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基于实时形状估计的连续机器人的混合离线-在线配置规划方法
Hexiang Yuan1,2, Zhibo Jing1,2,3, Yibo He1,2
1College of Artificial Intelligence, Nankai University, Tianjin 300350, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
这项研究介绍了连续机器人的混合规划框架,提高了它们在复杂空间安全导航的能力. 这种方法可以在狭窄的环境中提高运动的流性和控制.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 机械工程 机械工程
背景情况:
- 连续机器人为狭窄空间提供高灵活性.
- 它们固有的非线性和不确定性给运动规划带来了挑战.
研究的目的:
- 开发一种混合线下线上框架,用于连续机器人运动规划.
- 在复杂的环境中解决避免碰撞和结构约束的问题.
主要方法:
- 制定规划作为与碰撞和结构约束的非线性优化.
- 使用与差异进化 (DE) 的共同进化策略来优化分解.
- 实现了一个无气味的卡尔曼波器 (UKF) 实时形状估计和安全距离监控.
- 设计了一个在线配置精炼器,根据形状反进行本地调整.
主要成果:
- 证明了混合规划框架的有效性.
- 在现实世界的测试中实现了增强的运动流性和安全的运动性能.
- 通过受限空间成功引导连续操纵器.
结论:
- 拟议的混合框架有效地管理连续机器人的非线性.
- 这种方法提高了在充满挑战的环境中安全和顺的航行.
- 该方法为连续机器人运动规划提供了强大的解决方案.
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