動的ロボット環境における運動パターン認識のための合成神経視覚システム設計
Synthetic Neural Vision System Design for Motion Pattern Recognition in Dynamic Robot Scenes
昆虫の視覚ニューロン(LGMDとDSN)を模したスパイキングニューラルネットワークで、接近・後退・並進などの運動パターンを認識し衝突回避や追従を実現する組込み視覚システムを提案した。
著者: Qinbing Fu, Cheng Hu, Pengcheng Liu, Shigang Yue
分類: cs.NE, cs.AI, cs.RO
原文アブストラクト
Insects have tiny brains but complicated visual systems for motion perception. A handful of insect visual neurons have been computationally modeled and successfully applied for robotics. How different neurons collaborate on motion perception, is an open question to date. In this paper, we propose a novel embedded vision system in autonomous micro-robots, to recognize motion patterns in dynamic robot scenes. Here, the basic motion patterns are categorized into movements of looming (proximity), recession, translation, and other irrelevant ones. The presented system is a synthetic neural network, which comprises two complementary sub-systems with four spiking neurons -- the lobula giant movement detectors (LGMD1 and LGMD2) in locusts for sensing looming and recession, and the direction selective neurons (DSN-R and DSN-L) in flies for translational motion extraction. Images are transformed to spikes via spatiotemporal computations towards a switch function and decision making mechanisms, in order to invoke proper robot behaviors amongst collision avoidance, tracking and wandering, in dynamic robot scenes. Our robot experiments demonstrated two main contributions: (1) This neural vision system is effective to recognize the basic motion patterns corresponding to timely and proper robot behaviors in dynamic scenes. (2) The arena tests with multi-robots demonstrated the effectiveness in recognizing more abundant motion features for collision detection, which is a great improvement compared with former studies.
