Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey

Ni, Jianjun; Wu, Liuying; Fan, Xiaopeng; Yang, Simon X.

doi:10.1155/2016/3810903

Cited by 80 publications

(47 citation statements)

References 62 publications

(61 reference statements)

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“…The field of artificial intelligence has seen dramatic success in recent years in the decision-making 7 and learning capacity of machines as well as their ability to interact with human operators using natural language systems. 37,44,51 Indeed, this year a team at the Children's National Medical Center …”

Section: Control Systemsmentioning

confidence: 99%

Augmented-reality integrated robotics in neurosurgery: are we there yet?

Madhavan¹,

Kolcun²,

Chieng³

et al. 2017

Neurosurgical Focus

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Surgical robots have captured the interest—if not the widespread acceptance—of spinal neurosurgeons. But successful innovation, scientific or commercial, requires the majority to adopt a new practice. “Faster, better, cheaper” products should in theory conquer the market, but often fail. The psychology of change is complex, and the “follow the leader” mentality, common in the field today, lends little trust to the process of disseminating new technology. Beyond product quality, timing has proven to be a key factor in the inception, design, and execution of new technologies. Although the first robotic surgery was performed in 1985, scant progress was seen until the era of minimally invasive surgery. This movement increased neurosurgeons’ dependence on navigation and fluoroscopy, intensifying the drive for enhanced precision. Outside the field of medicine, various technology companies have made great progress in popularizing co-robots (“cobots”), augmented reality, and processor chips. This has helped to ease practicing surgeons into familiarity with and acceptance of these technologies. The adoption among neurosurgeons in training is a “follow the leader” phenomenon, wherein new surgeons tend to adopt the technology used during residency. In neurosurgery today, robots are limited to computers functioning between the surgeon and patient. Their functions are confined to establishing a trajectory for navigation, with task execution solely in the surgeon’s hands. In this review, the authors discuss significant untapped technologies waiting to be used for more meaningful applications. They explore the history and current manifestations of various modern technologies, and project what innovations may lie ahead.

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Section: Control Systemsmentioning

confidence: 99%

Augmented-reality integrated robotics in neurosurgery: are we there yet?

Madhavan¹,

Kolcun²,

Chieng³

et al. 2017

Neurosurgical Focus

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show abstract

“…Klasifikasi SI sebagian besar dilakukan untuk memetakan SI berdasarkan mekanisme biomimetiknya [10]. Xiao dkk [11] melakukan pendekatan analisis kemiripan antara SI dan jaringan syaraf tiruan (JST).…”

Section: Abstract -unclassified

“…Penelitian tersebut perlu diuji untuk membuktikan apakah algoritma dalam rumpun SI tersebut merupakan bagian dari rumpun ilmu CAS atau tidak memiliki karakteristik sesuai dengan teori CAS walaupun dasar dari inspirasinya berasal dari interaksi kolektif mahluk hidup.Klasifikasi SI sebagian besar dilakukan untuk memetakan SI berdasarkan mekanisme biomimetiknya [10]. Xiao dkk [11] melakukan pendekatan analisis kemiripan antara SI dan jaringan syaraf tiruan (JST).…”

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Klasifikasi Algoritma Swarm Intelligence Dalam Perspektif Complex Adaptive System dengan Metode Uji Komparasi Statistik

Bintoro

Permana

2017

J. Teknol. dan Sist. Komput.

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- [9]. Penelitian tersebut perlu diuji untuk membuktikan apakah algoritma dalam rumpun SI tersebut merupakan bagian dari rumpun ilmu CAS atau tidak memiliki karakteristik sesuai dengan teori CAS walaupun dasar dari inspirasinya berasal dari interaksi kolektif mahluk hidup.Klasifikasi SI sebagian besar dilakukan untuk memetakan SI berdasarkan mekanisme biomimetiknya [10]. Xiao dkk [11] melakukan pendekatan analisis kemiripan antara SI dan jaringan syaraf tiruan (JST). Penelitian ini bertujuan untuk mengklasifikasikan SI berdasarkan kesamaan karakteristiknya terhadap CAS. menempatkan algoritma SI tersebut menjadi objek yang akan diklasifikasikan dilihat dari sudut pandang CAS. Teknik uji komparasi digunakan sebagai pendekatan pembuktian. Uji komparasi algoritma akan digunakan untuk mengelompokkan beberapa algoritma dalam SI yang masuk ke dalam CAS dan algoritma mana saja yang tidak memiliki karakteristik CAS.

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“…Figure 1 shows the general classification of the Deterministic algorithm, Nondeterministic (Stochastic) algorithm, and Evolutionary algorithm, which are implemented for mobile robot navigation by various authors. Navigation is an essential task in the field of mobile robotics, which can be classified into two types: global navigation and local navigation [1]. In the global navigation, the prior knowledge of the environment should be available.…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

178

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Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor-actuator control techniques. The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance. The present article focuses on the study of the intelligent navigation techniques, which are capable of navigating a mobile robot autonomously in static as well as dynamic environments. Keywords: IntroductionThis article introduces the literature survey of the various techniques used for mobile robot navigation. Navigation and obstacle avoidance are one of the fundamental problems in mobile robotics, which are being solved by the various researchers in the past two decades. The aim of navigation is to search an optimal or suboptimal path from the start point to the goal point with obstacle avoidance competence. Basically, the mobile robot navigation has been done by the Deterministic algorithm and Nondeterministic (Stochastic) algorithm. Nowadays, the hybridization of both the algorithms called as an Evolutionary algorithm is being used to solve the mobile robot navigation problem. Figure 1 shows the general classification of the Deterministic algorithm, Nondeterministic (Stochastic) algorithm, and Evolutionary algorithm, which are implemented for mobile robot navigation by various authors.

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Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey

Cited by 80 publications

References 62 publications

Augmented-reality integrated robotics in neurosurgery: are we there yet?

Augmented-reality integrated robotics in neurosurgery: are we there yet?

Klasifikasi Algoritma Swarm Intelligence Dalam Perspektif Complex Adaptive System dengan Metode Uji Komparasi Statistik

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

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