We discuss the linkage between dark matter mass in the one-loop radiative seesaw model and the effective neutrino mass for the neutrino less double beta decay. This linkage, which has been already numerically suggested, is confirmed to be a reasonable relationship by deriving analytical expressions for two zero flavor neutrino mass texture.
This paper proposes a brush-cutting robot which can cut high weeds and grass automatically by hand-held brush-cutter sold in hardware store. The robot vehicle is constructed with a center articulated body, a manipulating mechanism of the brush-cutter, motor controller and sensors for self-localization. In order to get its own position continuously, two Laser Range Finders (LRF), a GPS campus and odometry encoder for each wheel are installed. Authors proposed Sensor Steering Mechanism (SSM) for the lateral guided vehicle with steering mechanism. This robot is applied SSM for the center articulated body to follow the way point line on the 2-D map. The map for self-localization is constructed with a 0.1m by 0.1m grid that is measured by the 3-D LRF. The experimental set up was developed and installed in the control program and then autonomous mowing was tested, leading to the results of running and mowing in the quadrangle of the No. 4 building of science and engineering at National Defense Academy. As a result, the proposed system achieved an error of 0.2m or less for the way point line following performance. The developed brush-cutting robot successfully completed the work without untreated area by providing an appropriate overlap by the result.
[abstFig src='/00280004/03.jpg' width='300' text='Smart Dump 9 started at the Tsukuba Challenge 2015 final' ] The Tsukuba Challenge course includes a pedestrian road in which walkers, bicyclists, and mobile robots coexist. As a result, mobile robots encounter potentially dangerous situations when faced with moving bicycles. Navigating the challenge course involves locating target individuals in the search area and paying attention to the safety of bicyclists. Target individuals involve those who typically wear a cap and a refracted vest and are seated on chairs. This study proposes a method to identify pedestrians, bicyclists, and seated individuals by using a 3D LiDAR on Smart Dump 9. The SVM method was employed to identify the target seated individuals. An experiment was conducted on the challenge course to illustrate the advantages of the proposed method.
Our research object is to develop a wheel chair that is able to climb up and down stairs. Conventional wheel chairs use a parallel two-wheel type mobile base that is effective for moving on even surfaces but has limited clearance, posing difficulties in climbing over obstacles. In IROS2014, authors proposed and demonstrated an Octal Wheel unit that has 8 wheels with link-mechanisms and is able to climb up and down stairs. This robot is just a prototype to show the effectiveness of the mechanism. This research has begun to develop an AR chair that is able to carry a passenger, move autonomously, and climb up and down stairs. The first step of the AR chair project is to develop and construct a wheel chair with a center articulated body. The 8-wheel mechanism is applied to the AR chair model after the construction of an autonomous system. A control system with 3D LIDAR was installed on the prototype and it autonomously moved 1.4km in the Real World Robotics Challenge (RWRC) 2014 in Tsukuba on the official pedestrian road. Experimental results demonstrate the effectiveness of this method.
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