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基于RCSM-PL网络的非结构化环境中的轮式机器人的能源效率类似的人类轨迹规划
Hao Xu1, Guanyu Zhang1, Huanyu Zhao1
1College of Instrument Science and Electrical Engineering, Jilin University, Jilin, China.
iScience
|September 2, 2025
概括
这项研究引入了轮式机器人的深度学习轨迹规划方法,大大降低了城市检查任务的能源消耗. 这种新的方法通过学习复杂环境中的类似人类导航行为来提高准确性.
科学领域:
- 机器人技术
- 人工智能
- 深度学习
背景情况:
- 在城市检查和非结构化环境中,轮式机器人面临着重大的能源消耗挑战.
- 有效的轨道规划对于扩大运行范围和减少功耗至关重要.
研究的目的:
- 使用深度学习开发类似人类的轨迹规划方法,以减少轮式机器人的能源消耗.
- 在复杂和危险的环境中提高轨迹预测的准确性.
主要方法:
- 采用卷积神经网络 (CNN) 以多维注意力从驾驶场景和雷达地图中提取空间特征.
- 采用改进的长期短期内存 (LSTM) 网络来捕获时间依赖性,将状态信息纳入门更新模块.
- 集成功率,速度和角速度作为限制,以完善轨迹映射的准确性.
主要成果:
- 与传统和最先进的方法相比,建议的深度学习方法显著降低了累积功耗.
- 在预测未来机器人的轨迹方面取得了更高的准确性.
- 该模型有效地学习了人类的操纵行为,从而提高了节能性能.
结论:
- 这种类似于人类的轨道规划方法为在具有挑战性的城市和非结构化的环境中节能机器人导航提供了有前途的解决方案.
- 深度学习,特别是具有注意力机制的CNN和LSTM,可以有效地优化机器人的能量消耗和导航精度.
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