Small Brains, Smart Machines: From Fly Vision to Robot Vision and Back Again

Franceschini, Nicolas

doi:10.1109/jproc.2014.2312916

Cited by 65 publications

(54 citation statements)

References 241 publications

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“…In addition, a comprehensive survey about insect-like robots that navigate and control their motion using biologically inspired visual strategies can be referred in Franceschini (2014).…”

Section: Applications Of Curved Compound Eyesmentioning

confidence: 99%

Artificial compound eye: a survey of the state-of-the-art

Jiang

Zhang

et al. 2016

Artif Intell Rev

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An artificial compound eye system is the bionic system of natural compound eyes with much wider field-of-view, better capacity to detect moving objects and higher sensitivity to light intensity than ordinary single-aperture eyes. In recent years, renewed attention has been paid to the artificial compound eyes, due to their better characteristics inheriting from insect compound eyes than ordinary optical imaging systems. This paper provides a comprehensive survey of the state-of-the-art work on artificial compound eyes. This review starts from natural compound eyes to artificial compound eyes including their system design, theoretical development and applications. The survey of artificial compound eyes is developed in terms of two main types: planar and curved artificial compound eyes. Finally, the most promising future research developments are highlighted.

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“…In addition, a comprehensive survey about insect-like robots that navigate and control their motion using biologically inspired visual strategies can be referred in Franceschini (2014).…”

Section: Applications Of Curved Compound Eyesmentioning

confidence: 99%

Artificial compound eye: a survey of the state-of-the-art

Jiang

Zhang

et al. 2016

Artif Intell Rev

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“…It was decided to use the ruspinni partition in this way to obtain a value of one generating a better control of the movement. Also, the defuzzification method used corresponds to the center of gravity method [2], because it allows to better balance the output value with respect to the activation of the corresponding rules. In Eq.…”

Section: Fuzzy Position Controllermentioning

confidence: 99%

“…The control fields of robotic systems through machine vision, include applications of aerial auto navigation, for example those based on nano-cybernetic systems as the one in [2]. Similarly, Kinect-based machine vision systems, the Microsoft-developed sensor that recognizes gestures, voice commands, images and objects, have resulted in developments such as those presented in [3] and [4], both based on recognition of the human face.…”

Section: Introductionmentioning

confidence: 99%

Humanoid Robot Cooperative System by Machine Vision

2017

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Abstract-This article presents a supervised control position system, based on image processing and oriented to the cooperative work between two humanoid robots that work autonomously. The first robot picks up an object, carry it to the second robot and after that the same second robot places it in an endpoint, this is achieved through doing movements in straight line trajectories and turns of 180 degrees. Using for this the Microsoft Kinect , finding for each robot and the reference object its exact spatial position, through the color space conversion and filtering, derived from the information of the RGB camera that counts and obtains this result using the information transmitted from the depth sensor, obtaining the final location of each. Through programming in C #, and the developed algorithms that allow to command each robot in order to work together for transport the reference object, from an initial point, delivering this object from one robot to the other and depositing it in an endpoint. This experiment was tested performed the same trajectory, under uniform light conditions, achieving each time the successful delivering of the object.

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“…Following the pioneering work of Nicolas Franceschini, who was one of the first to translate the principles of insect vision to the development of ground-based robots that would navigate autonomously and avoid collisions with obstacles [21], a number of laboratories have progressed research in this area (e.g., [22][23][24][25]). These principles have been used to design and implement numerous bio-inspired algorithms for terrestrial and aerial vehicles, including safely navigating through narrow corridors (e.g., [26][27][28][29][30]), avoiding obstacles (e.g., [31]), maintaining a prescribed flight altitude (e.g., [23,32,33]), monitoring and stabilizing flight attitude (e.g., [34][35][36][37]), and docking (e.g., [38][39][40][41][42]) or executing smooth landings (e.g., [43][44][45][46]).…”

Section: Vision-based Techniques and Biological Inspirationmentioning

confidence: 99%

Bio-Inspired Principles Applied to the Guidance, Navigation and Control of UAS

2016

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Abstract:This review describes a number of biologically inspired principles that have been applied to the visual guidance, navigation and control of Unmanned Aerial System (UAS). The current limitations of UAS systems are outlined, such as the over-reliance on GPS, the requirement for more self-reliant systems and the need for UAS to have a greater understanding of their environment. It is evident that insects, even with their small brains and limited intelligence, have overcome many of the shortcomings of the current state of the art in autonomous aerial guidance. This has motivated research into bio-inspired systems and algorithms, specifically vision-based navigation, situational awareness and guidance.

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Small Brains, Smart Machines: From Fly Vision to Robot Vision and Back Again

Cited by 65 publications

References 241 publications

Artificial compound eye: a survey of the state-of-the-art

Artificial compound eye: a survey of the state-of-the-art

Humanoid Robot Cooperative System by Machine Vision

Bio-Inspired Principles Applied to the Guidance, Navigation and Control of UAS

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