Abstract The goal of this research is to develop a “Selective Grasp” system with which robots can grasp and identify the target object even in occluded environments. In pursuit of this goal, we first develop a robot hand on which proximity sensors are mounted all around. In addition to the development, we propose a sensor model of the robot hand. By using the sensor model, robots can estimate the distance to the object and calibrate the sensors. With our robot hand, robots can accurately recognize their surroundings without touch. Secondly, we propose an approach in which robots can memorize spatial information of surroundings by building an environment map. The building map motion is generated by a combination of manipulation primitives based on proximity sensors. Thirdly, we propose a grasp planning method and an object shape classification method based on the environment map. By these methods, robots can grasp objects and classify shapes of the objects in occluded spaces. Lastly, we conduct real robot experiments, through which we validate the effectiveness of our proposed Selective Grasp system.

中文翻译：

---

通过全方位接近可感知手指在封闭空间中选择性抓取

摘要 本研究的目标是开发一种“选择性抓取”系统，机器人即使在封闭环境中也能抓取和识别目标物体。为了实现这一目标，我们首先开发了一种机械手，其上安装了接近传感器。除了开发之外，我们还提出了机器人手的传感器模型。通过使用传感器模型，机器人可以估计到物体的距离并校准传感器。借助我们的机器人手，机器人无需触摸即可准确识别周围环境。其次，我们提出了一种方法，其中机器人可以通过构建环境地图来记忆周围环境的空间信息。建筑地图运动是由基于接近传感器的操作基元组合生成的。第三，我们提出了一种基于环境图的抓取规划方法和物体形状分类方法。通过这些方法，机器人可以在封闭空间中抓取物体并对物体的形状进行分类。最后，我们进行了真实的机器人实验，通过实验验证了我们提出的 Selective Grasp 系统的有效性。