Automatic controller generation based on dependency network of multi-modal sensor variables for musculoskeletal robotic arm
Automatic controller generation based on dependency network of multi-modal sensor variables for musculoskeletal robotic arm
复制标题
基于多模态传感器变量依赖网络的肌肉骨骼机械臂自动控制器生成
DOI：
10.1016/j.robot.2019.04.010
复制
发表时间：
2019
影响因子：
4.3
通讯作者：
Takagi Kentaro
中科院分区：
文献类型：
--
作者：
Kobayashi Yuichi;Harada Kentaro;Takagi Kentaro
在与人类相同的环境中工作的自主机器人更受欢迎，以确保与周围环境的软接触方面的机械安全，以及处理各种工具和管理部分故障的适应性。为了确保满足这些对机器人的要求，本研究提出了一种获得机器人结构的方法，并将其应用于机器人动态运动的构建控制器。假设传感器变量之间的物理关系是未知的。在利用互信息构建依赖网络的基础上，通过寻找合适的传感器变量因果链来生成控制器并进行测试。以肌肉骨骼机械臂的控制任务为例，对提出的控制器生成方法进行了测试。因此，所提出的控制器生成算法找到了合适的控制器，并且该生成的框架对身体的变化是鲁棒的。
Autonomous robots that work in the same environment as humans are preferred to ensure mechanical safety with respect to soft contact with their surroundings and adaptivity to handle various tools and to manage partial malfunctions. To ensure that these requirements for robots are satisfied, this study proposes an approach for obtaining a robot structure and its application to building controller for dynamic motion of a robot. It is assumed that the physical relations between the sensor variables are unknown. On the basis of dependency network construction using mutual information, controllers are generated and tested by finding appropriate causal chains of the sensor variables. The proposed controller generation methods were tested using the control tasks of a musculoskeletal robotic arm. Thus, the proposed controller generation algorithm finds appropriate controllers, and the framework of this generation is robust to the changes in the body of the body.