Insect-type microrobot with mounted bare chip IC of artificial neural networks

Iwata, Kei; Okane, Yuki; Ishihara, Yuki; Sugita, Kazuki; Ono, Satoko; Abe, Matanobu; Chiba, Satohiro; Takato, Minami; Saito, Ken; Uchikoba, Fumio

doi:10.1109/memsys.2016.7421853

Cited by 3 publications

(4 citation statements)

References 9 publications

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Section: Thermal Actuatorsmentioning

confidence: 99%

“…[18,94] The motion is initiated by applying electric current to selected shape memory alloy wires connected to the rotary actuator via a link mechanism, which converts the rotary motion of the actuator into forward or backward locomotion, as shown in Figure 6b. [18,94,98,99] The robot achieves a locomotion speed of 90.8 mm min À1 with a step size of 2.8 mm. [98] Shi et al [15] introduced an inchworm-like crawling soft microbot that utilizes two SMA wires to mimic the muscle fibers of inchworms.…”

Section: Thermal Actuatorsmentioning

confidence: 99%

See 1 more Smart Citation

Actuation of Mobile Microbots: A Review

Hussein,

Damdam,

Ren

et al. 2023

Advanced Intelligent Systems

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Maturation of robotics research and advances in the miniaturization of machines have contributed to the development of microbots and enabled new technological possibilities and applications. Microbots have a wide range of applications, including the navigation of confined spaces, environmental monitoring, micro‐assembly and manipulation of small objects, and in vivo micro‐surgeries and drug delivery. Actuators are among the most critical components that define the performance of robots. A comprehensive review of the actuation mechanisms that have been employed in mobile microbots is provided, including piezoelectric, magnetic, electrostatic, thermal, acoustic, biological, chemical, and optical actuation, with a focus on the most recent development and methodologies.

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Section: Thermal Actuatorsmentioning

confidence: 99%

Section: Thermal Actuatorsmentioning

confidence: 99%

Actuation of Mobile Microbots: A Review

Hussein,

Damdam,

Ren

et al. 2023

Advanced Intelligent Systems

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“…Full custom or application specific integrated circuit (ASIC) chips have also been very common for neuromorphic implementations [6], [9], [348], [350], [389]- [404], [438], [477], [677], [683], [749]- [764], [764]- [808], [1047], [1048], [1054]- [1060], [1100], [1101], [1118]- [1130], [1170], [1183]- [1185], [1204], [1205], [1212], [1236]- [1242], [1260], [1275], [1283], [1363], [1410], [1527]- [1561]. IBM's TrueNorth, one of the most popular present-day neuromorphic implementations, is a full custom ASIC design [1562]- [1569].…”

Section: A High-levelmentioning

confidence: 99%

“…Many of the requirements for robotics, including motor control, are applications that have been successfully demonstrated in neural networks. Some of the common applications of neuromorphic systems for robotics include learning a particular behavior [2548], [2549], locomotion control or control of particular joints to achieve a certain motion [67]- [69], [361], [663], [1082], [1083], [1261], [1279], [1527], [1784], [1885], [2550], [2551], social learning [2552], [2553], and target or wall following [498], [606], [1576], [1682], [2554]. Thus far, in terms of robotics, the most common use of neuromorphic implementations is for autonomous navigation tasks [16], [195], [393], [486], [532], [547], [735], [1077], [1329]- [1333], [1339], [1503], [1539], [1563], [1671], [1716], [2379], [2555]- [2560].…”

Section: Applicationsmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

176

180

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Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brainlike ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.

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