在非结构化环境中基于memristor的自适应神经形态感知
Shengbo Wang1, Shuo Gao2, Chenyu Tang3
1School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China.
Nature communications
|May 31, 2024
概括
本研究介绍了一种基于memristor的新型神经形态计算方法,用于机器人和自动驾驶汽车. 该系统可以实时适应非结构化环境,增强对抓取和驾驶等任务的感知和控制.
科学领域:
- 神经形态工程的神经形态工程
- 机器人技术 机器人技术 机器人技术
- 自主系统 自主系统
背景情况:
- 机器人和自动驾驶需要对非结构化的环境有先进的感知能力.
- 类似人类的适应性和准确性对于现实世界导航至关重要.
- 当前的系统经常与现实世界的场景的复杂性和变化性作斗争.
研究的目的:
- 开发基于memristor的神经形态计算方法,以增强感知和在线适应.
- 为了证明该方法在现实世界机器人和自动驾驶应用中的能力.
- 模仿人类低级感知机制,以提高系统性能.
主要方法:
- 利用基于memristor的微分神经形态计算来进行感知信号处理.
- 实施一种对外部感官刺激有反应的在线适应机制.
- 在对象抓取 (机器人手) 和自动驾驶模拟中测试系统.
主要成果:
- 一个单一的memristor能够快速 (~1ms) 适应,使机器人手握得安全稳定.
- 一个40x25的memristor阵列在从10个非结构化的驾驶环境中提取决策信息时达到94%的准确性.
- 该系统展示了实时适应和高级反应,模仿人类的感知.
结论:
- 基于memristor的神经形态计算为非结构化环境中的实时适应提供了可行的解决方案.
- 拟议的方法显著提高了机器人和自动驾驶领域的感知和控制能力.
- 这种方法为更普遍,更适应的智能系统铺平了道路.
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