Automated Calibration of Planar Cable-Driven Parallel Manipulators by Reinforcement Learning in Joint-Space
Automated Calibration of Planar Cable-Driven Parallel Manipulators by Reinforcement Learning in Joint-Space
复制标题
通过关节空间强化学习自动校准平面电缆驱动并联机械手
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
J. Mattila
中科院分区：
文献类型：
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作者：
M. M. Aref;J. Mattila
得益于模块化，电缆驱动的并联机器人（cdpr）能够通过改变其附着点来重新配置，因此，它们的运动结构也会发生重大变化。由于它们的大范围运动，测量cdpr固定附着点的位置可能会受到限制。本文采用强化学习的方法，解决了基于内感受传感器的机械臂几何形状识别问题。我们建议使用雅可比矩阵元素将奖励和动作映射到关节空间中，而不出现局部最小值和正运动学的多重解。通过平面冗余CDPR验证了该方法的可行性。在没有昂贵的跟踪系统的情况下，机器人能够根据电缆长度测量（执行器反馈）和任何配置空间的量化因素进行自动校准，同时保持所有电缆处于张力下。以网格分辨率为1 cm的工作空间离散为例，该算法可将初始误差降低2.2 m，低至1 cm。将这种方法进一步扩展到更高的技术就绪水平，可以提高这些操纵器向即插即用装置商业化的可能性。
Benefiting from modularity, cable-driven parallel robots (CDPRs) are capable of being reconfigurable by changes in their attachment points and, therefore, significant changes in their kinematic structures. Due to their wide-range motion, measuring CDPRs’ fixed attachment points location can be limiting. This paper tackles the problem of identifying the manipulators’ geometry based on their interoceptive sensors by reinforcement learning. We propose using Jacobian matrix elements to map rewards and actions into joint space without the appearance of local minimums and multiple solutions of forward kinematics. Feasibility of this method is demonstrated by a planar redundant CDPR.Without an expensive tracking system, the robot is capable of autocalibration based on the cable length measurements (actuator feedback) and quantization factors of any configuration space, while keeping all the cables under tension force. For instance, if the workspace is discretized with a grid resolution of 1 cm, this algorithm is capable of reducing the initial error of 2.2 m, as low as 1 cm. Further extension of this method toward higher technology readiness levels can improve the possibility of commercializing these manipulators toward plug-and-play setups.