环吸引器作为生物仿真导航系统的基础
Thomas C Knowles1, Anna G Summerton2, James G H Whiting2
1Bristol Robotics Laboratory, University of the West of England, Bristol BS16 1QY, UK.
Biomimetics (Basel, Switzerland)
|September 27, 2023
概括
这项研究模拟了一种尖端神经网络 (SNN) 环吸引系统,用于人工智能导航. 该系统使用速度和内存回忆精确估计2D运动,显示出移动机器人技术的前景.
科学领域:
- 神经科学是一个神经科学.
- 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:
- 有效的导航对于动物的生存至关重要,特殊的神经结构,如环吸引器起着关键作用.
- 尖端神经网络 (SNN) 为神经过程提供生物启发的计算模型.
研究的目的:
- 使用尖端神经网络 (SNN) 模拟导航系统,用于人工代理的环吸引器.
- 为了研究意识形态速度和意识形态记忆的整合,以准确的2D运动估计.
主要方法:
- 模拟了三组SNN环吸引器,以模拟平面转换估计.
- 用于动态校准,使用了一个没有里程碑的全神论多感官体验的回忆回忆系统.
主要成果:
- 基于SNN的环吸引器系统准确地建模了2D运动.
- 意识形态速度的整合和回忆中的意识形态体验作为一个纠正机制.
结论:
- 开发的SNN导航系统有效地保持人工代理的平面转换的内部估计.
- 这种生物灵感系统展示了低功耗移动机器人的潜力,其传感输入不可靠.
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