改进的雅科比矩阵估计应用到蛇机器人.
Jostein Løwer1, Damiano Varagnolo1, Øyvind Stavdahl1
1Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway.
Frontiers in robotics and AI
|June 12, 2023
概括
本研究介绍了蛇机器人的两个雅科比矩阵估计器,使障碍物辅助运动 (OAL) 成为可能. 没有香味的卡尔曼波器方法比凸优化更有效和强大,用于机器人控制.
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:
- 蛇机器人在狭窄的空间中提供独特的运动能力.
- 基于雅科比的控制对于精确的操纵器运动至关重要.
- 障碍物辅助机动 (OAL) 利用环境相互作用进行推进.
研究的目的:
- 为受约束的平面蛇机器人开发和评估雅科比矩阵估计器.
- 为了实现基于雅科比的障碍物辅助运动 (OAL) 控制方案.
- 为了比较凸优化和无气味卡尔曼波器方法的性能.
主要方法:
- 开发了两个新的操纵器雅可比安矩阵估计器.
- 从软机器人研究中采用凸优化方法.
- 使用一种没有香味的卡尔曼过器方法.
- 通过模拟评估估计器,评估统计性能,执行时间和噪声强度.
主要成果:
- 这两种估计器都提供了有用的雅可比矩阵估计,用于预测终端效应器运动.
- 没有香味的卡尔曼波器方法表明计算资源要求显著降低.
- 没有香味的卡尔曼波器方法避免了与凸优化方法相关的收问题.
- 这两种方法都显示出对测量噪声的稳定性.
结论:
- 开发的雅可比安估计器对于受约束的平面蛇机器人是有效的.
- 没有香味的卡尔曼波器估计器是OAL.com的计算效率高和强大.
- 这些估计器在软机器人,视觉伺服和一般非平面蛇机器人中具有潜在的应用.
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