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Toward the creation of a new relationship among people and robots, a partner robot, CanguRo, capable of self-traveling, human-following as well as transforming into a personal vehicle, is designed and developed. In this paper, concepts and features pursued on this model are overviewed. As a key component, then, its in-wheel drive unit with cycloid gear is focused, and design methodology and performance evaluation of the drive unit are fully discussed.

The goal of this study is to achieve automatic extrinsic calibration of a camera-LiDAR system that does not require calibration targets. Calibration through maximization of statistical dependence using mutual information (MI) is a promising approach. However, we observed that existing methods perform poorly on outdoor data sets. Because of their susceptibility to noise, objective functions of previous methods tend to be non-smooth, and gradient-based searches fail in local optima. To overcome these issues, we introduce a novel dependence estimator called bagged least-squares mutual information (BLSMI). BLSMI is a combination of methods composed of a kernel-based dependence estimator and noise reduction by bootstrap aggregating (bagging), which can handle richer features and robustly estimate dependence. We compared ours with previous methods using indoor and outdoor data sets, and observed that our method performed best in terms of calibration accuracy. While previous methods showed degraded performance on outdoor data sets because of the local optima problem, our method exhibited high calibration accuracy both on indoor and outdoor data sets.

Aesculus turbinata acorns were dispersed earlier than other acorn-producing species and are removed completely by rodents, although the acorns are unpalatable. The dispersal timing and low palatability to rodents on A. turbinata acorns promote the seed caching by rodents, facilitating the seedling emergence is possible even in lower-seeding years.

We proposed a new procedure for evaluating the parameters of the asperity models for predicting strong ground motions from inland earthquakes caused by long strike-slip faults. The evaluation formula of averaged dynamic stress drops obtained by Irie et al. (2010)1) for surface ruptured faults 15 to 100 km long was adopted in our procedure, because the evaluation formula of the averaged static stress drops for circular cracks, commonly adopted in existing procedures, could not be applied to surface ruptured faults or long faults. The dynamic stress drop averaged on the entire fault was estimated to be 34 bars from the data of the rupture areas and the seismic moments of actual earthquakes, and the dynamic stress drop on the asperity was estimated to be 122 bars from the data of the short-period levels and the seismic moments of actual earthquakes. These results led to the conclusions that the asperity areas were 23 % of the entire ruptured areas.

We proposed a procedure to establish a model of dynamic fault rupturing for predicting strong ground motions under the constraints by empirical relations among fault parameters of the surface and subsurface faults such as the fault length, the dislocation on the surface, the ruptured area, the seismic moment, and the short-period level of the acceleration source spectrum. As an example, given the depth of the seismogenic layer 4 to 18 km deep and the earthquake magnitude of 7.2, we evaluated 14 model parameters of the dynamic fault rupturing. We confirmed that the results of the dynamic fault rupturing simulation based on this model satisfied the empirical relations adopted prior to the simulation.

Rodent densities were estimated on Mount Usu, northern Japan, 17 years after the 1977–1978 volcanic eruptions. The summit area was completely deforested by the eruptions, and was covered with thick tephra. When the survey was performed, two plant communities developed. One was a forest dominated by Populus maximowiczii, and another was a grassland with a canopy of the large perennial, Polygonum sachalinense. In each plant community, we estimated the abundance of rodents by Sherman traps. The abundance was 5 Clethrionomys rutocanus bedfordiae/100 traps/night in the grassland, and 0 in the forest. This implied that a generalist, C. rutocanus bedfordiae became established in the grassland, probably because of plant community structure, including litter deposits, rather than food resources. The results compared with those from Mount St. Helens suggested that rodent immigration was greatly determined by the scales of eruptions and recovery patterns of plant communities.

In this paper, we present a novel approach to 2D street map-based localization for mobile robots that navigate mainly in urban sidewalk environments. Recently, localization based on the map built by Simultaneous Localization and Mapping (SLAM) has been widely used with great success. However, such methods limit robot navigation to environments whose maps are prebuilt. In other words, robots cannot navigate in environments that they have not previously visited. We aim to relax the restriction by employing existing 2D street maps for localization. Finding an exact match between sensor data and a street map is challenging because, unlike maps built by robots, street maps lack detailed information about the environment (such as height and color). Our approach to coping with this difficulty is to maximize statistical dependence between sensor data and the map, and localization is achieved through maximization of a Mutual Information-based criterion. Our method employs a computationally efficient estimator of Squared-loss Mutual Information through which we achieved near real-time performance. The effectiveness of our method is evaluated through localization experiments using real-world data sets.

In this paper, we describe the system design of the robots developed by our Team CIT Brains for the RoboCup soccer humanoid KidSize league. We have been participating in the Humanoid League for eight years. Two years ago, we redesigned the system to put a large weight on maintainability and usability. In RoboCup 2014, we won the first prizes of 4on4 soccer and technical challenge. Consequently, we were awarded the Louis Vuitton Humanoid Cup. The system we developed has high mobility, well-designed control system, position estimation by a monocular camera, user-friendly interface and a simulator. The robot can walk speedily and robustly. It also has a feedback system with a gyro sensor to prevent falls. It detects positions of landmarks by color-based image processing. A particle filter is employed to localize the robot in the soccer field fusing the motion model and landmark observation.

In this paper, we describe technologies of the robot Accelite that is the winner of the RoboCup2015 Humanoid KidSize League. In 2015, the rules of the humanoid league were changed dramatically. The ball was changed from a monochromatic orange ball to a real soccer ball. Furthermore, the goal color was also changed from yellow to white. Therefore, the soccer robots in the humanoid league were not able to detect the ball based on just colors. We developed some detection methods and verified their effectiveness. Consequently, our robots detected the ball robustly and we succeeded in the technical challenge "Goal-Kick from Moving Ball". In RoboCup 2015, the field of play was also changed from a punch carpet to an artificial grass. Because the pile length of the artificial grass in RoboCup2015 was long, our robots were not able to walk robustly when we started to use the field. We also explain how we solved the problem. We also discuss how we succeeded in the technical challenges.

We propose a new technique for a joint inversion of apparent resistivity data from a vertical electrical sounding and peak-period of spectral ratio of horizontal microtremors to vertical one in order to infer resistivity and S-wave profiles simultaneously. Least-squares errors between the observed apparent resistivity and theoretical ones are minimized together with errors between the peak-periods of the horizontal-to-vertical spectrum of microtremors and theoretical ellipticity for fundamental-mode Rayleigh wave. We use a Simulated Annealing to minimize the misfit function defined by the summation of the two least-squared errors. First, we confirmed the performance of the method using synthetic data for a shallow soil model. The method reconstructs well the soil model, even though synthetic observed apparent resistivity data are incomplete. We, then, apply the method to actual observed data at a site in reclaimed area in Yokohama, Japan. The obtained profiles are in good agreements with results of geological and PS loggings. It is concluded that the proposed technique can be addressed as one of the easiest methods to infer shallow S-wave and resistivity profiles simultaneously.