相关实验视频
Updated: Jan 28, 2026

10:19
Studying the Neural Basis of Adaptive Locomotor Behavior in Insects
Published on: April 13, 2011
13.3K
缓解昆虫生物机器人的神经习惯:一种双时间尺度的自适应控制方法
Le Minh Triet1, Nguyen Truong Thinh1
1Institute of Intelligent and Interactive Technologies, University of Economics Ho Chi Minh City-UEH, Ho Chi Minh City 70000, Vietnam.
Biomimetics (Basel, Switzerland)
|January 27, 2026
概括
这项研究介绍了一种适应性神经模糊推理系统 (ANFIS),用于生物网络生物体. 安菲斯 (ANFIS) 增强了导航和控制,在搜索和救援等任务中优于非适应性方法.
科学领域:
- 生物网络学就是生物网络学.
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 生物网络生物体将生物运动与电子控制相结合.
- 挑战包括个体变化和习惯刺激.
- 现有的系统缺乏对个体标本的动态适应.
研究的目的:
- 为生物网络应用引入适应性神经模糊推理系统 (ANFIS).
- 为了动态校准控制系统到单个节肢动物标本.
- 在生物混合平台中建立适应性控制的基准.
主要方法:
- 开发和实施使用小型神经控制器的ANFIS.
- 对比ANFIS的表现与自然行为和非适应性对照在Gromphadorhina portentosa*.
- 在14天内评估了障碍物导航,目标获取,刺激反应和生物相容性.
主要成果:
- ANFIS实现了81%的障碍物导航效率,明显高于非适应性方法 (42%).
- 有效的行为调节持续了47分钟,而非适应性对照组持续了26分钟.
- 刺激反应时间长达3.5倍,在复杂的地形中达到73%的目标.
结论:
- 适应性神经模糊架构显著优于生物网络学中的传统方法.
- 安菲斯系统表现出强大的控制和持续的响应能力.
- 这项研究使先进的生物混合平台能够用于狭窄空间的搜救行动.
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