• 한국군사과학기술학회지
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• 제12권5호
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• pp.563-570
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• 2009

In the future, most military activities will be replaced by robots. Because of many dangerous factors in battlefield, reconnaissance should be performed by robot. Reconnaissance robot should be small for not being detected, be light and simple structure for personal portability and overcome unexpected rough terrain for mission completion. In case of small and light robot, it can't get enough friction force for movement. Therefore small reconnaissance robot need jumping function for movement. In this paper we proposed a biologically inspired jumping mechanism. And we adjusted moment and jumping angle by using four bar linkage, especially varying coupler length.

• International Journal of Aeronautical and Space Sciences
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• 제13권1호
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• pp.1-13
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• 2012

Over 3.8 billion years, through evolution nature came up with many effective continually improving solutions to its challenges. Humans have always been inspired by nature capabilities in problems solving and innovation. These efforts have been intensified in recent years where systematic studies are being made towards better understanding and applying more sophisticated capabilities in this field that is increasingly being titled biomimetics. The ultimate challenge to this field is the development of humanlike robots that talk, interpret speech, walk, as well as make eye-contact and facial expressions with some capabilities that are exceeding the original model from nature. This includes flight where there is no creature that is as large, can fly as high, carry so heavy weight, fly so fast, and able to operate in extreme conditions as the aircraft and other aerospace systems. However, there are many capabilities of biological systems that are not feasible to mimic using the available technology. In this paper, the state-of-the-art of some of the developed biomimetic capabilities, potentials and challenges will be reviewed.

• Biomaterials and Biomechanics in Bioengineering
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• 제2권4호
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• pp.249-269
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• 2015

A problem faced by oil companies is the maintenance of the location register of pipelines that cross the surf zone, the regular survey of their location, and also their inspection. A survey of the state of art did not allow identifying operating systems capable of executing such tasks. Commercial technologies available on the market also do not address this problem and/or do not satisfy the presented requirements. A possible solution is to use robotic systems which have the ability to walk on the shore and in the surf zone, subject to existing currents and ripples, and being able to withstand these ambient conditions. In this sense, the authors propose the development of a spider crab biologically inspired robot to achieve those tasks. Based on these ideas, this work presents a biomechanical study of the spider crab, its modeling and simulation using the SimMechanics toolbox of Matlab/Simulink, which is the first phase of this more vast project. Results show a robot model that is moving in an "animal like" manner, the locomotion, the algorithm presented in this paper allows the crab to walk sideways, in the desired direction.

• 전자통신동향분석
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• 제36권3호
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• pp.106-118
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• 2021

The field of brain science (or neuroscience in a broader sense) has inspired researchers in artificial intelligence (AI) for a long time. The outcomes of neuroscience such as Hebb's rule had profound effects on the early AI models, and the models have developed to become the current state-of-the-art artificial neural networks. However, the recent progress in AI led by deep learning architectures is mainly due to elaborate mathematical methods and the rapid growth of computing power rather than neuroscientific inspiration. Meanwhile, major limitations such as opacity, lack of common sense, narrowness, and brittleness have not been thoroughly resolved. To address those problems, many AI researchers turn their attention to neuroscience to get insights and inspirations again. Biologically plausible neural networks, spiking neural networks, and connectome-based networks exemplify such neuroscience-inspired approaches. In addition, the more recent field of brain network analysis is unveiling complex brain mechanisms by handling the brain as dynamic graph models. We argue that the progress toward the human-level AI, which is the goal of AI, can be accelerated by leveraging the novel findings of the human brain network.