Disaster Response Robotxe2x80x99s Autonomous Manipulation of Valves in Disaster Sites Based on Visual Analyses of RGBD Images For building a disaster response robot, WAREClxe2x80x99s fullyautomated system for manipulating a valve, this paper proposes a method for (1) detecting a valve which is far away from the robot, (2) estimating the position and orientation for grasping the valve by the robot at a closer position. Our methods do not need any prior information about a valve for the abovementioned detection and estimation for grasping. In addition, our estimation for grasping provides useful information, by which WARECI can rotate a valve autonomously. The method (1) uses the RGB image and the point cloud data captured by Multisense SL as the input, and estimate the position and orientation of a valve far away from the robot. The method (2) uses both the RGB and depth images captured by KinectV2 as input and estimate information for grasping the valve. Our experiments are conducted using a real disaster response robot. Our experimental results show the error of the estimation by the (a) two methods are small enough to achieve a fullyautomated system for detecting and rotating the valve by WARECI.

GlassLoc: Plenoptic Grasp Pose Detection in Transparent Clutter Transparent objects are prevalent across many environments of interest for dexterous robotic manipulation. Such transparent material leads to considerable uncertainty for robot perception and manipulation, and remains an open challenge for robotics. This problem is exacerbated when multiple transparent objects cluster into piles of clutter. In household environments, for example, it is common to encounter piles of glassware in kitchens, dining rooms, and reception areas, which are essentially invisible to modern robots. We present the GlassLoc algorithm for grasp pose detection of transparent objects in transparent clutter using plenoptic sensing. GlassLoc classifies graspable locations in space informed by a Depth Likelihood Volume (DLV) descriptor. We extend the DLV to infer the occupancy of transparent objects over a given space from multiple plenoptic viewpoints. We demonstrate and evaluate the GlassLoc algorithm on a Michigan Progress Fetch mounted with a first generation Lytro. The effectiveness of our algorithm is evaluated through experiments for grasp detection and execution with a variety of transparent glassware in minor clutter.