在软机器人上进行高度动态任务的自适应控制和最佳轨迹生成.
Haley P Sanders1, Curtis C Johnson1, Marc D Killpack1
1Department of Mechanical Engineering, Robotics and Dynamics Lab, Brigham Young University, Provo, Utah, USA.
Soft robotics
|November 1, 2025
概括
这项研究为软机器人引入了一种新的控制器,使它们能够执行动态任务,例如抛. 模型参考自适应控制器 (MRAC) 允许精确的轨迹跟踪,提高机器人的能力.
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
- 软机器人软机器人 软机器人软机器人
背景情况:
- 软机器人,气动驱动,优秀的动态任务,由于固有的储能和低度的关节.
- 由于复杂的动态和形状不确定性,控制高速移动的软机器人具有挑战性.
- 现有的控制方法难以实现软机器人系统中动态任务所需的精确轨迹跟踪.
研究的目的:
- 为能够执行高度动态任务的气动驱动软机器人制定强大的控制策略.
- 为了使三环软机器人臂能够模仿二阶临界水系统,以改善运动控制.
- 为动态任务创建轨迹生成方法,特别通过投球应用程序进行演示.
主要方法:
- 制定一个模型参考自适应控制器 (MRAC) 来控制软机器人臂的动态.
- 基于二级系统动态的轨迹生成技术的实施,用于精确执行任务.
- 通过模拟和物理硬件实验验证控制器和轨迹发生器的验证.
主要成果:
- MRAC控制器成功地使软机器人臂表现得像第二阶段的临界水系统.
- 在模拟和硬件中,在参考和执行的轨迹之间报告了0.0872的最大根平均平方误差.
- 联合控制器和轨迹发生器在硬件上实现了平均投球精度在目标的25-28%以内,距离为1.5-2米.
结论:
- 开发的MRAC为软机器人中的动态任务提供了有效的轨迹跟踪,克服了系统不确定性带来的挑战.
- MRAC和轨迹生成的综合方法显著提高了软机器人的实用能力,用于像射弹发射这样的任务.
- 这项研究表明,在复杂,动态的环境中,推进大型软机器人系统的性能和适用性是一个可行的途径.
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