default.
- W4309012502 doi "https://doi.org/10.3389/femat.2022.1000781" @default.
- W4309012502 hasPublicationYear "2022" @default.
- W4309012502 type Work @default.
- W4309012502 citedByCount "2" @default.
- W4309012502 countsByYear W43090125022023 @default.
- W4309012502 crossrefType "journal-article" @default.
- W4309012502 hasAuthorship W4309012502A5001827630 @default.
- W4309012502 hasAuthorship W4309012502A5049798683 @default.
- W4309012502 hasBestOaLocation W43090125021 @default.
- W4309012502 hasConcept C111919701 @default.
- W4309012502 hasConcept C115575686 @default.
- W4309012502 hasConcept C127413603 @default.
- W4309012502 hasConcept C133731056 @default.
- W4309012502 hasConcept C154945302 @default.
- W4309012502 hasConcept C2776058767 @default.
- W4309012502 hasConcept C41008148 @default.
- W4309012502 hasConcept C90509273 @default.
- W4309012502 hasConcept C98045186 @default.
- W4309012502 hasConceptScore W4309012502C111919701 @default.