Spring-IMU Fusion-Based Proprioception for Feedback Control of Soft Manipulators
Spring-IMU Fusion-Based Proprioception for Feedback Control of Soft Manipulators
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DOI：
10.1109/tmech.2023.3320980
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Yinan Meng;Guoxin Fang;Jiong Yang;Yuhu Guo;Charlie C. L. Wang
中科院分区：
文献类型：
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作者：
Yinan Meng;Guoxin Fang;Jiong Yang;Yuhu Guo;Charlie C. L. Wang
本文提出了一种实现软机械臂本体感知和闭环控制的新框架。在机器学习技术的帮助下，利用从感应弹簧和惯性测量单元(IMU)获得的基于几何的传感器信号，可以精确地预测大伸长和大弯曲的变形。将多个几何信号融合到稳健的位姿估计中，并应用拟实转换策略实现了数据高效的训练过程。结果表明，气动软机械手的本体感觉对外部载荷的变化具有较强的鲁棒性，整个工作空间的平均误差为0.7%。实现的软机械臂本体感知有助于建立基于传感器空间的闭环系统控制算法。开发了一种梯度下降求解器，通过迭代计算参考传感器信号序列来驱动末端执行器实现所需的位姿。在算法的内环中使用常规控制器来更新执行器(即，腔室中的压力)以逼近传感器空间中的参考信号。闭环控制的系统功能已经在不同外载荷下的路径跟踪和拾取放置等任务中得到了演示。
This article presents a novel framework to realize proprioception and closed-loop control for soft manipulators. Deformations with large elongation and large bending can be precisely predicted using geometry-based sensor signals obtained from the inductive springs and the inertial measurement units (IMUs) with the help of machine learning techniques. Multiple geometric signals are fused into robust pose estimations, and a data-efficient training process is achieved after applying the strategy of sim-to-real transfer. As a result, we can achieve proprioception that is robust to the variation of external loading and has an average error of 0.7% across the workspace on a pneumatic-driven soft manipulator. The realized proprioception on soft manipulator is then contributed to building a sensor-space-based algorithm for closed-loop control. A gradient-descent solver is developed to drive the end-effector to achieve the required poses by iteratively computing a sequence of reference sensor signals. A conventional controller is employed in the inner loop of our algorithm to update actuators (i.e., the pressures in chambers) for approaching a reference signal in the sensor-space. The systematic function of closed-loop control has been demonstrated in tasks like path following and pick-and-place under different external loads.