人工智能和计算机技术在智能机器人领域的应用和发展
1Beijing Normal University - Hong Kong Baptist University United International College, Zhuhai, 519000, Guangdong, China. zhangjw21265@163.com.
Scientific reports
|April 26, 2025
概括
这项研究引入了智能机器人导航和路径规划的新方法,通过将模糊神经网络 (FNN) 与遗传算法 (GA) 集成. 这种方法提高了机器人导航的准确性,并优化了路径规划效率,以更快地完成任务.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算智能是一种计算智能.
背景情况:
- 智能机器人在复杂环境中的导航和路径规划方面面临重大挑战.
- 现有的方法可能缺乏必要的准确性或效率,以适用于现实世界的应用.
研究的目的:
- 为智能机器人导航和路径规划开发和评估一种混合方法,将模糊神经网络 (FNN) 和遗传算法 (GA) 结合起来.
- 为了提高导航准确度和优化路径规划效率.
主要方法:
- 用一个模糊神经网络 (FNN) 算法来提高机器人在复杂环境中的导航精度.
- 使用基因算法 (GA) 来优化路径规划效率,以最短的导航时间为目标.
- 拟议的FNN-GA模型与BP神经网络 (BPNN),自组织地图 (SOM) 和自适应共振理论神经网络 (ART) 综合导航方法进行了比较.
主要成果:
- 基于FNN的导航实现了98.64%的高精度.
- 记录的最短导航时间为9.64秒,最小误差角度偏差为1.52%.
- FNN和GA组合模型在路线规划方面表现出卓越的效率,在最短的时间内完成导航.
结论:
- FNN和GA的整合显著提高了智能机器人导航和路径规划能力.
- 与其他神经网络方法相比,拟议的方法提供了更好的准确性和效率.
- 这一进步有助于进一步发展智能机器人系统.
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