分散的非线性模型基于预测控制的集群导航,在未知的阻碍环境中实时避开障碍物
Nuthasith Gerdpratoom1, Kaoru Yamamoto1
1Graduate School and Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan.
Frontiers in robotics and AI
|June 25, 2025
概括
这项研究提高了使用非线性模型预测控制 (NMPC) 的机器人导航,并避免了局部障碍. 改进的NMPC使机器人机队能够在复杂的,受阻的环境中安全导航.
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
- 人工智能的人工智能
背景情况:
- 分布式非线性模型预测控制 (NMPC) 对机器人舰队导航至关重要.
- 现有的方法需要改进,以便在未知的环境中切实避免障碍.
研究的目的:
- 将现实的本地避障策略整合到机器人车队的分布式NMPC框架中.
- 开发高效的点云处理,以实时检测和响应障碍.
主要方法:
- 在NMPC框架内使用点云数据纳入局部避障约束.
- 开发一个点云处理技术,涉及方向过和下方采样,以提高计算效率.
- 通过Gazebo中的3D模拟和嵌入式平台上的硬件在循环 (HIL) 测试进行验证.
主要成果:
- 机器人代理人通过受阻环境进行安全导航.
- 证实了点云处理技术的计算效率.
- 通过HIL模拟验证了NMPC方案在嵌入式系统上的实际可行性.
结论:
- 拟议的NMPC方案具有集成的局部避障系统,对于分散的机器人系统是有效的.
- 该方法适用于复杂和动态环境中的现实世界部署.
- 高效的点云处理是实现NMPC中实时避障的关键.
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