使用 RBF 标识符和扩展卡尔曼波器的 I2RIS 机器人的模型预测路径集成控制
Mojtaba Esfandiari1, Pengyuan Du1, Haochen Wei2
1Mojtaba Esfandiari, Pengyuan Du, and Iulian Iordachita are with the Department of Mechanical Engineering and Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, 21218, USA.
概括
这项研究使用数据驱动模型和自适应控制来提出眼科机器人蛇的强有力的控制策略. 模型预测路径集成 (MPPI) 控制器在未知的外科环境中提高了性能.
科学领域:
- 机器人技术
- 医疗机器人
- 控制系统
背景情况:
- 控制电缆驱动的蛇机器人,特别是用于眼科手术 (例如,I2RIS),由于歇斯底里和摩擦等非线性因素而复杂.
- 像I2RIS这样的小型机器人缺乏感官反,
研究的目的:
- 开发和评估眼科机器人蛇的数据驱动模型的自适应控制策略.
- 在面对模型不确定性和未知的环境时,提高机器人蛇控制的稳定性和性能.
主要方法:
- 将模型预测路径积分 (MPPI) 控制器应用于基于 I2RIS 的高斯混合模型 (GMM) 和高斯混合回归 (GMR) 的数据驱动模型.
- 模拟未知的外部干扰和环境负荷,以测试未见的场景中的性能.
- 使用辐射基函数 (RBF) 网络实现在线不确定性识别,重量由扩展卡尔曼波器 (EKF) 更新.
主要成果:
- 即使在模拟不确定性的情况下,MPPI控制器也展示了强大的最佳控制解决方案.
- 适应机制有效地识别和补偿在线模型的不确定性.
- 与传统的模型预测控制 (MPC) 相比,MPPI显示了计算优势.
结论:
- 拟议的MPPI自适应控制器提高了眼科机器人蛇的数据驱动模型的可靠性.
- 这种方法在复杂的外科环境中为控制机器人蛇提供了计算效率高且强大的解决方案.
- 整合GMM-GMR,RBF和EKF为自适应机器人控制提供了一个强大的框架.
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