人与机器人导航中的交叉点估计-统计线性化与西格玛点转换
Rainer Palm1, Achim J Lilienthal2
1Center for Applied Autonomous Sensor Systems (AASS), Department of Technology, Örebro University, SE-701 82 Örebro, Sweden.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
本研究通过计算预期的交互区域来解决人机导航中的安全问题. 它使用统计线性化和西格玛点转换来处理模糊和杂的轨迹数据,以实现更安全的机器人导航.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与机器人的交互
- 导航系统 导航系统
背景情况:
- 移动机器人和人类操作员在共享空间的安全导航至关重要.
- 轨道规划和避开障碍是关键的安全考虑因素.
- 由于噪音的影响,计划轨迹的不确定性会影响安全.
研究的目的:
- 开发用于计算人类和机器人之间预期交互区域的方法.
- 解决人类机器人导航中模糊和杂信息的挑战.
- 分析机器人/人类状态和轨道交叉区域之间的非线性关联.
主要方法:
- 对于噪音输入的非线性转换的统计线性化.
- 用于不确定性传播的西格玛点转换.
- 两种方法的模糊近似值.
- 讨论参数估计的反向问题.
主要成果:
- 考虑轨迹不确定性的预期相互作用区域的量化.
- 从运动输入到交叉区域的非线性映射的演示.
- 模糊近似的演示,用于实际应用.
结论:
- 提出的方法有效地计算了人机导航中预期的交互区域.
- 处理模糊和杂数据对于稳健的轨迹规划至关重要.
- 了解这些交互区域可以提高共享环境中的安全性.
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