感知-行动循环的反应-扩散耦合场模型及其在机器人导航中的应用

E. Aidman, V. Ivancevic, A. Jennings
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引用次数: 8

摘要

提出了一种用于机器人导航的广义反应扩散场模型。它利用两个相互拮抗的神经场,以类似于控制人体主要关节运动的屈/伸肌的模式进行对抗。结合Amari神经场方程所代表的局部激活和广义抑制,并由Fitzhugh-Nagumo和Wilson-Cowan激活剂-抑制剂系统扩展,可以得到可能导致自发振荡模式形成的神经吸引子动力学类型。初步的模拟数据表明,这种方法可以让一组自动驾驶汽车在拥挤的人行横道上行驶。
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A coupled reaction-diffusion field model for perception-action cycle with applications to robot navigation
A generalised reaction-diffusion field model for robot navigation is proposed. It utilises two mutually antagonistic neural fields counteracting in patterns similar to that of flexor/extensor muscles controlling the movements in major joints in the human body. Combining local activation and generalised inhibition represented by Amari's neural field equations and extended by the Fitzhugh-Nagumo and Wilson-Cowan activator-inhibitor systems, results in the type of neural attractor dynamics that may lead to spontaneous oscillatory pattern formation. Preliminary simulation data suggest that this approach has utility in enabling a team of autonomous vehicles to navigate in a crowded pedestrian crossing.
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